Minimum Edit Research Articles

Reverse-Engineering Satire, or “Paper on Computational Humor Accepted despite Making Serious Advances”

Humor is an essential human trait. Efforts to understand humor have called out links between humor and the foundations of cognition, as well as the importance of humor in social engagement. As such, it is a promising and important subject of study, with relevance for artificial intelligence and human– computer interaction. Previous computational work on humor has mostly operated at a coarse level of granularity, e.g., predicting whether an entire sentence, paragraph, document, etc., is humorous. As a step toward deep understanding of humor, we seek fine-grained models of attributes that make a given text humorous. Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines. The corpus is constructed via Unfun.me, an online game that incentivizes players to make minimal edits to satirical headlines with the goal of making other players believe the results are serious headlines. The edit operations used to successfully remove humor pinpoint the words and concepts that play a key role in making the original, satirical headline funny. Our analysis reveals that the humor tends to reside toward the end of headlines, and primarily in noun phrases, and that most satirical headlines follow a certain logical pattern, which we term false analogy. Overall, this paper deepens our understanding of the syntactic and semantic structure of satirical news headlines and provides insights for building humor-producing systems.

Machine Learning–Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance

Three-dimensional echocardiography (3DE) allows accurate and reproducible measurements of right ventricular (RV) size and function. However, widespread implementation of 3DE in routine clinical practice is limited because the existing software packages are relatively time-consuming and skill demanding. The aim of this study was to test the accuracy and reproducibility of new machine learning- (ML-) based, fully automated software for three-dimensional quantification of RV size and function. Fifty-six unselected patients with a wide range of RV size and function and image quality, referred for clinically indicated cardiac magnetic resonance (CMR) imaging, underwent a transthoracic 3DE exam on the same day. End-systolic and end-diastolic RV volumes (ESV, EDV) and ejection fraction (EF) were measured using the ML-based algorithm and compared with CMR reference values using Bland-Altman and linear regression analyses. RV function quantification by echocardiography was feasible in all patients. The automatic approach was accurate in 32% patients with analysis time of 15±1seconds and 100% reproducible. Endocardial contour editing was necessary after the automated postprocessing in the remaining 68% patients, prolonging analysis time to 114±71seconds. With these minimal adjustments, RV volumes and EF measurements were accurate in comparison with CMR reference (biases: EDV, -25.6±21.1mL; ESV, -7.4±16mL; EF, -3.3%±5.2%) and showed excellent reproducibility reflected by coefficients of variation <7% and intraclass correlations ≥0.95 for all measurements. The new ML-based 3DE algorithm provided accurate and completely reproducible RV volume and EF measurements in one-third of unselected patients without any boundary editing. In the remaining patients, quick minimal editing resulted in reasonably accurate measurements with excellent reproducibility. This approach provides a promising solution for fast three-dimensional quantification of RV size and function.

Indian Board of Orthodontics Case Report – Dr. Prem Prakash Memorial Award

This case report was adjudged one of the best-treated cases displayed in the Indian Board of Orthodontics after the board certification process. The summary of the treatment and various records are reprinted here with minimal editing and reformatting so that the presentation resembles the actual documents submitted to the board.

Improved dynamic match phone lattice search for Persian spoken term detection system in online and offline applications

Spoken term detection (STD) refers to discovering all occurrences of a given term in a set of speech utterances. One of the well-known approaches for the STD system is the phone lattice search (PLS) that produces a phone-based lattice of speech utterances. Since the accuracy of a phone recognizer affects the accuracy of the STD system, the PLS approach utilizes the minimum edit distance (MED) measure to compensate the phone recognizer errors. While this measure increases the detection rate, it also raises the false alarm rate. In this paper, we consider the PLS approach as the baseline. Then, we use Viterbi scoring and Jaro-Winkler similarity measure in order to decrease the false alarm rate. Since the proposed approach uses more techniques than the baseline approach, the search speed may decrease. To overcome this problem, we use lattice pruning and indexing techniques such as depth first search algorithm to increase the search speed in online and offline applications, respectively. We report the experimental results for monophone-based and triphone-based STD system. The results indicate that using triphone-based STD system improved the performance about 2% in comparison with monophone-based STD system. Moreover, when we used triphone-based models, the proposed approach including MED measure, Viterbi scores and Jaro-Winkler similarity measure improved the accuracy of the method with only MED measure, about 17%.

Platforms of invivo genome editing with inducible Cas9 for advanced cancer modeling.

The emergence of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 technology has dramatically advanced how we manipulate the genome. Regarding in vivo experiments, Cas9‐transgenic animals could provide efficient and complex genome editing. However, this potential has not been fully realized partly due to a lack of convenient platforms and limited examples of successful disease modeling. Here, we devised two doxycycline (Dox)‐inducible Cas9 platforms that efficiently enable conditional genome editing at multiple loci in vitro and in vivo. In these platforms, we took advantage of a site‐specific multi‐segment cloning strategy for rapid and easy integration of multiple single guide (sg)RNAs. We found that a platform containing rtTA at the Rosa26 locus and TRE‐Cas9 together with multiple sgRNAs at the Col1a1 locus showed higher efficiency of inducible insertions and deletions (indels) with minimal leaky editing. Using this platform, we succeeded to model Wilms’ tumor and the progression of intestinal adenomas with multiple mutations including an activating mutation with a large genomic deletion. Collectively, the established platform should make complicated disease modeling in the mouse easily attainable, extending the range of in vivo experiments in various biological fields including cancer research.

Detecting and correcting real-word errors in Tamil sentences

Spell checkers concern two types of errors namely non-word errors and real-word errors. Non-word errors can be of two categories: First one is that the word itself is invalid; the other is that the word is valid but not present in a valid lexicon. Real-word error means the word is valid but inappropriate in the context of the sentence. An approach to correcting real-word errors in Tamil language is proposed in this paper. A bigram probability model is constructed to determine appropriateness of the valid word in the context of the sentence using a 3GB volume of corpora of Tamil text. In case of lacking appropriateness, the word is marked as a real-word error and minimum edit distance technique is used to find lexically similar words, and the appropriateness of such words is measured by a word-level n-gram language probability model. A hash table with word-length as the key is used to speed up the search for words to check for the lexical similarity. Words of lengths of m-1 to m+1 are considered with m being the length of the word found to be ‘inappropriate’. Test results show that the suggestions generated by the system are with more than 98% accuracy as approved by a Scholar in Tamil.

Application of Genetic Algorithms for Finding Edit Distance between Process Models

Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics. Recently, with the development of process mining techniques, it became important to adapt and apply existing graph analysis methods to examine process models (annotated graphs) discovered from event data. In particular, finding graph-edit distance techniques can be used to reveal patterns (subprocesses), compare discovered process models. As it was shown experimentally and theoretically justified, exact methods for finding graph-edit distances between discovered process models (and graphs in general) are computationally expensive and can be applied to small models only. In this paper, we present and assess accuracy and performance characteristics of an inexact genetic algorithm applied to find distances between process models discovered from event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from event logs by using different process discovery algorithms. We show that the genetic algorithm allows us to dramatically reduce the time of comparison and produces results which are close to the optimal solutions (minimal graph edit distances calculated by the exact search algorithm).

Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income Countries.

PurposeWe assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide.MethodsAutocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics.ResultsRetrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram–based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively.ConclusionAutomated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms.

A Comprehensive Orthodontic, Surgical, and Prosthodontic Rehabilitation of Congenital Unilateral Cleft Lip and Cleft Palate Patient: An Interdisciplinary Case Report

This case was submitted to the board under category IV for the Indian Board of Orthodontics examination, November 2017. The summary of the treatment, various records, treatment progress, and critical appraisal are reprinted here with minimal editing and reformatting and hence that the presentation resembles the actual documents submitted to the board.

A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit

This article develops an integrated methodology to investigate dominant trajectories of neighborhood change that are often confronted in urban studies. Currently, researchers are using k-means cluster analysis to establish diverse neighborhood typologies and principal component analysis (PCA) to identify socioeconomic interactions explaining the neighborhood typologies. Little attention has been given to longitudinal trajectories and dynamics of neighborhood evolution over a long period. Our new model adapts a newly developed dynamic sequential analysis (the weighted minimum edit distance algorithm) in big data analytics to sort and identify dominant trajectories of neighborhood change. Our model also innovatively synthesizes three statistical procedures—k-means, PCA, and analysis of variance—to derive the weight matrix, which naturally integrates the core characteristics of urban neighborhood changes into the sequential reordering. Using the census data in Metro Detroit over five census years (1970, 1980, 1990, 2000, and 2010), this model was tested to identify a unique city's demographic and socioeconomic transition pattern in the past forty years. This model successfully provided a thorough analysis of the neighborhood typologies and exhibited a much-enhanced performance in identifying long-term trajectories of urban evolution.

Valuable Research in Fake Journals and Self-boasting with Fake Metrics.

Valuable research works are getting wasted by publishing them in so called, fake journals (pseudo journals, hijacked or predatory journals). Fake journals are the journals which do not perform peer review or minimal language editing in the name of peer review. Unaware of negative consequences of publishing in fake journals, budding or novice academician/clinician/researcher continue to fall prey for them. Present scenario, forced them to get their valuable research published for promotion, pay hike, academic or research reputation, etc. But, they boast themselves by publishing them in fake journals with fake metrics. By making publication in fake journals, only the publishers make profit and pseudo enhance the bio-data of novice academician. It is becoming a big business. In this short communication, we have highlighted the most common prevalent issue among the novice or budding academician.

Guidelines for Contributors to AJOT.

Guidelines for Contributors to AJOT.

Indian Board of Orthodontics Case Report – Dr. Prem Prakash Memorial Award

This case report was adjudged one of the best-treated cases displayed in the Indian Board of Orthodontics after the board certification process. The summary of the treatment and various records are reprinted here with minimal editing and reformatting so that the presentation resembles the actual documents submitted to the board.

Minimal point volumetric outlining and editing for radiotherapy treatment planning

AbstractPurposeA novel radiotherapy outlining application uses a small number of user-assigned points across orthogonal planes to generate a mesh which is then edited across multiple slices using innovative three-dimensional (3D) sculpting tools. This paper presents the results of a bladder outlining study that compared times and volumes for the new tool with those of a conventional manual outlining tool.Materials and methodsAll students undertaking their first University radiotherapy planning module were invited to participate. Following training, they performed a timed outlining of the same male bladder dataset and provided feedback on their preferred method.ResultsComparison of times from the resulting ten datasets demonstrated that the 3D segmentation tool was significantly faster than conventional software with a mean time of 11·9 minutes compared with 19·2 minutes (p=0·03). The users expressed a preference for the new tool (eight users) over the conventional outlining software (two users).ConclusionsA minimal point 3D volumetric manual outlining tool utilising orthogonal computed tomography planes demonstrated significant time saving for bladder segmentation compared with axial-based outlining within a group of novice outliners. Future work aims to establish the role of the 3D multi-slice sculpting tools in editing of auto-segmentation derived contour sets.

Legislative amendments and informal politics in the European Union: A text reuse approach

The ability to amend legislative proposals introduced by the Commission is central to legislative process in the European Union. Despite this, very few attempts have been made to capture and explain such amendments. This study addresses this gap in the literature by considering the changes between the Commission’s proposals and the final legislative outcome passed by the European Union. It does so by implementing minimum edit distance algorithms to measure changes between legislative proposals and outcomes. The findings suggest that legislative amendments are determined by the formal and informal institutional structures in which negotiations take place and characteristics of the proposal itself. Our conclusions contribute to the ongoing debate on the nature and distribution of legislative powers in the European Union.

Resection planning for robotic acoustic neuroma surgery.

Acoustic neuroma surgery is a procedure in which a benign mass is removed from the internal auditory canal (IAC). Currently, this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working on the development of an acoustic neuroma surgery robot (ANSR) to perform the initial drilling procedure. Planning the ANSR's drilling region using preoperative CT requires expertise and takes about 35min. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and require minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from nine subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2min were required in each case for the surgeon to verify and edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeon's planning effort.

How time-based alignment of realized acoustic landmarks and predicted landmarks improves analysis of feature cue modification patterns in speech

Acoustic landmarks are abrupt spectral changes that signal the underlying manner features of phonemes (Stevens 2002). Our goal in developing an automatic method to detect these landmarks is to create a robust, knowledge-based approach to phoneme extraction in automatic speech signal processing. One challenge in such an approach is posed by massive reductions, in which many landmarks and other feature cues are missing. Thus, there is a need to hand-label the acoustic landmarks that actually occur in the speech signal and align them with predicted landmarks. However, this often results in a discrepancy between the locations of labels for the automatically generated and hand-labeled landmarks, which leads to an inaccurate analysis of where realized landmarks occur with respect to word and phoneme interval boundaries. We attempted to solve this issue with a time-based alignment method derived from the minimum edit distance algorithm. The result improved alignment of the realized landmark labels with the predicted landmark labels, enabling a more accurate analysis of modifications in the hand-labeled (realized) landmark tiers.

Exact Reconstruction From Insertions in Synchronization Codes

This paper studies problems in data reconstruction, an important area with numerous applications. In particular, we examine the reconstruction of binary and nonbinary sequences from synchronization (insertion/deletion-correcting) codes. These sequences have been corrupted by a fixed number of symbol insertions (larger than the minimum edit distance of the code), yielding a number of distinct traces to be used for reconstruction. We wish to know the minimum number of traces needed for exact reconstruction. This is a general version of a problem tackled by Levenshtein for uncoded sequences. We introduce an exact formula for the maximum number of common supersequences shared by sequences at a certain edit distance, yielding an upper bound on the number of distinct traces necessary to guarantee exact reconstruction. Without specific knowledge of the code words, this upper bound is tight. We apply our results to the famous single deletion/insertion-correcting Varshamov–Tenengolts (VT) codes and show that a significant number of VT code word pairs achieve the worst case number of outputs needed for exact reconstruction. We also consider extensions to other channels, such as adversarial deletion and insertion/deletion channels and probabilistic channels.

Orthology Correction for Gene Tree Reconstruction: Theoretical and Experimental Results

We consider how the orthology/paralogy information can be corrected in order to represent a gene tree, a problem that has recently gained interest in phylogenomics. Interestingly, the problem is related to the Minimum CoGraph Editing problem on the relation graph that represents orthology/paralogy information, where we want to minimize the number of edit operations on the given relation graph in order to obtain a cograph. In this paper we provide both theoretical and experimental results on the Minimum CoGraph Editing problem. On the theoretical side, we provide approximation algorithms for bounded degree relation graphs, for the general problem and for the problem restricted to deletion of edges. On the experimental side, we present a genetic algorithm for Minimum CoGraph Editing and we provide an experimental evaluation of the genetic algorithm on synthetic data.

Strategies for Segmenting the Upper Airway in Cone-Beam Computed Tomography (CBCT) Data

The wide availability, low radiation dose and short acquisition time of Cone-Beam CT (CBCT) scans make them an attractive source of data for compiling databases of anatomical structures. However CBCT has higher noise and lower contrast than helical slice CT, which makes segmentation more challenging and the optimal methods are not yet known. This paper evaluates several methods of segmenting airway geometries (nares, nasal cavities and pharynx) from typical dental quality head and neck CBCT data. The nasal cavity has narrow and intricate passages and is separated from the paranasal sinuses by thin walls, making it is susceptible to either over- or under-segmentation. The upper airway was split into two: the nasal cavity and the pharyngeal region (nasopharynx to larynx). Each part was segmented using global thresholding, multi-step level-set, and region competition methods (the latter using thresholding, clustering and classification initialisation and edge attraction techniques). The segmented 3D surfaces were evaluated against a reference manual segmentation using distance-, overlap- and volume-based metrics. Global thresholding, multi-step level-set, and region competition all gave satisfactory results for the lower part of the airway (nasopharynx to larynx). Edge attraction failed completely. A semi-automatic region-growing segmentation with multi-thresholding (or classification) initialization offered the best quality segmentation. With some minimal manual editing, it resulted in an accurate upper airway model, as judged by the similarity and volumetric indices, while being the least time consuming of the semi-automatic methods, and relying the least on the operator’s expertise.