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Cloud computing enables users to remove the necessity of the need of local hardware architecture, which removes the burden of the users from high computation costs. Therefore, it has attracted much attention and research has been conducted heavily on it. To protect users' privacy, data is usually encrypted prior to being sent to the cloud server. New cryptographic primitive such as public-key encryption with equality test (PKEET) has been introduced to make it more practical. In PKEET, users can test whether the underlying messages of two ciphertexts encrypted under different public keys are equal or not without the need to decrypt those ciphertexts. This is a very useful tool, especially for the cloud database, since PKEET mainly focuses on the equality test between two ciphertexts. However, in practice, the cloud server may need to verify the equivalence among more than two ciphertexts. This leads to disclosing unnecessary information of users and redundant computation cost will also occur when using traditional PKEET schemes. How to make this more efficient and practical remains an interesting research problem. In this paper, to solve the aforementioned problems by providing a novel concept of public-key encryption with multi-ciphertext equality test (PKE-MET).

Recently, Elhabob et al. proposed an identity-based encryption with authorized equivalence test (IBE-AET), which allows authorized testers to check whether messages in two ciphertexts are the same or not under the identity-based setting (Cluster Computing, 2020). This paper looks into the security and correctness of their IBE-AET construction. More specifically, contrary to the security claim in the original paper, we point out that their proposed scheme cannot achieve the indistinguishability under adaptively chosen identity and ciphertext attacks (IND-ID-CCA2) for adversaries who do not have authorization. In terms of the correctness of the scheme, we also identify that their type-4 authorization does not work correctly. Finally, we remedy their construction in order that it satisfies the IND-ID-CCA2 security with the rigorous security proof as well as the type-4 authorization works correctly.

To improve the performcance of community discovery algorithm applied to dynamic community detection objects, a parallel clustering analysis based on packet permission hierarchical association mining in community discovery of big data has been proposed. First, an evolutionary non-negative matrix decomposition framework based on clustering quality is proposed for dynamic community detection. Second, a clustering combined with dynamic pruning binary tree support vector machine (SVM) algorithm is proposed to prove the equivalence between evolutionary binary tree clustering and evolutionary module density optimization from the perspective of theoretical analysis. Based on this equivalence, a new semi-supervised association mining algorithm is proposed by adding prior information to the sample data without increasing the time complexity. Finally, through the experimental analysis on the static and dynamic community detection model, the performance advantage of the proposed algorithm on the community detection performance index is verified.

This research explores translation and interpreting quality assessment standards (TIQA), selecting those fit for the purpose of specialized translation quality assurance, with the aim to systematize them into a step-by-step framework, referred to as “the TIQA pyramid”, a framework that provides valid and reproducible benchmarks that are endowed with universal features and reflected in codes of ethics and professional standards. The TIQA standards may be subdivided into two major groups: text-oriented and ethical-deontological ones. Such classification is based on the notion of translation quality which is the projection of a translator (interpreter)’s personality (inchoate quality assurance arising out of a system of ethical and deontological precepts), or of textual requirements (choate quality assurance arising out of a system of text-oriented criteria). The “pas-de-trois” in a translated interaction among the commissioner of a specialized translation, its performer and end-user is grounded in the presumably existing mediated communication contract (typically a translation brief). Its positive upshot is manifested in the confidence-imbued multi-party polypragmatic interlingual and intercultural behaviour; the negative, however, is underscored by its implicit nature which leads to the absence of a concerted system of quality criteria, resulting in a lack of satisfaction and mutual trust.

This study employed electrophysiological measures to investigate the processes of expectancy fulfillment and semantic integration during the processing of binding and compound nouns in Chinese. Sequential expectancy and cloze probability between the two constituents of a two-character noun were manipulated in two experiments, resulting in four types of target characters (i.e., the final characters of two-character nouns): (i) high-cloze, binding character (HB); (ii) high-cloze, compound character (HC); (iii) low-cloze, binding character (LB); (iv) low-cloze, compound character (LC). Participants were asked to judge as quickly and accurately as possible whether the two characters, presented sequentially, formed a real word. The two experiments varied in stimulus onset asynchrony (SOA) between the initial and the target characters: 1000 ms and 300 ms. ERPs acquired at the target characters revealed a significant semantic integration effect in both experiments whereby low-cloze target characters elicited more negative-going activities than high-cloze target characters (L > H) with both long and short SOAs. Importantly, there was a graded N400 effect (LC = LB > HC > HB) at the central region: a clearly visible positive deflection for HB relative to HC in synchrony with an equivalence in negativity for LB and LC. This graded pattern was observed with an SOA of 1000 ms but not 300 ms. These results are discussed in terms of the possible coexistence of an expectancy fulfillment mechanism indexed by the P300 that monitors incoming information for realization of expectancies based on stored mental representations and a semantic integration mechanism indexed by the N400 that incorporates incoming information into the preceding context based on semantic-pragmatic knowledge. Manifestation of this coexistence appears sensitive to SOA-modulated attention orientation.

As data volume and complexity grow at an unprecedented rate, the performance of data manipulation programs is becoming a major concern for developers. In this article, we study how alternative API choices could improve data manipulation performance while preserving task-specific input/output equivalence. We propose a lightweight approach that leverages the comparative structures in Q&A sites to extracting alternative implementations. On a large dataset of Stack Overflow posts, our approach extracts 5,080 pairs of alternative implementations that invoke different data manipulation APIs to solve the same tasks, with an accuracy of 86%. Experiments show that for 15% of the extracted pairs, the faster implementation achieved >10x speedup over its slower alternative. We also characterize 68 recurring alternative API pairs from the extraction results to understand the type of APIs that can be used alternatively. To put these findings into practice, we implement a tool, AlterApi7 , to automatically optimize real-world data manipulation programs. In the 1,267 optimization attempts on the Kaggle dataset, 76% achieved desirable performance improvements with up to orders-of-magnitude speedup. Finally, we discuss notable challenges of using alternative APIs for optimizing data manipulation programs. We hope that our study offers a new perspective on API recommendation and automatic performance optimization.

This study explores translation task equivalence by comparing parallel translation test tasks in levels of difficulty, using eye-tracking, as well as NASA-TLX and retrospection. It also examines th...

Machine translation has the potential to make huge contributions to translation industries, but it seems, for now, that machine translation equivalence has led to a crucial point for literary translation by using machine translation because of the problem of the equivalence itself. This paper, therefore, aimed to see the equivalence degree of literary translation resulted by machine translation, i.e., freeware toolkit AntConc 3.5.0 software 2019. The data were collected from English-Indonesian J.K. Rowling's Harry Potter and The Order of The Phoenix novel by using the software to find the equivalent translation. The collected data were analyzed qualitatively based on the strategy of translation equivalent level proposed by Mona Baker (1992). The analyzed data revealed that the equivalent level of the software mostly occurred in a word level, above word level, and grammatical level. The software was likely difficult to find a textual level and a pragmatic level of translation equivalence because they required a context and still needed human involvement as part of a greater creative project of translation which were not done by the machine translation. After all, Antconc 3.5.0 as Computer Assisted Tool (CAT) brought a huge contribution to translation industry, helped to analyze large corpora particularly to find the degree of translation equivalence in word and above word level.

The performance of one computer relative to another is traditionally characterized through benchmarking, a practice occasionally deficient in statistical rigor. The performance is often trivialized through simplified measures, such as the approach of central tendency, but doing so risks a loss of perspective of the variability and non-determinism of modern computer systems. Authentic performance evaluations are derived from statistical methods that accurately interpret and assess data. Methods that currently exist within performance comparison frameworks are limited in efficacy, statistical inference is either overtly simplified or altogether avoided. A prevalent criticism from computer performance literature suggests that the results from difference hypothesis testing lack substance. To address this problem, we propose a new framework, SPARC, that pioneers a synthesis of difference and equivalence hypothesis testing to provide relevant conclusions. It is a union of three key components: (i) identifying either superiority or similarity through difference and equivalence hypotheses (ii) scalable methodology (based on the number of benchmarks), and (iii) a conditional feedback loop from test outcomes that produces informative conclusions of relevance, equivalence, trivial, or indeterminant. We present an experimental analysis characterizing the performance of a trio of RISC-V open-source processors to evaluate SPARC and its efficacy compared to similar frameworks.

The article describes the principles and experience of building brach thesauri designed to represent subject areas within the formal ontology of the unified knowledge space. Ttrms jf the thesauri include the words that are actually used for thematic indexing of documents — keywords and classification categories. The system of the thesaurus relations is established by a semi-automatic procedure and includes: equivalence (synonymy), generic hierarchy, association, and definitive relationship.

Studies examining the similarity of online self-report survey responses using different item formats have yielded inconclusive results. Additionally, no studies have used appropriate methods for thoroughly and correctly examining equivalence across conditions. We examined the comparability of survey responses across four item formats—horizontal radio button, text box, drop-down menu, and vertical radio button—in two studies. The second study added two response categories: optional responding and forced responding. Participants were college students at two institutions of higher education who were randomly assigned to conditions. They completed measures of computer self-efficacy, personality, and social desirability. Results of both studies indicated quantitative (mean scores) and qualitative (internal consistency estimates and scale intercorrelations) equivalence. However, there were notable differences in auxiliary equivalence such that participants in the text box condition had lower amounts of missing data than those in the other conditions, those in the horizontal radio button condition completed the study in the shortest amount of time, and participants across conditions generally preferred to use drop-down menus compared to other item formats.

Introduction. This paper (a) discusses the theoretical and methodological evidence for the equivalence of computerized and conventional versions of psychological tests, (b) analyzes the studies investigating psychometric parameters of computerized versions of conventional tests, and (c) examines contradictions in approaches to assessing the equivalence of the two test forms. This paper represents a first effort in structuring the main problems in establishing the equivalence of paper-and-pencil and computer-based testing, as well as in finding ways and means to overcome them. Much attention is devoted to minimal and sufficient mathematical and statistical tools for assessing the equivalence of the two test forms. Theoretical Basis. The main problems associated with assessing the equivalence of computerized and conventional tests include the following: (a) the level of cultural and informational competence of respondents, (b) anxiety, (c) social environment, (d) motivation for testing, (e) difficulties in creating the same conditions for paper-and-pencil and computer-based testing (L. N. Babanin, Y. P. Chua, M. Russell, P. Květon et al.). Researchers most often choose the following procedures for assessing the equivalence of the two versions of psychological tests: (a) comparison of the main statistical parameters (mean values, variances, etc.) and (b) assessment of construct validity and reliability of the computer-based versions of psychological tests. Results and Discussion. The analysis of research methodology for assessing the equivalence of computerized and conventional versions of psychological tests focuses on a variety of approaches to (a) the use of mathematical and statistical methods for assessing psychometric parameters of computer-based versions of conventional tests, (b) the choice of research design, (c) considering specific characteristics of the situation of computer-based testing. It is necessary to formulate specific and structured requirements for the procedure for assessing the equivalence of computerized and conventional versions of psychological tests. The author suggests recommendations for the main sections of such requirements related to (a) the procedure for conducting empirical research, (b) mathematical and statistical methods, and (c) control of the factors specific to computer-based testing that may have impact on the results of equivalence assessment.

Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R2 value of 0.78 and a constant error of (X ± SD) 0.8 ± 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland–Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.

The purpose of the article is to study problems of translation equivalence in machine translation, which is based on a sequence of invariable actions (algorithms) with a text to identify linguistic equivalents in a pair of languages at a given direction of translation by means of a computer, in respect of the pragmatic aspect. Translation equivalence is understood as a specific type of equivalence, which is fundamentally different from other types, since it does not correlate with the phenomena that have a special place in the structure of a language, but the phenomena that currently exist in a language correlation or are equivalent to the text content. The translation is formalized, but allows getting an idea of the text content at the introductory level, since it is not an accurate, adequate translation, but performs the function of rendering basic information. Machine translation is not able to render nuances of an original text, not only at the lexical level. When translating, it is necessary to take peculiarities of syntax and semantics into account. Adequate computer translation is almost impossible in this case. This fact is recognized by all scholars who study possibilities of this type of translation only when rendering main content of a document without taking language nuances and features into account. Machine translation can be carried out on a basis of the translation equivalence (objective and dynamic) model. The model in terms of linguistic technology provides an optimal solution of problems of independent linguistic description and algorithm. The system of translation equivalence, which can be implemented within the model of translation equivalence, allows providing sufficient quality of machine translation at the pre-editing stage. When creating a machine translation program, in addition to solving linguistic problems, a program of their implementation is also necessary, since a translation program is a tool for studying and finding information in a foreign language, and the prospects of a machine translation are related to the further development of translation theory and practice in general.

Purpose The purpose of this study was to verify the equivalence of 2 alternate test forms with nonoverlapping content generated by an item response theory (IRT)–based computer-adaptive test (CAT). The Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996)was utilized as an item bank in a prospective, independent sample of persons with aphasia. Method Two alternate CAT short forms of the PNT were administered to a sample of 25 persons with aphasia who were at least 6 months postonset and received no treatment for 2 weeks before or during the study. The 1st session included administration of a 30-item PNT-CAT, and the 2nd session, conducted approximately 2 weeks later, included a variable-length PNT-CAT that excluded items administered in the 1st session and terminated when the modeled precision of the ability estimate was equal to or greater than the value obtained in the 1st session. The ability estimates were analyzed in a Bayesian framework. Results The 2 test versions correlated highly ( r = .89) and obtained means and standard deviations that were not credibly different from one another. The correlation and error variance between the 2 test versions were well predicted by the IRT measurement model. Discussion The results suggest that IRT-based CAT alternate forms may be productively used in the assessment of anomia. IRT methods offer advantages for the efficient and sensitive measurement of change over time. Future work should consider the potential impact of differential item functioning due to person factors and intervention-specific effects, as well as expanding the item bank to maximize the clinical utility of the test. Supplemental Material https://doi.org/10.23641/asha.11368040

The present paper discusses the possibility of using parallel corpora in the study of phraseology. The results of the analysis support the assumption that the use of corpus data not only significantly improves cross-linguistic descriptions, but also changes the very idea of the specifics of phraseology as a subsystem of the lexicon. Parallel corpus data make it possible to identify the language-specific features of idioms that were traditionally considered completely equivalent. As the empirical bases, parallel subcorpora of the Russian National Corpus (RNC) are used: Russian-German and primarily German-Russian. In some cases, the material of the main corpus of the RNC is involved. In addition to the task of identifying similarities and differences between semantically similar idioms of German and Russian, the paper also addresses general issues, in particular the question of the boundaries of the concept of crosslinguistic equivalence and ways to operationalize it. At least two different types of equivalence have to be distinguished: equivalence at the level of the language system and equivalence at the text level. The proposed method includes a subsequent comparison of individual pairs of correlating idioms of different languages based on corpus data. It aims at identifying differences between them, including semantic, syntactic, pragmatic and combinatorial ones. The results of the performed analysis may find application in bilingual lexicography.

This is an entry for the publication titled "Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches". You can cite the paper as: Andrea Campagner, Federico Cabitza, Pedro Berjano, Davide Ciucci. Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches. Information Sciences, Volume 579, 2021, Pages 347-367. This entry contains both a pre-publication version of the paper, as well as the only non-previously available dataset used in the study. This dataset, in particular, contains observations for 325 patients, of which 124 have been annotated by two different clinicians. The legend of the labels is as follows: 0 Normal sagittal alignment 1 Cervical kyphosis 2 Thoracic kyphosis 3 Thoracolumbar kyphosis 4 Lumbar kyphosis 5 Lower lumbar kyphosis 6 Global kyphosis 7 Pelvic kyphosis While the features are the following: PI: pelvic incidence PT: pelvic tilt SS: sacral slope LL: lumbar lordosis LLL: lower lumbar lordosis TLK: thoraco-lumbar kyphosis TK: thoracic kyphosis SVA: sagittal vertical axis T1PA: T1 pelvic angle sex: 0=female; 1=male type of X-ray: 0=EOS; 1=traditional X-ray&nbsp

Due to the gradual improvement of computing power and the development of big data, the error rate of voice recognition systems and voice assistance functions gradually decreases with the gradual application of deep learning. In terms of commercial contracts, it is a legally binding text. This article first analyzes the theory of functional equivalence translation and then analyzes the theory of functional equivalence translation from the perspective of professionalism and accuracy and determines the principles of business contract English vocabulary translation. At the same time, it studies English in terms of vocabulary equivalence. In the process of teaching trade English, some common vocabulary in trade English is easy to be misunderstood. This article analyzes these common translation errors and their main causes and proposes some appropriate solutions. Business English vocabulary translation must follow the principle of pragmatic equivalence, and the translation must be flexible to deal with communication obstacles caused by cultural background, language habits, and other factors, because they are subject to two different language and cultural characteristic rules (English and Chinese). The seasonal autoregressive summed moving average (ARIMA) model is used to simulate the time series of the surface water temperature in the coastal waters, and the structure of the model is determined through the principle of irrelevance and simplicity. On this basis, the optimal ARIMA prediction model is established and the surface water temperature of the coastal waters is monthly. The prediction results are consistent with the monthly change trend of the sea surface temperature, indicating that the model is suitable for predicting the offshore surface temperature.

Function block diagrams (FBD) are widely used for implementation of programmable logic control (PLC) in safety critical domains and in the conventional factory automation. With the growing software intensity of such systems, the size and complexity of the PLC FBD applications is growing. The implicit execution order of PLC FBD can be ambiguous for developers, causing misinterpretation of the control programs behaviour. This work aims at reducing this ambiguity, investigating re-implementation of FBDs in a new programming language of IEC 61499, which has explicit mechanism for defining the execution order. A method is proposed for generation of IEC 61499 FBDs from the PLC FBDs. We also present a tool that implements our approach and which is complemented with an automated tester to prove the equivalence in the behaviour of the source and generated systems.

A biosimilar is a biological product that is highly similar to and has no clinically meaningful differences from an approved reference product. Focusing on two-arm randomized clinical trials that aim to establish the equivalence between a test biosimilar product and the reference product, we propose the elastic meta-analytic-predictive (EMAP) prior method to leverage rich historical data available on the reference product to improve the power of the biosimilar trials. We first extract the prior information from multiple historical studies through meta-analysis, and then we discount the resulting meta-analytic-predictive (MAP) prior adaptively according to the congruence between the historical reference data and the trial reference arm data via an elastic function. The EMAP prior method is information-borrowing consistent in that asymptotically it achieves full information borrowing when trial reference arm data are congruent to historical reference data, and no information borrowing when trial reference arm data are not congruent to historical reference data. As a result, the method asymptotically controls the type I error rate at the nominal value. Extensive simulation studies show that the EMAP prior outperforms the robust MAP prior. The EMAP prior generates comparable or higher power and provides better-controlled type I errors. We illustrate the proposed methodology using two trial examples.

Equivalence checking techniques help establish whether two versions of a program exhibit the same behavior. The majority of popular techniques for formally proving/refuting equivalence are evaluated on small and simplistic benchmarks, omitting "difficult" programming constructs, such as non-linear arithmetic, loops, floating-point arithmetic, and string and array manipulation. This hinders efficient evaluation of these techniques and the ability to establish their practical applicability in real scenarios. This paper addresses this gap by contributing EqBench – the largest and most comprehensive benchmark for equivalence checking analysis, which contains 147 equivalent and 125 non-equivalent cases, in both C and Java languages. We believe EqBench can facilitate a more realistic evaluation of equivalence checking techniques, assessing their individual strength and weaknesses. EqBench is publicly available at: https://osf.io/93s5b/.

Semantic Textual Similarity (STS) and paraphrase de- tection are two NLP tasks that have a high focus on the meaning of sentences, and current research in both re- lies heavily on comparing fragments of text. Little to no work has been done in studying inference-centric ap- proaches to solve these tasks. We study the relation be- tween existing work and what we call mutual implica- tion (MI), a binary relationship between two sentences that holds when they textually entail each other. MI thus shifts the focus of STS and paraphrase detection to un- derstanding the meaning of a sentence in terms of its in- ferential properties. We study the comparison between MI, paraphrasing, and STS work. We then argue that MI should be considered a complementary evaluation met- ric for advancing work in areas as diverse as machine translation, natural language inference, etc. Finally, we study the limitations of MI and discuss possibilities for overcoming them.

This objective of the research is to classify the types of translation technique from English to Indonesian subtitle, to determine the most dominant type of translation technique of English to Indonesian subtitle of “Bhaijan Bajrangi” movie. The study of this research used the descriptive qualitative method to describe the subject or the object of the research based on the fact or reality. The data of this research are utterances or sentences in the movie script, which involves two different languages, both the SL (English) and the TL (Indonesian). The source data are “Bhaijan Bajrangi” movie. The data were collected by using the documentation method in the movie. The type of translation techniques used are: 1) Literal (137 data or 28.84 %), 2) Modulation (86 data or 18.11 %), 3) Reduction/Omission (83 data or 17.47 %), 4) Established Equivalence (49 data or 10.32 %), 5) Amplification/Addition (42 data or 8.84 %), 6) Transposition/Shift (33 data or 6.95 %), 7) Borrowing (20 data or 4.21 %), 8) Particularization (12 data or 2.53 %), 9) Generalization (6 data or 1.26 %), 10) Adaptation (5 data or 1.05 %), and 11) Description (2 data or 0.42 %).

Background: Questionnaires are forward-translated and then back-translated by human translators during adaptation of a survey questionnaire in the local language. The machine translation method is now available free from “Google Translate” website and it can be used for translation from English to Bengali and vice versa. Using machine translation method may save researchers from investing money and time in translation by human experts. Aim: The aim of this pilot study was to test the feasibility of “Google Translate” in forward-translation and back-translation during adaptation of a survey questionnaire from English to Bengali. Materials and Methods: A survey questionnaire, originally developed in English, was forward-translated to Bengali and then back-translated to English by machine translation method and manual translation method. Both the versions were checked for errors expressed in subjective sentence error rate (SSER). Back-translated English questionnaire obtained from machine translation and manual translation was checked for equivalence with the original questionnaire. Results: SSER for forward-translated Bengali questionnaire by machine translation method was 66.60 ± 4.04 and manual translation method was 117.20 ± 1.30 (P < 0.001). SSER for back-translated English questionnaire by machine translation method was 61.20 ± 5.02 and manual translation method was 116.20 ± 2.77 (P < 0.001). Equivalence between back-translated questionnaire and original questionnaire by machine translation method was 48.40 ± 6.02 and by manual translation method was 112.40 ± 3.29 (P < 0.001). Conclusion: The machine-translated questionnaire showed higher error than manually translated questionnaire. Machine-translated questionnaire also showed lower equivalence with the original questionnaire. Hence, the use of machine translation (from English to Bengali) offered by “Google Translate” should be used with caution.

In this article we will review theory of comparison which is a very crucial when decisions have to be done. We will present Minkowski distance-based operator, non-metric-based pseudo similarity based operator and combined t-norm and t-conorm operators. We will study these measures for comparison vs. to the measures that are used generally for comparison that are min, max, Euclidean distance and exponent. Practical part of this article show that presented comparison operators work well in classification example and in expert system example. As a classification example we will use classification done for Image Segmentation data and expert system example is about defining athlete’s aerobic and anaerobic thresholds. Classification results are better than decision tree-, KNN- and SVM-classifiers give for Image Segmentation. Classification accuracy given by Shweizer & Sklar - Łukasiewicz - equivalence was 89.70%, while best result from the classifiers selected for comparison was given by decision medium tree classifier with 72.00% accuracy. In expert system our comparison measure-based method gives similar estimations as given by sport medicine experts.

Appropriate discipline and decorum maintenance is the essential requirement on the online platform with the growth of technology. Many uncivilized threat and degrading messages are sent by the unauthorized users in the electronic mails and mobile messages. The process of exact identification and removal of toxic messages from the social media is critical and still remains a challenging issue. With this background, this paper provides the following contribution towards predicting the toxic comment classification. Firstly, the Jigsaw toxic comment classification dataset extracted from KAGGLE repository is subjected with the data pre-processing. Secondly, the distribution of the toxic behaviour classes is identified and depicted as pictorial representations. Thirdly, TFIDF is used for segregating the most likely features of the dataset with the methodology of both unigrams and bigrams. Fourth, the removed Counter vectorized features from the toxic comment classification dataset is fitted to various classifiers and the efficiency is analysed for binary equivalence, Classifier chain and Label power setclassification. Fifth, the extracted TFID vectorized features from the toxic comment classification dataset is fitted to various classifiers and the efficiency is analysed for binary equivalence, Classifier chain and Label power setclassification. Sixth, the extracted Hash vectorized features from the toxic comment classification dataset is fitted to various classifiers and the efficiency is analysed for binary equivalence, Classifier chain and Label power setclassification. The efficiency analysis is done with the use of evaluation metrics of the classification. Experimental results shows that Kernel SVM classifier have the 87% accuracy with the binary equivalence classification for all the tokenization methods. Random Forest classifier have the accuracy of 88% with the classifier chain and 87% with label power set classification for all the tokenization methods.

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

Commonly used rating scales and tests have been found lacking reliability and validity, for example in neurodegenerative diseases studies, owing to not making recourse to the inherent ordinality of human responses, nor acknowledging the separability of person ability and item difficulty parameters according to the well-known Rasch model. Here, we adopt an information theory approach, particularly extending deployment of the classic Brillouin entropy expression when explaining the difficulty of recalling non-verbal sequences in memory tests (i.e., Corsi Block Test and Digit Span Test): a more ordered task, of less entropy, will generally be easier to perform. Construct specification equations (CSEs) as a part of a methodological development, with entropy-based variables dominating, are found experimentally to explain (r =R2 = 0.98) and predict the construct of task difficulty for short-term memory tests using data from the NeuroMET (n = 88) and Gothenburg MCI (n = 257) studies. We propose entropy-based equivalence criteria, whereby different tasks (in the form of items) from different tests can be combined, enabling new memory tests to be formed by choosing a bespoke selection of items, leading to more efficient testing, improved reliability (reduced uncertainties) and validity. This provides opportunities for more practical and accurate measurement in clinical practice, research and trials.

Objectives The EuroQol Group is developing a new EQ-5D-Y-5L version with 5 severity levels for each of the 5 dimensions. The 5 severity levels describe different health severities and there is a potential for severity level inversion. This article aims to report the process of cross-cultural adaptation of the beta EQ-5D-Y-5L into Chichewa (Malawi) using the card ranking exercise, which has been added to the EQ-5D-Y-5L translation protocol. Methods To assess the correct hierarchical ordering of severity levels, the adaptation followed the EQ-5D-Y-5L translation protocol. Cognitive interviews were undertaken to establish conceptual equivalence. Thereafter, 4 iterations of ranking exercises were conducted, leading to amendments of the translated Chichewa version to arrive at a final version. Results The iterations were assessed by 18 participants aged 8 to 14 years. Health proved to be a difficult concept to translate as was “discomfort.” Cognitive interviews identified further conceptual issues, particularly with the “looking after myself” dimension. Considerations about lack of soap or water indicated that some children did not fully comprehend this dimension as being about the ability to wash and dress themselves. The iterative card ranking exercise detected severity level inversion between “a little bit” and “some,” and between “a lot” and “extreme” and alternative Chichewa words/phrases were then tested. Ultimately, the intended hierarchical severity ranking was achieved and an acceptable Chichewa version was produced. Conclusions Conceptual and linguistic equivalence to the English EQ-5D-Y-5L was established for the Chichewa EQ-5D-Y-5L version. The card ranking exercise was instrumental in correcting severity level inversion and supporting the comprehensible translation.

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.

We propose three linguistically motivated metrics to quantify syntactic equivalence between a source sentence and its translation. Firstly, syntactically aware cross (SACr) measures the degree of word group reordering by creating syntactically motivated groups of words that are aligned. Secondly, an intuitive approach is to compare the linguistic labels of the word-aligned source and target tokens. Finally, on a deeper linguistic level, aligned syntactic tree edit distance (ASTrED) compares the dependency structure of both sentences. To be able to compare source and target dependency labels, we make use of Universal Dependencies (UD). We provide an analysis of our metrics by comparing them with translation process data in mixed models. Even though our examples and analysis focus on English as the source language and Dutch as the target language, the proposed metrics can be applied to any language for which UD models are attainable. An open-source implementation is made available.

Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval. To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. We first establish an equivalence between the tasks of question summarization and Recognizing Question Entailment (RQE) using their definitions in the medical domain. Based on this equivalence, we propose a data augmentation algorithm to use just one dataset to optimize for both tasks, with a weighted MTL loss. We introduce gradually soft parameter-sharing: a constraint for decoder parameters to be close, that is gradually loosened as we move to the highest layer. We show through ablation studies that our proposed novelties improve performance. Our method outperforms existing MTL methods across 4 datasets of medical question pairs, in ROUGE scores, RQE accuracy and human evaluation. Finally, we show that our method fares better than single-task learning under 4 low-resource settings.

This study compares two techniques of reconstruction and representation of the anterior and posterior surfaces of the human cornea, both for healthy and for keratoconus eyes, in a CAD software environment using raw tomographic data, by two different methods: a mesh model (obtained from a grid of points) and a NURBS surface model. After reconstruction, a morpho-geometric analysis was made, and several parameters were defined and measured in a set of 100 healthy eyes and 61 keratoconus eyes, detecting the statistical analysis significant differences between methods for both groups. Mesh method proved to be more accurate, but less eyes could be modelled. Volumetric and surface measurement values showed a total equivalence between both methods, so these would be the ones to use when modelling eyes for keratoconus detection and characterization purposes.

Code completion is one of the main features of modern Integrated Development Environments (IDEs). Its objective is to speed up code writing by predicting the next code token(s) the developer is likely to write. Research in this area has substantially bolstered the predictive performance of these techniques. However, the support to developers is still limited to the prediction of the next few tokens to type. In this work, we take a step further in this direction by presenting a large-scale empirical study aimed at exploring the capabilities of state-of-the-art deep learning (DL) models in supporting code completion at different granularity levels, including single tokens, one or multiple entire statements, up to entire code blocks (e.g., the iterated block of a for loop). To this aim, we train and test several adapted variants of the recently proposed RoBERTa model, and evaluate its predictions from several perspectives, including: (i) metrics usually adopted when assessing DL generative models (i.e., BLEU score and Levenshtein distance); (ii) the percentage of perfect predictions (i.e., the predicted code snippets that match those written by developers); and (iii) the "semantic" equivalence of the generated code as compared to the one written by developers. The achieved results show that BERT models represent a viable solution for code completion, with perfect predictions ranging from ~7%, obtained when asking the model to guess entire blocks, up to ~58%, reached in the simpler scenario of few tokens masked from the same code statement., Comment: Accepted to the 18th International Conference on Mining Software Repositories (MSR 2021)

Any modification to a validated assay must be evaluated in terms of the impact on the assay's performance characteristics and whether the assay remains fit for the intended purpose. The comparison is referred to as a 'method comparison', 'method comparability', 'method change', or 'comparative validation'. This review presents recommendations and examples of studies found in the current literature as a means of assessing minor modifications. In addition, the authors discuss common statistical approaches used for these comparisons.

In the two decades of continuous development of genomic selection, a great variety of models have been proposed to make predictions from the information available in dense marker panels. Besides deciding which particular model to use, practitioners also need to make many minor choices for those parameters in the model which are not typically estimated by the data (so called “hyper-parameters”). When the focus is placed on predictions, most of these decisions are made in a direction sought to optimize predictive accuracy. Here we discuss and illustrate using publicly available crop datasets the use of cross validation to make many such decisions. In particular, we emphasize the importance of paired comparisons to achieve high power in the comparison between candidate models, as well as the need to define notions of relevance in the difference between their performances. Regarding the latter, we borrow the idea of equivalence margins from clinical research and introduce new statistical tests. We conclude that most hyper-parameters can be learnt from the data by either minimizing REML or by using weakly-informative priors, with good predictive results. In particular, the default options in a popular software are generally competitive with the optimal values. With regard to the performance assessments themselves, we conclude that the paired k-fold cross validation is a generally applicable and statistically powerful methodology to assess differences in model accuracies. Coupled with the definition of equivalence margins based on expected genetic gain, it becomes a useful tool for breeders.

Problem equivalence discrimination aims to compare two problems and determine whether they are semantically equivalent, it is a binary classification problem. This is a crucial task for QA. This paper studies the problem equivalence discrimination in the field of Chinese social security, proposes a question equivalence discrimination system based on Bert + Focal-Loss, and constructs the question data set in the field of social security. It solves the problem of imbalance between positive and negative samples, so as to input more negative samples for pre training, improve the accuracy of the system, construct the question data set of social security field according to a certain annotation proportion, fill in the lack of data set in professional field, and expand the data set according to the users' wishes.

The gold standard for measuring anhedonia is the Snaith-Hamilton Pleasure Scale (SHAPS). To date, there are no validated electronic versions of this questionnaire. We aim to study the equivalence between the traditional paper-and-pencil format and a digital version of the SHAPS. A group of 67 patients completed both SHAPS formats, and differences between formats were assessed. McNemar’s test showed no significant differences between the two systems. The Kappa coefficient was over 40% for most items, and reliability was above 0.8, showing good to excellent levels of internal consistency. Thus, we have demonstrated a close equivalence between paper-and-pencil and electronic SHAPS.

International audience; Preference elicitation is an important component in many AI applications, including decision support and recommender systems. Such systems must assess user preferences, based on interactions with their users, and make recommendations using (possibly incomplete and imprecise) beliefs about those preferences. Mechanisms for explicit preference elicitation—asking users to answer direct queries about their preferences—can be of great value; but due to the cognitive and time cost imposed on users, it is important to minimize the number of queries by asking those that have high (expected) value of information.An alternative approach is to simply make recommendations and have users provide feedback (e.g., accept a recommendation or critique it in some way) and use this more indirect feedback to gradually improve the quality of the recommendations. Due to inherent uncertainty about a user's true preferences, often a set of recommendations is presented to the user at each stage. Conceptually, a set of recommendations can also be viewed as choice query, in which the user indicates which option is most preferred from that set.Because of the potential tension between making a good set recommendation and asking an informative choice query, we explore the connection between the two. We consider two different models of preference uncertainty and optimization: (a) a Bayesian framework in which a posterior over user utility functions is maintained, optimal recommendations are assessed using expected utility, and queries are assessed using expected value of information; and (b) a minimax-regret framework in which user utility uncertainty is strict (represented by a polytope), recommendations are made using the minimax-regret robustness criterion, and queries are assessed using worst-case regret reduction. We show that, somewhat surprisingly, in both cases, there is no tradeoff to be made between good recommendations and good queries: we prove that the optimal recommendation set of size k is also an optimal choice query of size k. We also examine the case where user responses to choice queries are error prone (using both constant and mixed multinomial logit noise models) showing the results are robust to this form of noise.In both frameworks, our theoretical results have practical consequences for the design of interactive recommenders. Our results also allow us to design efficient algorithms to compute optimal query/recommendation sets. We develop several such algorithms (both exact and approximate) for both settings and provide empirical validation of their performance.

We develop a new approach for building cryptographic implementations. Our approach goes the last mile and delivers assembly code that is provably functionally correct, protected against side-channels, and as efficient as handwritten assembly. We illustrate our approach using ChaCha20Poly1305, one of the two ciphersuites recommended in TLS 1.3, and deliver formally verified vectorized implementations which outperform the fastest non-verified code.We realize our approach by combining the Jasmin framework, which offers in a single language features of high-level and low-level programming, and the EasyCrypt proof assistant, which offers a versatile verification infrastructure that supports proofs of functional correctness and equivalence checking. Neither of these tools had been used for functional correctness before. Taken together, these infrastructures empower programmers to develop efficient and verified implementations by "game hopping", starting from reference implementations that are proved functionally correct against a specification, and gradually introducing program optimizations that are proved correct by equivalence checking.We also make several contributions of independent interest, including a new and extensible verified compiler for Jasmin, with a richer memory model and support for vectorized instructions, and a new embedding of Jasmin in EasyCrypt., This work is partially supported by project ONR N00014-19-1-2292. Manuel Barbosa was supported by grant SFRH/BSAB/143018/2018 awarded by FCT. This work was partially funded by national funds via FCT in the context of project PTDC/CCI-INF/31698/2017.

There are two main objectives of this paper. One is to review the current Thurstonian models for the conventional and modified triangle and duo‐trio methods. Another objective is to develop Thurstonian models for, and assess the performances of the paired triangle and paired duo‐trio methods with AB and BA pairs. Bradley's models based on the noncentral F distribution for the triangle and duo‐trio methods are revisited and are used to derive the psychometric functions for the paired versions of the triangle and duo‐trio methods. The simulation‐derived psychometric functions for the new methods are also produced. The performances of the new methods in both difference and similarity/equivalence tests are explored and show that the new methods are more powerful than most of the conventional unspecified forced‐choice methods and are similar to the unspecified tetrad. PRACTICAL APPLICATIONS: The triangle and the duo‐trio methods are popular sensory discrimination methods used widely in sensory and consumer research. One weakness of the methods is relatively lower testing power. The paired triangle and paired duo‐trio methods proposed and explored in this paper overcome this weakness of the conventional methods. The paired versions of the methods are more powerful than most of the conventional unspecified discrimination methods in difference and similarity tests for products. The new methods are particularly suitable for assessment of before–after treatment effects of products. The methods have wide potential application for visual and manual inspection testing of food or non‐food products where less sensory fatigue is of concern. R codes were developed and provided in the paper for the Thurstonian models of both the conventional versions and the paired versions of the triangle and the duo‐trio methods.