Irrelevant Matches Research Articles

Finding environmental discourse in historical newspapers: a topic model workflow for query disambiguation


 
 
 Digitized historical newspapers are a treasure trove of information for our understanding of the past. As one popular application, the frequencies of query matches can be used to understand the prevalence of some discourse in a historical era. This requires the construction good queries: broad enough to capture diverse contexts and narrow enough to exclude irrelevant ones. For historical research in digital humanities, targeted queries that emphasize precision have been advised. In this paper, we develop an alternative approach, by using broad queries to cast a wider net and then using topic models built on the match contexts to filter out irrelevant matches. Specifically, we look for contexts discussing environmental issues throughout the 20th century using a corpus of two Australian newspapers. We report on a comparison of iteratively constructed narrow and broad queries and their precision and recall, and find our approach to discover roughly 7-10x more matches with a comparable level of accuracy. This combined approach can work well for focussed research projects where deliberate query construction and qualitative feedback on the results is feasible.
 
 

Irrelevant matches in round-robin tournaments

We consider tournaments played by a set of players in order to establish a ranking among them. We introduce the notion of irrelevant match, as a match that does not influence the ultimate ranking of the involved parties. After discussing the basic properties of this notion, we seek out tournaments that have no irrelevant matches, focusing on the class of tournaments where each player challenges each other exactly once. We prove that tournaments with a static schedule and at least five players always include irrelevant matches. Conversely, dynamic schedules for an arbitrary number of players can be devised that avoid irrelevant matches, at least for one of the players involved in each match. Finally, we prove by computational means that there exist tournaments where all matches are relevant to both players, at least up to eight players.

How common are explicit research questions in journal articles?

Although explicitly labeled research questions seem to be central to some fields, others do not need them. This may confuse authors, editors, readers, and reviewers of multidisciplinary research. This article assesses the extent to which research questions are explicitly mentioned in 17 out of 22 areas of scholarship from 2000 to 2018 by searching over a million full-text open access journal articles. Research questions were almost never explicitly mentioned (under 2%) by articles in engineering and physical, life, and medical sciences, and were the exception (always under 20%) for the broad fields in which they were least rare: computing, philosophy, theology, and social sciences. Nevertheless, research questions were increasingly mentioned explicitly in all fields investigated, despite a rate of 1.8% overall (1.1% after correcting for irrelevant matches). Other terminology for an article’s purpose may be more widely used instead, including aims, objectives, goals, hypotheses, and purposes, although no terminology occurs in a majority of articles in any broad field tested. Authors, editors, readers, and reviewers should therefore be aware that the use of explicitly labeled research questions or other explicit research purpose terminology is non-standard in most or all broad fields, although it is becoming less rare.

An impossibility theorem for paired comparisons

In several decision-making problems, alternatives should be ranked on the basis of paired comparisons between them. We present an axiomatic approach for the universal ranking problem with arbitrary preference intensities, incomplete and multiple comparisons. In particular, two basic properties -- independence of irrelevant matches and self-consistency -- are considered. It is revealed that there exists no ranking method satisfying both requirements at the same time. The impossibility result holds under various restrictions on the set of ranking problems, however, it does not emerge in the case of round-robin tournaments. An interesting and more general possibility result is obtained by restricting the domain of independence of irrelevant matches through the concept of macrovertex.

Multiple Semantic Matching on Augmented $N$ -Partite Graph for Object Co-Segmentation.

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object co-segmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Second, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complement the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between the two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark data sets, i.e., iCoseg and MSRC data sets achieve desirable performance and demonstrate the effectiveness of our proposed framework.

Data‐Driven Automatic Cropping Using Semantic Composition Search

AbstractWe present a data‐driven method for automatically cropping photographs to be well‐composed and aesthetically pleasing. Our method matches the composition of an amateur's photograph to an expert's using point correspondences. The correspondences are based on a novel high‐level local descriptor we term the ‘Object Context’. Object Context is an extension of Shape Context: it is a descriptor encoding which objects and scene elements surround a given point. By searching a database of expertly composed images, we can find a crop window which makes an amateur's photograph closely match the composition of a database exemplar. We cull irrelevant matches in the database efficiently using a global descriptor which encodes the objects in the scene. For images with similar content in the database, we efficiently search the space of possible crops using generalized Hough voting. When comparing the result of our algorithm to expert crops, our crop windows overlap the expert crops by 83.6%. We also perform a user study which shows that our crops compare favourably to an expert humans' crops.

An automatic method for extracting citations from Google Books

Recent studies have shown that counting citations from books can help scholarly impact assessment and that Google Books (GB) is a useful source of such citation counts, despite its lack of a public citation index. Searching GB for citations produces approximate matches, however, and so its raw results need time‐consuming human filtering. In response, this article introduces a method to automatically remove false and irrelevant matches from GB citation searches in addition to introducing refinements to a previous GB manual citation extraction method. The method was evaluated by manual checking of sampled GB results and comparing citations to about 14,500 monographs in the Thomson Reuters Book Citation Index (BKCI) against automatically extracted citations from GB across 24 subject areas. GB citations were 103% to 137% as numerous as BKCI citations in the humanities, except for tourism (72%) and linguistics (91%), 46% to 85% in social sciences, but only 8% to 53% in the sciences. In all cases, however, GB had substantially more citing books than did BKCI, with BKCI's results coming predominantly from journal articles. Moderate correlations between the GB and BKCI citation counts in social sciences and humanities, with most BKCI results coming from journal articles rather than books, suggests that they could measure the different aspects of impact, however.

Solving the Search for Source Code

Programmers frequently search for source code to reuse using keyword searches. The search effectiveness in facilitating reuse, however, depends on the programmer's ability to specify a query that captures how the desired code may have been implemented. Further, the results often include many irrelevant matches that must be filtered manually. More semantic search approaches could address these limitations, yet existing approaches are either not flexible enough to find approximate matches or require the programmer to define complex specifications as queries. We propose a novel approach to semantic code search that addresses several of these limitations and is designed for queries that can be described using a concrete input/output example. In this approach, programmers write lightweight specifications as inputs and expected output examples. Unlike existing approaches to semantic search, we use an SMT solver to identify programs or program fragments in a repository, which have been automatically transformed into constraints using symbolic analysis, that match the programmer-provided specification. We instantiated and evaluated this approach in subsets of three languages, the Java String library, Yahoo! Pipes mashup language, and SQL select statements, exploring its generality, utility, and trade-offs. The results indicate that this approach is effective at finding relevant code, can be used on its own or to filter results from keyword searches to increase search precision, and is adaptable to find approximate matches and then guide modifications to match the user specifications when exact matches do not already exist. These gains in precision and flexibility come at the cost of performance, for which underlying factors and mitigation strategies are identified.

QUBLE: towards blending interactive visual subgraph search queries on large networks

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the gui to filter irrelevant matches and prefetch partial query results [8]. Our recent attempts at implementing this vision [8, 9] show significant improvement in system response time (srt) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. In this paper, we propose a novel algorithm called quble (QUery Blender for Large nEtworks) to realize this visual subgraph querying paradigm on very large networks (e.g., protein interaction networks, social networks). First, it decomposes a large network into a set of graphlets and supergraphlets using a minimum cut-based graph partitioning technique. Next, it mines approximate frequent and small infrequent fragments (sifs) from them and identifies their occurrences in these graphlets and supergraphlets. Then, the indexing framework of [9] is enhanced so that the mined fragments can be exploited to index graphlets for efficient blending of visual subgraph query formulation and query processing. Extensive experiments on large networks demonstrate effectiveness of quble.

Combining Feature- and Correspondence-Based Methods for Visual Object Recognition

We present an object recognition system built on a combination of feature- and correspondence-based pattern recognizers. The feature-based part, called preselection network, is a single-layer feedforward network weighted with the amount of information contributed by each feature to the decision at hand. For processing arbitrary objects, we employ small, regular graphs whose nodes are attributed with Gabor amplitudes, termed parquet graphs. The preselection network can quickly rule out most irrelevant matches and leaves only the ambiguous cases, so-called model candidates, to be verified by a rudimentary version of elastic graph matching, a standard correspondence-based technique for face and object recognition. According to the model, graphs are constructed that describe the object in the input image well. We report the results of experiments on standard databases for object recognition. The method achieved high recognition rates on identity and pose. Unlike many other models, it can also cope with varying background, multiple objects, and partial occlusion.

Bioinformatic Requirements for Protein Database Searching Using Predicted Epitopes from Disease-associated Antibodies

We describe a new approach to identify proteins involved in disease pathogenesis. The technology, Epitope-Mediated Antigen Prediction (E-MAP), leverages the specificity of patients' immune responses to disease-relevant targets and requires no prior knowledge about the protein. E-MAP links pathologic antibodies of unknown specificity, isolated from patient sera, to their cognate antigens in the protein database. The E-MAP process first involves reconstruction of a predicted epitope using a peptide combinatorial library. We then search the protein database for closely matching amino acid sequences. Previously published attempts to identify unknown antibody targets in this manner have largely been unsuccessful for two reasons: 1) short predicted epitopes yield too many irrelevant matches from a database search and 2) the epitopes may not accurately represent the native antigen with sufficient fidelity. Using an in silico model, we demonstrate the critical threshold requirements for epitope length and epitope fidelity. We find that epitopes generally need to have at least seven amino acids, with an overall accuracy of >70% to the native protein, in order to correctly identify the protein in a nonredundant protein database search. We then confirmed these findings experimentally, using the predicted epitopes for four monoclonal antibodies. Since many predicted epitopes often fail to achieve the seven amino acid threshold, we demonstrate the efficacy of paired epitope searches. This is the first systematic analysis of the computational framework to make this approach viable, coupled with experimental validation.

METEOR-S WSDI: A Scalable P2P Infrastructure of Registries for Semantic Publication and Discovery of Web Services

Web services are the new paradigm for distributed computing. They have much to offer towards interoperability of applications and integration of large scale distributed systems. To make Web services accessible to users, service providers use Web service registries to publish them. Current infrastructure of registries requires replication of all Web service publications in all Universal Business Registries. Large growth in number of Web services as well as the growth in the number of registries would make this replication impractical. In addition, the current Web service discovery mechanism is inefficient, as it does not support discovery based on the capabilities of the services, leading to a lot of irrelevant matches. Semantic discovery or matching of services is a promising approach to address this challenge. In this paper, we present a scalable, high performance environment for Web service publication and discovery among multiple registries. This work uses an ontology-based approach to organize registries into domains, enabling domain based classification of all Web services. Each of these registries supports semantic publication and discovery of Web services. We believe that the semantic approach suggested in this paper will significantly improve Web service publication and discovery involving a large number of registries. This paper describes the implementation and architecture of the METEOR-S Web Service Discovery Infrastructure, which leverages peer-to-peer computing as a scalable solution.

Toward high-precision service retrieval

Online repositories are increasingly called on to provide access to services that describe or provide useful behaviors. Existing techniques for finding services offer low retrieval precision, returning many irrelevant matches. We introduce a novel service retrieval approach that captures service semantics using process models, and applies a pattern-matching algorithm to find the services with the behavior the user wants. Evaluations suggest that process-based queries offer substantially greater retrieval precision than existing approaches and scale well with the number of services being accessed.

Perspectives: sequence data base searching in the era of large-scale genomic sequencing.

Large-scale sequencing of human and model organism genomes will have a profound impact on our ability to use sequence data base searching to predict the biochemical functions of sequences of interest. Despite the great value of more sequences in the data bases, a huge increase in data base size will also have adverse effects on data base searches. Upcoming problems will include (1) greatly increased search times, (2) an increase in background noise of high-scoring but biologically irrelevant matches, (3) inaccurate coding region prediction, leading to problems in protein data base searching, and (4) limited first-pass sequence annotation, making it difficult to determine the biological relevance of data base hits. Improved data base annotation tools and construction of smaller data bases of representative and highly-annotated sequences for first-pass analyses will be essential to deal with the impending flood of new genomic sequence.