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Journal articles on the topic 'AUTOMATIC EVALUATING'

Author: Grafiati

Published: 11 September 2023

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1

Fabbri, Alexander R., Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. "SummEval: Re-evaluating Summarization Evaluation." Transactions of the Association for Computational Linguistics 9 (2021): 391–409. http://dx.doi.org/10.1162/tacl_a_00373.

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Abstract The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress. We address the existing shortcomings of summarization evaluation methods along five dimensions: 1) we re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations; 2) we consistently benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics; 3) we assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format; 4) we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics; and 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN/Daily Mail dataset annotated by both expert judges and crowd-source workers. We hope that this work will help promote a more complete evaluation protocol for text summarization as well as advance research in developing evaluation metrics that better correlate with human judgments.

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Liu, Zhixiang, Huan Liu, Yuanji Chen, Wenbo Zhang, Wei Song, Liping Zhou, Quanmiao Wei, and Jingxiang Xu. "Evaluating Airfoil Mesh Quality with Transformer." Aerospace 10, no. 2 (January 23, 2023): 110. http://dx.doi.org/10.3390/aerospace10020110.

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Mesh quality is a major factor affecting the structure of computational fluid dynamics (CFD) calculations. Traditional mesh quality evaluation is based on the geometric factors of the mesh cells and does not effectively take into account the defects caused by the integrity of the mesh. Ensuring the generated meshes are of sufficient quality for numerical simulation requires considerable intervention by CFD professionals. In this paper, a Transformer-based network for automatic mesh quality evaluation (Gridformer), which translates the mesh quality evaluation into an image classification problem, is proposed. By comparing different mesh features, we selected the three features that highly influence mesh quality, providing reliability and interpretability for feature extraction work. To validate the effectiveness of Gridformer, we conduct experiments on the NACA-Market dataset. The experimental results demonstrate that Gridformer can automatically identify mesh integrity quality defects and has advantages in computational efficiency and prediction accuracy compared to widely used neural networks. Furthermore, a complete workflow for automatic generation of high-quality meshes based on Gridformer was established to facilitate automated mesh generation. This workflow can produce a high-quality mesh with a low-quality mesh input through automatic evaluation and optimization cycles. The preliminary implementation of automated mesh generation proves the versatility of Gridformer.

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Shinar, David, Meir Meir, and Israel Ben-Shoham. "How Automatic Is Manual Gear Shifting?" Human Factors: The Journal of the Human Factors and Ergonomics Society 40, no. 4 (December 1998): 647–54. http://dx.doi.org/10.1518/001872098779649346.

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Manual gear shifting is often used as an example of an automated (vs. controlled) process in driving. The present study provided an empirical evaluation of this assumption by evaluating sign detection and recall performance of novice and experienced drivers driving manual shift and automatic transmission cars in a downtown area requiring frequent gear shifting. The results showed that manual gear shifting significantly impaired sign detection performance of novice drivers using manual gears compared with novice drivers using an automatic transmission, whereas no such differences existed between the two transmission types for experienced drivers. The results clearly demonstrate that manual gear shifting is a complex psychomotor skill that is not easily (or quickly) automated and that until it becomes automated, it is an attention-demanding task that may impair other monitoring aspects of driving performance. Actual or potential applications of this research include a reevaluation of the learning process in driving and the need for phased instruction in driving from automatic gears to manual gears.

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Sai, Ananya B., Akash Kumar Mohankumar, and Mitesh M. Khapra. "A Survey of Evaluation Metrics Used for NLG Systems." ACM Computing Surveys 55, no. 2 (March 31, 2023): 1–39. http://dx.doi.org/10.1145/3485766.

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In the last few years, a large number of automatic evaluation metrics have been proposed for evaluating Natural Language Generation (NLG) systems. The rapid development and adoption of such automatic evaluation metrics in a relatively short time has created the need for a survey of these metrics. In this survey, we (i) highlight the challenges in automatically evaluating NLG systems, (ii) propose a coherent taxonomy for organising existing evaluation metrics, (iii) briefly describe different existing metrics, and finally (iv) discuss studies criticising the use of automatic evaluation metrics. We then conclude the article highlighting promising future directions of research.

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Sungdo Moon, Byoungro So, and M. W. Hall. "Evaluating automatic parallelization in SUIF." IEEE Transactions on Parallel and Distributed Systems 11, no. 1 (2000): 36–49. http://dx.doi.org/10.1109/71.824639.

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Gonzalez-Rodriguez, Joaquin. "Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)." Loquens 1, no. 1 (June 30, 2014): e007. http://dx.doi.org/10.3989/loquens.2014.007.

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7

Oña, Edwin, Patricia Sánchez-Herrera, Alicia Cuesta-Gómez, Santiago Martinez, Alberto Jardón, and Carlos Balaguer. "Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier." Sensors 18, no. 9 (August 31, 2018): 2876. http://dx.doi.org/10.3390/s18092876.

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Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson’s disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes’ displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user’s performance.

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White, Hannah, Joshua Penney, Andy Gibson, Anita Szakay, and Felicity Cox. "Evaluating automatic creaky voice detection methods." Journal of the Acoustical Society of America 152, no. 3 (September 2022): 1476–86. http://dx.doi.org/10.1121/10.0013888.

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There is growing interest in research on the non-modal voice quality, creaky voice; however, its identification often relies on time-consuming manual annotation, leading to a recent focus on automatic creak detection methods. Various automatic methods have been proposed, which rely on varying types and combinations of acoustic cues for creak detection. In this paper, we compare the performance of three automatic tools, the AntiMode method, the Creak Detector algorithm, and the Roughness algorithm, against manual annotation of creak using data from 80 Australian English speakers. We explore the possibility that tools used in combination may yield more accurate creak detection than individual tools used alone. Based on method comparisons, we present options for researchers, including an “out-of-the-box” approach, which supports combining automatic tools, and propose additional steps to further improve creak detection. We found restricting analysis to sonorant segments significantly improves automatic creak detection, and tools performed consistently better on female speech than male speech. Findings support previous work showing detection may be optimised by performing a creak probability threshold sweep on a subset of data prior to applying the Creak Detector algorithm on new datasets. Results provide promising solutions for advancing efficient large-scale research on creaky voice.

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Bernstein, Jared, and Elizabeth Rosenfeld. "Evaluating automatic speech-to-speech interpreting." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 2079. http://dx.doi.org/10.1121/1.4755669.

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van Diepen, Merel, and Philip Hans Franses. "Evaluating chi-squared automatic interaction detection." Information Systems 31, no. 8 (December 2006): 814–31. http://dx.doi.org/10.1016/j.is.2005.03.002.

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11

Torralba, Álvaro, Jendrik Seipp, and Silvan Sievers. "Automatic Instance Generation for Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 376–84. http://dx.doi.org/10.1609/icaps.v31i1.15983.

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The benchmarks from previous International Planning Competitions (IPCs) are the de-facto standard for evaluating planning algorithms. The IPC set is both a collection of planning domains and a selection of instances from these domains. Most of the domains come with a parameterized generator that generates new instances for a given set of parameter values. Due to the steady progress of planning research some of the instances that were generated for past IPCs are inadequate for evaluating current planners. To alleviate this problem, we introduce Autoscale, an automatic tool that selects instances for a given domain. Autoscale takes into account constraints from the domain designer as well as the performance of current planners to generate an instance set of appropriate difficulty, while avoiding too much bias with respect to the considered planners. We show that the resulting benchmark set is superior to the IPC set and has the potential of improving empirical evaluation of planning research.

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Sai, Ananya B., Mithun Das Gupta, Mitesh M. Khapra, and Mukundhan Srinivasan. "Re-Evaluating ADEM: A Deeper Look at Scoring Dialogue Responses." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6220–27. http://dx.doi.org/10.1609/aaai.v33i01.33016220.

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Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. ADEM (Lowe et al. 2017) formulated the automatic evaluation of dialogue systems as a learning problem and showed that such a model was able to predict responses which correlate significantly with human judgements, both at utterance and system level. Their system was shown to have beaten word-overlap metrics such as BLEU with large margins. We start with the question of whether an adversary can game the ADEM model. We design a battery of targeted attacks at the neural network based ADEM evaluation system and show that automatic evaluation of dialogue systems still has a long way to go. ADEM can get confused with a variation as simple as reversing the word order in the text! We report experiments on several such adversarial scenarios that draw out counterintuitive scores on the dialogue responses. We take a systematic look at the scoring function proposed by ADEM and connect it to linear system theory to predict the shortcomings evident in the system. We also devise an attack that can fool such a system to rate a response generation system as favorable. Finally, we allude to future research directions of using the adversarial attacks to design a truly automated dialogue evaluation system.

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13

Lokbani, Ahmed Chaouki. "A New Metric of Validation for Automatic Text Summarization by Extraction." International Journal of Strategic Information Technology and Applications 8, no. 3 (July 2017): 20–40. http://dx.doi.org/10.4018/ijsita.2017070102.

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In this article, the author proposes a new metric of evaluation for automatic summaries of texts. In this case, the adaptation of the F-measure that generates a hybrid method of evaluating an automatic summary at the same time as both extrinsic and intrinsic. The article starts by studying the feasibility of adaptation of the F-measure for the evaluation of automatic summarization. After that, the author defines how to calculate the F-measure for a candidate summary. Text is presented with a term vector which can be either a word or a phrase, with a binary-weighted or occurrence. Finally, to determine to the exactitude of evaluation of the F-measure for automatic summarization by extraction calculates correlation with the ROUGE Evaluation.

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OBAIYS, SUZAN J., Z. K. ESKHUVATOV, and N. M. A. NIK LONG. "AUTOMATIC QUADRATURE SCHEME FOR EVALUATING HYPERSINGULAR INTEGRALS." International Journal of Modern Physics: Conference Series 09 (January 2012): 581–85. http://dx.doi.org/10.1142/s2010194512005697.

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Hasegawa constructed the automatic quadrature scheme (AQS), of Cauchy principle value integrals for smooth functions. There is a close connection between Hadamard and Cauchy principle value integral. In this paper, we modify AQS for hypersingular integrals with second-order singularities, using hasegawa's formula and based on the relations between Hadamard finite part integral and Cauchy principle value integral. Numerical experiments are also given, to validate the modified AQS.

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15

SCHMIDT, LINDA C., and JOHN JACKMAN. "Evaluating assembly sequences for automatic assembly systems." IIE Transactions 27, no. 1 (February 1995): 23–31. http://dx.doi.org/10.1080/07408179508936714.

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Marrero-Fernández, Pedro, Arquímedes Montoya-Padrón, Antoni Jaume-i-Capó, and Jose Maria Buades Rubio. "Evaluating the Research in Automatic Emotion Recognition." IETE Technical Review 31, no. 3 (May 4, 2014): 220–32. http://dx.doi.org/10.1080/02564602.2014.906863.

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17

Mohamed A. Wahdan, Mohamed A. Wahdan, and Manar Hassan Mohamed A. Wahdan. "Automatic Assessment of Materiality: A Knowledge-based Approach." International Journal of Computer Auditing 1, no. 1 (December 2019): 064–91. http://dx.doi.org/10.53106/256299802019120101004.

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<p>This paper explores how can a knowledge–based system (KBS) be used in auditing especially when the task requires using professional judgments. It concerned with modeling, implementing and evaluating a KBS called Materiality EXpert (MEX). MEX can assess the level of planning materiality and performance materiality as professionals act. Knowledge used to build MEX is acquired from literature, international standards on auditing, and experienced auditors using questionnaire as well as unstructured and structured interviews. MEX was evaluated by 34 auditors from different audit firms in Egypt including international audit firms. The evaluation results acquired from experienced auditors in Egypt indicated that MEX successfully executes the task of assessing the level of planning materiality and performance materiality. Moreover, MEX is efficient, effective, and acceptable from auditors for assessing the level of planning materiality and performance materiality.</p> <p> </p>

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18

Silva, Eliazar de Carvalho, and Cassio Vinicius Serafim Prazeres. "An Extensible Platform to Evaluate Semantic Web Services Automatic Composition Approaches." International Journal of Semantic Computing 09, no. 01 (March 2015): 47–66. http://dx.doi.org/10.1142/s1793351x15500026.

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The problem of automatically composing Web Services that can match client's request has been the focus of several researchers. However, only a small number of such studies have evaluated and compared these different techniques on equal terms. Generally, these techniques have been evaluated on an ad hoc basis. This article presents a platform for evaluating the different techniques for the automatic composition of Semantic Web Services. The proposed platform provides: (i) an extensible architecture that enables the inclusion and evaluation of different techniques; (ii) a suite of metrics aimed at evaluating these techniques.

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Bischof, Christian H., H. Martin Bücker, Arno Rasch, Emil Slusanschi, and Bruno Lang. "Automatic Differentiation of the General-Purpose Computational Fluid Dynamics Package FLUENT." Journal of Fluids Engineering 129, no. 5 (October 12, 2006): 652–58. http://dx.doi.org/10.1115/1.2720475.

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Derivatives are a crucial ingredient to a broad variety of computational techniques in science and engineering. While numerical approaches for evaluating derivatives suffer from truncation error, automatic differentiation is accurate up to machine precision. The term automatic differentiation comprises a set of techniques for mechanically transforming a given computer program to another one capable of evaluating derivatives. A common misconception about automatic differentiation is that this technique only works on local pieces of fairly simple code. Here, it is shown that automatic differentiation is not only applicable to small academic codes, but scales to advanced industrial software packages. In particular, the general-purpose computational fluid dynamics software package FLUENT is transformed by automatic differentiation.

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Setiadi Citawan, Rico, Viny Christanti Mawardi, and Bagus Mulyawan. "Automatic Essay Scoring in E-learning System Using LSA Method with N-Gram Feature for Bahasa Indonesia." MATEC Web of Conferences 164 (2018): 01037. http://dx.doi.org/10.1051/matecconf/201816401037.

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In the world of education, e-learning system is a system that can be used to support the educational process. E-learning system is usually used by educators to learners in evaluating learning outcomes. In the process of evaluating learning outcomes in the e-learning system, the form type of exam questions that are often used are multiple choice and short stuffing. For exam questions in the form of essays are rarely used in the evaluation process of educational because of the difference in the subjectivity and time consuming in the assessment process. In this design aims to create an automatic essay scoring feature on e-learning system that can be used to support the learning process. The method used in automatic essay scoring is Latent Semantic Analysis (LSA) with n-gram feature. The evaluation results of the design features automatic essay scoring showed that the accuracy of the average achieved in the amount of 78.65 %, 58.89 %, 14.91 %, 71.37 %, 64.49 % in the LSA unigram, bigram, trigram, unigram + bigram, unigram + bigram + trigram.

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Xu, Nuo, and Samuel H. Huang. "Multiple Attributes Utility Analysis in Setup Plan Evaluation." Journal of Manufacturing Science and Engineering 128, no. 1 (May 29, 2005): 220–27. http://dx.doi.org/10.1115/1.2117407.

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Setup planning is an intermediate phase of process planning, and automating setup planning constitutes a critical component of computer-aided process planning. The common absence of a clearly defined optimality of setup plans has been a major obstacle to converting automatic setup planning research progress into real advancement in practice. The optimality of a setup plan is a multiple attributes problem associated with uncertainties, and human interaction is vital in clarifying the optimality structure in a dynamic setup planning environment. In this paper, a quantitative setup plan evaluation system driven by multiple-attributes utility analysis coupled with manufacturing error simulation is proposed to serve three purposes: (1) to clarify what is optimality of setup plans, (2) to provide a systematic method of evaluating setup plan alternatives quantitatively, and (3) to incorporate in existing automatic setup planning systems a human interface to fulfill their potential values.

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Hutter, F., H. H. Hoos, K. Leyton-Brown, and T. Stuetzle. "ParamILS: An Automatic Algorithm Configuration Framework." Journal of Artificial Intelligence Research 36 (October 30, 2009): 267–306. http://dx.doi.org/10.1613/jair.2861.

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The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithms performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.

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23

Tull, Renetta G., Erin E. Molin, Meaghan M. Lindstedt, and Jessica R. Dykstra. "Evaluating consonant errors in automatic speech recognition programs." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2492. http://dx.doi.org/10.1121/1.4744866.

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24

Ocampo, Brenda, and Todd A. Kahan. "Evaluating automatic attentional capture by self-relevant information." Cognitive Neuroscience 7, no. 1-4 (August 14, 2015): 22–23. http://dx.doi.org/10.1080/17588928.2015.1075486.

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25

Lewis, James R. "Evaluating the Potential Effectiveness of Automatic Document Analysis." International Journal of Speech Technology 7, no. 1 (January 2004): 35–43. http://dx.doi.org/10.1023/b:ijst.0000004805.89798.39.

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26

Lee, Seungjun, Jungseob Lee, Hyeonseok Moon, Chanjun Park, Jaehyung Seo, Sugyeong Eo, Seonmin Koo, and Heuiseok Lim. "A Survey on Evaluation Metrics for Machine Translation." Mathematics 11, no. 4 (February 16, 2023): 1006. http://dx.doi.org/10.3390/math11041006.

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The success of Transformer architecture has seen increased interest in machine translation (MT). The translation quality of neural network-based MT transcends that of translations derived using statistical methods. This growth in MT research has entailed the development of accurate automatic evaluation metrics that allow us to track the performance of MT. However, automatically evaluating and comparing MT systems is a challenging task. Several studies have shown that traditional metrics (e.g., BLEU, TER) show poor performance in capturing semantic similarity between MT outputs and human reference translations. To date, to improve performance, various evaluation metrics have been proposed using the Transformer architecture. However, a systematic and comprehensive literature review on these metrics is still missing. Therefore, it is necessary to survey the existing automatic evaluation metrics of MT to enable both established and new researchers to quickly understand the trend of MT evaluation over the past few years. In this survey, we present the trend of automatic evaluation metrics. To better understand the developments in the field, we provide the taxonomy of the automatic evaluation metrics. Then, we explain the key contributions and shortcomings of the metrics. In addition, we select the representative metrics from the taxonomy, and conduct experiments to analyze related problems. Finally, we discuss the limitation of the current automatic metric studies through the experimentation and our suggestions for further research to improve the automatic evaluation metrics.

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Wang, Na, Lin Xu, Li Yao Li, and Lu Xiong Xu. "Design and Implementation of an Automatic Scoring Subjective Question System Based on Domain Ontology." Advanced Materials Research 753-755 (August 2013): 3039–42. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.3039.

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Automated assessment technology for subjective tests is one of the key techniques of exam systems. A model based on domain ontology is proposed in this paper, which can be used in exam systems to estimate subjective tests. After analysing the present research status of subjective automated assessment technology, the paper makes a study on the construction method of domain ontology by taking software engeering domain as an example. Semantic similarity calculation based on domain ontology is used for automatic assessment in this paper. The automatic assessment system can divide a sentence into a series of phrases by using the natural language processing technology and get the score by evaluating the semantic similarity of the student's answer. The experiments show that the results of the system which has certain valuable feasibility and applicability are credible and the scoring errors are acceptable.

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VO, Bien Van, Martin MACKO, and Hung M. DAO. "Experimental Study of Automatic Weapon Vibrations when Burst Firing." Problems of Mechatronics Armament Aviation Safety Engineering 12, no. 4 (December 30, 2021): 9–28. http://dx.doi.org/10.5604/01.3001.0015.5984.

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The article presents a new approach to finding the dynamic characteristics of automatic weapons, mainly in case of burst firing. The experiments were tested out on a 30 mm AGS-17 grenade launcher mounted on a tripod in the event of a shot. The obtained results are the basis for evaluating the firing stability of an automatic weapon when burst firing, which allows modernising the existing weapons and evaluating similar weapon systems. Furthermore, the outputs can be used to validate a dynamic model of an automatic weapon system mounted on the tripod. The procedure can be used as an example of practical technique and methodology for other weapon systems.

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Souza, Jackson Wilke da Cruz, and Ariani Di Felippo. "Evaluating a typology of signals for automatic detection of complementarity." Domínios de Lingu@gem 16, no. 4 (September 12, 2022): 1517–43. http://dx.doi.org/10.14393/dl52-v16n4a2022-10.

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In a cluster of news texts on the same event, two sentences from different documents might express different multi-document phenomena (redundancy, complementarity, and contradiction). Cross-Document Structure Theory (CST) provides labels to explicitly represent these phenomena. The automatic identification of the multi-document phenomena and their correspondent CST relations is definitely handy for Automatic Multi-Document Summarization since it helps computers understand text meaning. In this paper, we evaluated a typology of (textual) signals for the automatic detection of the CST relations of complementarity (i.e., Historical background, Follow-up and Elaboration) in a multi-document corpus of news texts in Brazilian Portuguese. Using algorithms from different machine-learning paradigms, we obtained classifiers that achieved high general accuracy (higher than 90%), indicating the potential of the signals.

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Cai, Xudong, Shengli Li, and Gengzhong Feng. "Evaluating the performance of government websites: An automatic assessment system based on the TFN-AHP methodology." Journal of Information Science 46, no. 6 (August 12, 2019): 760–75. http://dx.doi.org/10.1177/0165551519866548.

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Government websites are currently important for providing information and services to citizens. It is a crucial task to evaluate the performance of each government website. The traditional evaluation processes based on experts are criticised as being subjective and cannot work in real time. This article proposes a framework and automatic assessment system for evaluating the performance of government websites in real time. To test the proposed framework and the automatic assessment system, we evaluate and classify 70 websites from Shaanxi Province of China. This article provides guidance for government agencies, managers of government websites and researchers.

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Tseng, Cynthia, Xueying He, Wenlin Chen, and Chung-Li Tseng. "Evaluating automatic hand hygiene monitoring systems: A scoping review." Public Health in Practice 4 (December 2022): 100290. http://dx.doi.org/10.1016/j.puhip.2022.100290.

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Ostermann, Fabian, Igor Vatolkin, and Günter Rudolph. "Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation." Transactions of the International Society for Music Information Retrieval 4, no. 1 (November 30, 2021): 210. http://dx.doi.org/10.5334/tismir.90.

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Gröndahl, Peter, and Mauro Onori. "Standardised flexible automatic assembly – evaluating the Mark IV approach." Assembly Automation 20, no. 3 (September 2000): 217–24. http://dx.doi.org/10.1108/01445150010336269.

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Plevin, Rebecca E., Robert Kaufman, Laura Fraade-Blanar, and Eileen M. Bulger. "Evaluating the Potential Benefits of Advanced Automatic Crash Notification." Prehospital and Disaster Medicine 32, no. 2 (January 31, 2017): 156–64. http://dx.doi.org/10.1017/s1049023x16001473.

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AbstractObjectiveAdvanced Automatic Collision Notification (AACN) services in passenger vehicles capture crash data during collisions that could be transferred to Emergency Medical Services (EMS) providers. This study explored how EMS response times and other crash factors impacted the odds of fatality. The goal was to determine if information transmitted by AACN could help decrease mortality by allowing EMS providers to be better prepared upon arrival at the scene of a collision.MethodsThe Crash Injury Research and Engineering Network (CIREN) database of the US Department of Transportation/National Highway Traffic Safety Administration (USDOT/NHTSA; Washington DC, USA) was searched for all fatal crashes between 1996 and 2012. The CIREN database also was searched for illustrative cases. The NHTSA’s Fatal Analysis Reporting System (FARS) and National Automotive Sampling System Crashworthiness Data System (NASS CDS) databases were queried for all fatal crashes between 2000 and 2011 that involved a passenger vehicle. Detailed EMS time data were divided into prehospital time segments and analyzed descriptively as well as via multiple logistic regression models.ResultsThe CIREN data showed that longer times from the collision to notification of EMS providers were associated with more frequent invasive interventions within the first three hours of hospital admission and more transfers from a regional hospital to a trauma center. The NASS CDS and FARS data showed that rural collisions with crash-notification times >30 minutes were more likely to be fatal than collisions with similar crash-notification times occurring in urban environments. The majority of a patient’s prehospital time occurred between the arrival of EMS providers on-scene and arrival at a hospital. The need for extrication increased the on-scene time segment as well as total prehospital time.ConclusionAn AACN may help decrease mortality following a motor vehicle collision (MVC) by alerting EMS providers earlier and helping them discern when specialized equipment will be necessary in order to quickly extricate patients from the collision site and facilitate expeditious transfer to an appropriate hospital or trauma center.PlevinRE, KaufmanR, Fraade-BlanarL, BulgerEM. Evaluating the potential benefits of advanced automatic crash notification. Prehosp Disaster Med. 2017;32(2):156–164.

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Chapman, Peter D., and Paul Troup. "The automatic implantable cardioverter-defibrillator: Evaluating suspected inappropriate shocks." Journal of the American College of Cardiology 7, no. 5 (May 1986): 1075–78. http://dx.doi.org/10.1016/s0735-1097(86)80225-x.

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Castle, Jennifer L. "Evaluating PcGets and RETINA as Automatic Model Selection Algorithms*." Oxford Bulletin of Economics and Statistics 67, s1 (December 2005): 837–80. http://dx.doi.org/10.1111/j.1468-0084.2005.00143.x.

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KURODA, Tomohiro, Nodoka NAKASHIMA, Mayuko KANEDA, Kikue SATO, Naoki OHBOSHI, Ayumu OKADA, Kazuya OKAMOTO, Hiroyuki YOSHIHARA, and Tomoya AKIYAMA. "Evaluating the Workload Reduction of Automatic Vital Data Transmission." Advanced Biomedical Engineering 2 (2013): 124–29. http://dx.doi.org/10.14326/abe.2.124.

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Gal, Avigdor, Ateret Anaby-Tavor, Alberto Trombetta, and Danilo Montesi. "A framework for modeling and evaluating automatic semantic reconciliation." VLDB Journal 14, no. 1 (March 2005): 50–67. http://dx.doi.org/10.1007/s00778-003-0115-z.

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Villarin, L., and F. Goss. "47: Evaluating Emergency Department Crowding Using Automatic Infrared Tracking." Annals of Emergency Medicine 52, no. 4 (October 2008): S56. http://dx.doi.org/10.1016/j.annemergmed.2008.06.112.

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Mulder, W. A., and A. P. E. ten Kroode. "Automatic velocity analysis by differential semblance optimization." GEOPHYSICS 67, no. 4 (July 2002): 1184–91. http://dx.doi.org/10.1190/1.1500380.

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We present a method for automatic velocity analysis of seismic data based on differential semblance optimization (DSO). The data are mapped for each offset from the time domain to the depth domain by a Born migration scheme using ray tracing with the efficient wavefront construction method. The DSO cost functional is evaluated by taking differences of the migration images for neighboring offsets. The gradient of this functional with respect to the underlying velocity model is obtained by a first‐order approximation of the adjoint‐state method, leading to an optimal complexity: the cost of evaluating the gradient is about the same as that of evaluating the functional. The method has been applied to a marine line. Multiples turned out to be a problem, but were handled effectively by incorporating a multiple filter inside the DSO cost functional.

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Genç, S., and M. Mendeş. "Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 73, no. 6 (December 2021): 1391–402. http://dx.doi.org/10.1590/1678-4162-12413.

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ABSTRACT This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance-covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling.

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Wintersberger, Philipp, Frederica Janotta, Jakob Peintner, Andreas Löcken, and Andreas Riener. "Evaluating feedback requirements for trust calibration in automated vehicles." it - Information Technology 63, no. 2 (January 16, 2021): 111–22. http://dx.doi.org/10.1515/itit-2020-0024.

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Abstract The inappropriate use of automation as a result of trust issues is a major barrier for a broad market penetration of automated vehicles. Studies so far have shown that providing information about the vehicle’s actions and intentions can be used to calibrate trust and promote user acceptance. However, how such feedback could be designed optimally is still an open question. This article presents the results of two user studies. In the first study, we investigated subjective trust and user experience of (N=21) participants driving in a fully automated vehicle, which interacts with other traffic participants in virtual reality. The analysis of questionnaires and semi-structured interviews shows that participants request feedback about the vehicle’s status and intentions and prefer visual feedback over other modalities. Consequently, we conducted a second study to derive concrete requirements for future feedback systems. We showed (N=56) participants various videos of an automated vehicle from the ego perspective and asked them to select elements in the environment they want feedback about so that they would feel safe, trust the vehicle, and understand its actions. The results confirm a correlation between subjective user trust and feedback needs and highlight essential requirements for automatic feedback generation. The results of both experiments provide a scientific basis for designing more adaptive and personalized in-vehicle interfaces for automated driving.

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Slavuj, Vanja, Lucia Nacinovic Prskalo, and Marija Brkic Bakaric. "Automatic generation of language exercises based on a universal methodology: An analysis of possibilities." Bulletin of the Transilvania University of Brasov. Series IV: Philology and Cultural Studies 14 (63), no. 2 (January 2022): 29–48. http://dx.doi.org/10.31926/but.pcs.2021.63.14.2.3.

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The aim of the paper is to examine the possibilities for automatic generation of language learning exercises and compare them to those manually compiled by language instructors. The paper first presents a universal methodology applied in manually created exercises for learning the language for specific purposes, elaborated with examples in the field of academic English. Next, the automation of the procedure is explored through a series of steps which include creating the corpus, analysing each exercise type and the possibility of its automatic generation, automatically generating the exercise, and evaluating the end result. The results of the evaluation suggest that automatic generation of exercises can serve as a preliminary step of a two-stage process of exercises development in which each exercise, however, needs additional approval from the language expert.

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Albaqami, Hezam, Ghulam Mubashar Hassan, and Amitava Datta. "Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM." Sensors 23, no. 13 (June 27, 2023): 5960. http://dx.doi.org/10.3390/s23135960.

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Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.

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Ari, Tugba, Hande Sağlam, Hasan Öksüzoğlu, Orhan Kazan, İbrahim Şevki Bayrakdar, Suayip Burak Duman, Özer Çelik, et al. "Automatic Feature Segmentation in Dental Periapical Radiographs." Diagnostics 12, no. 12 (December 7, 2022): 3081. http://dx.doi.org/10.3390/diagnostics12123081.

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While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

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CRUVINEL, PAULO ESTEVÃO. "Low-cost microprocessed instrument for evaluating soil temperature profile." Pesquisa Agropecuária Brasileira 35, no. 11 (November 2000): 2269–75. http://dx.doi.org/10.1590/s0100-204x2000001100020.

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This paper describes a low-cost microprocessed instrument for in situ evaluating soil temperature profile ranging from -20.0°C to 99.9°C, and recording soil temperature data at eight depths from 2 to 128 cm. Of great importance in agriculture, soil temperature affects plant growth directly, and nutrient uptake as well as indirectly in soil water and gas flow, soil structure and nutrient availability. The developed instrument has potential applications in the soil science, when temperature monitoring is required. Results show that the instrument with its individual sensors guarantees ±0.25°C accuracy and 0.1°C resolution, making possible localized management changes within decision support systems. The instrument, based on complementary metal oxide semiconductor devices as well as thermocouples, operates in either automatic or non-automatic mode.

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Feng, Jing Ge, Ye Ping He, and Qiu Ming Tao. "Evaluation of Compilers’ Capability of Automatic Vectorization Based on Source Code Analysis." Scientific Programming 2021 (November 30, 2021): 1–15. http://dx.doi.org/10.1155/2021/3264624.

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Automatic vectorization is an important technique for compilers to improve the parallelism of programs. With the widespread usage of SIMD (Single Instruction Multiple Data) extensions in modern processors, automatic vectorization has become a hot topic in the research of compiler techniques. Accurately evaluating the effectiveness of automatic vectorization in typical compilers is quite valuable for compiler optimization and design. This paper evaluates the effectiveness of automatic vectorization, analyzes the limitation of automatic vectorization and the main causes, and improves the automatic vectorization technology. This paper firstly classifies the programs by two main factors: program characteristics and transformation methods. Then, it evaluates the effectiveness of automatic vectorization in three well-known compilers (GCC, LLVM, and ICC, including their multiple versions in recent 5 years) through TSVC (Test Suite for Vectorizing Compilers) benchmark. Furthermore, this paper analyzes the limitation of automatic vectorization based on source code analysis, and introduces the differences between academic research and engineering practice in automatic vectorization and the main causes, Finally, it gives some suggestions as to how to improve automatic vectorization capability.

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Yatsko, V. A., and T. N. Vishnyakov. "A method for evaluating modern systems of automatic text summarization." Automatic Documentation and Mathematical Linguistics 41, no. 3 (June 2007): 93–103. http://dx.doi.org/10.3103/s0005105507030041.

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Uzuner, O., Y. Luo, and P. Szolovits. "Evaluating the State-of-the-Art in Automatic De-identification." Journal of the American Medical Informatics Association 14, no. 5 (September 1, 2007): 550–63. http://dx.doi.org/10.1197/jamia.m2444.

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Bradbury, Jeremy S., and Juergen Dingel. "Evaluating and improving the automatic analysis of implicit invocation systems." ACM SIGSOFT Software Engineering Notes 28, no. 5 (September 2003): 78–87. http://dx.doi.org/10.1145/949952.940083.

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