Translation and Validation of the Chinese ICD-11 International Trauma Questionnaire (ITQ) for the Assessment of Posttraumatic Stress Disorder (PTSD) and Complex PTSD (CPTSD)

Background: Two stress-related disorders have been proposed for inclusion in the revised ICD-11: Posttraumatic Stress Disorder (PTSD) and Complex PTSD (CPTSD). The International Trauma Questionnaire (ITQ) is a bespoke measure of PTSD and CPTSD and has been widely used in English-speaking countries. Objective: The primary aim of this study was to develop a Chinese version of the ITQ and assess its content, construct, and concurrent validity. Methods: Six mental health practitioners and experts rated the Chinese translated and back-translated items to assess content validity. A sample of 423 Chinese young adults completed the ITQ, the WHO Adverse Childhood Experiences International Questionnaire, and the Hospital Anxiety and Depression Scale. Among them, 31 participants also completed the English and Chinese versions of the ITQ administered in random order at retest. Four alternative confirmatory factor analysis models were tested using data from participants who reported at least one adverse childhood experience (ACE; N = 314). Results: The Chinese ITQ received excellent ratings on relevance and appropriateness. Test–retest reliability and semantic equivalence across English and Chinese versions were acceptable. The correlated first-order six-factor model and a second-order two-factor (PTSD and DSO) both provided an acceptable model fit. The six ITQ symptoms clusters were all significantly correlated with anxiety, depression, and the number of ACEs. Conclusions: The Chinese ITQ generates scores with acceptable psychometric properties and provides evidence for including PTSD and CPTSD as separate diagnoses in ICD-11

Perceived Environmental Supportiveness Scale: Portuguese Translation, Validation and Adaptation to the Physical Education Domain

Aim: Grounded on Self-Determination Theory, this study aimed to translate, adapt and validate the Perceived Environmental Supportiveness Scale (PESS) in a sample of Portuguese physical education students. Methods: The global sample was comprised of 964 students (518 females), divided in two groups: the calibration (n = 469) and the validation one (n = 483), all of them enrolled in two Physical Education (PE) classes/week. Results: The analysis provided support for a one factor and 12 items model, which are in line with the values adopted in the methodology (χ² = 196.123, df = 54, p = <.001, SRMR = .035, NNFI = .943, CFI = .954, RMSEA = .074, 90% CI .063-.085). Results express that the models are invariant in all analysis (i.e., calibration vs. validation, male vs. female,and 3rd vs. secondary cycle; three and single factor models). Conclusion: The present study suggests that the PESS with one factor and 12 items has good psychometric properties and can be used to assess perceived need supportive motivational environments provided by PE teachers. Additionally, invariance analysis showed support for the use of the scale in both genders and in the 3rd and secondary cycles.info:eu-repo/semantics/publishedVersio

Evaluating Value-Graph Translation Validation for LLVM

Translation validators are static analyzers that attempt to verify that program transformations preserve semantics. Normalizing trans- lation validators do so by trying to match the value-graphs of an original function and its transformed counterpart. In this paper, we present the design of such a validator for LLVM’s intra-procedural optimizations, a design that does not require any instrumentation of the optimizer, nor any rewriting of the source code to compile, and needs to run only once to validate a pipeline of optimizations. We present the results of our preliminary experiments on a set of bench- marks that include GCC, a perl interpreter, SQLite3, and other C programs.Engineering and Applied Science

Denotational Translation Validation

In this dissertation we present a simple and scalable system for validating the correctness of low-level program transformations. Proving that program transformations are correct is crucial to the development of security critical software tools. We achieve a simple and scalable design by compiling sequential low-level programs to synchronous data-flow programs. Theses data-flow programs are a denotation of the original programs, representing all of the relevant aspects of the program semantics. We then check that the two denotations are equivalent, which implies that the program transformation is semantics preserving. Our denotations are computed by means of symbolic analysis. In order to achieve our design, we have extended symbolic analysis to arbitrary control-flow graphs. To this end, we have designed an intermediate language called Synchronous Value Graphs (SVG), which is capable of representing our denotations for arbitrary control-flow graphs, we have built an algorithm for computing SVG from normal assembly language, and we have given a formal model of SVG which allows us to simplify and compare denotations. Finally, we report on our experiments with LLVM M.D., a prototype denotational translation validator for the LLVM optimization framework.Engineering and Applied Science

Enhancing Translation Validation of Compiler Transformations with Large Language Models

This paper presents a framework that integrates Large Language Models (LLMs) into translation validation, targeting LLVM compiler transformations where formal verification tools fall short. Our framework first utilizes existing formal verification tools for translation validation. In this work, we use Alive2, a well-known tool in LLVM compiler verification, as an example. When formal verification tools are unable to confirm a transformation's soundness, our framework employs fine-tuned LLMs for prediction. It then applies fuzzing to transformations predicted as potentially unsound by the LLMs due to return values or memory inconsistencies, aiming to find counterexamples. In cases where transformations are unsound for other reasons or sound, or if no counterexamples emerge, the framework directly reports these outcomes without further fuzzing. This methodology has shown effectiveness in complex application such as deep-learning accelerator designs, where traditional formal verification tools struggle.Comment: 6 page