LEARNING ONE UNIVERSAL MACHINE LEARNING MODEL FOR WI-FI UNDER DIVERSE DEVICES AND ENVIRONMENTS

Techniques are provided for associating similar devices and environments together so they can be effectively learned. Furthermore, a new device (e.g., smartphone) can be associated quickly with behaviors of other similar observed smartphones to avoid learning from scratch. Since wireless performance depends strongly on device and environments types, any machine learning method also needs to be conditioned on device and environment types

Kicking Prejudice: Large Language Models for Racism Classification in Soccer Discourse on Social Media

In the dynamic space of Twitter, now called X, interpersonal racism surfaces when individuals from dominant racial groups engage in behaviours that diminish and harm individuals from other racial groups. It can be manifested in various forms, including pejorative name-calling, racial slurs, stereotyping, and microaggressions. The consequences of racist speech on social media are profound, perpetuating social division, reinforcing systemic inequalities, and undermining community cohesion. In the specific context of football discourse, instances of racism and hate crimes are well-documented. Regrettably, this issue has seamlessly migrated to the football discourse on social media platforms, especially Twitter. The debate on Internet freedom and social media moderation intensifies, balancing the right to freedom of expression against the imperative to protect individuals and groups from harm. In this paper, we address the challenge of detecting racism on Twitter in the context of football by using Large Language Models (LLMs). We fine-tuned different BERT-based model architectures to classify racist content in the Twitter discourse surrounding the UEFA European Football Championships. The study aims to contribute insights into the nuanced language of hate speech in soccer discussions on Twitter while underscoring the necessity for context-sensitive model training and evaluation. Additionally, Explainable Artificial Intelligence (XAI) techniques, specifically the Integrated Gradient method, are used to enhance transparency and interpretability in the decision-making processes of the LLMs, offering a comprehensive approach to mitigating racism and offensive language in online sports discourses

Conocimiento pedagógico: explorando nuevas aproximaciones

Resumen La formación inicial docente se ha transformado en un nudo crítico para las políticas educativas. Uno de los elementos que muestra importantes deficiencias es la desarticulación del conocimiento pedagógico dentro del proceso de aprender a enseñar. En esta reflexión se propone una reconceptualización del conocimiento pedagógico a la luz de cuatro elementos centrales que debiera tenerse en consideración: el rol de las creencias sobre la enseñanza y aprendizaje en la formación docente inicial; la importancia del conocimiento pedagógico del contenido como un eje articulador en el proceso de formación; la vinculación entre teoría y práctica para contribuir en el desarrollo de un conocimiento de la enseñanza pertinente a los contextos en donde se implementará; y el desarrollo de prácticas reflexivas como una estrategia para articular y confrontar los anteriores elementos en la formación de los futuros profesores. El abordaje de estos tópicos contribuirá no sólo con profundizar temas escasamente abordados en el campo de la formación docente inicial, sino también con desarrollar una mirada más comprensiva de los procesos de enseñanza y aprendizaje que debiesen sustentar los programas de formación docente inicial. Se discutirán las implicancias de esta propuesta en los dispositivos de enseñanza-aprendizaje que promueve la formación inicial de profesores y en las posibilidades de innovación que se podrían implementar

Non-invasive diagnostic tests for Helicobacter pylori infection

BACKGROUND: Helicobacter pylori (H pylori) infection has been implicated in a number of malignancies and non-malignant conditions including peptic ulcers, non-ulcer dyspepsia, recurrent peptic ulcer bleeding, unexplained iron deficiency anaemia, idiopathic thrombocytopaenia purpura, and colorectal adenomas. The confirmatory diagnosis of H pylori is by endoscopic biopsy, followed by histopathological examination using haemotoxylin and eosin (H & E) stain or special stains such as Giemsa stain and Warthin-Starry stain. Special stains are more accurate than H & E stain. There is significant uncertainty about the diagnostic accuracy of non-invasive tests for diagnosis of H pylori. OBJECTIVES: To compare the diagnostic accuracy of urea breath test, serology, and stool antigen test, used alone or in combination, for diagnosis of H pylori infection in symptomatic and asymptomatic people, so that eradication therapy for H pylori can be started. SEARCH METHODS: We searched MEDLINE, Embase, the Science Citation Index and the National Institute for Health Research Health Technology Assessment Database on 4 March 2016. We screened references in the included studies to identify additional studies. We also conducted citation searches of relevant studies, most recently on 4 December 2016. We did not restrict studies by language or publication status, or whether data were collected prospectively or retrospectively. SELECTION CRITERIA: We included diagnostic accuracy studies that evaluated at least one of the index tests (urea breath test using isotopes such as13C or14C, serology and stool antigen test) against the reference standard (histopathological examination using H & E stain, special stains or immunohistochemical stain) in people suspected of having H pylori infection. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the references to identify relevant studies and independently extracted data. We assessed the methodological quality of studies using the QUADAS-2 tool. We performed meta-analysis by using the hierarchical summary receiver operating characteristic (HSROC) model to estimate and compare SROC curves. Where appropriate, we used bivariate or univariate logistic regression models to estimate summary sensitivities and specificities. MAIN RESULTS: We included 101 studies involving 11,003 participants, of which 5839 participants (53.1%) had H pylori infection. The prevalence of H pylori infection in the studies ranged from 15.2% to 94.7%, with a median prevalence of 53.7% (interquartile range 42.0% to 66.5%). Most of the studies (57%) included participants with dyspepsia and 53 studies excluded participants who recently had proton pump inhibitors or antibiotics.There was at least an unclear risk of bias or unclear applicability concern for each study.Of the 101 studies, 15 compared the accuracy of two index tests and two studies compared the accuracy of three index tests. Thirty-four studies (4242 participants) evaluated serology; 29 studies (2988 participants) evaluated stool antigen test; 34 studies (3139 participants) evaluated urea breath test-13C; 21 studies (1810 participants) evaluated urea breath test-14C; and two studies (127 participants) evaluated urea breath test but did not report the isotope used. The thresholds used to define test positivity and the staining techniques used for histopathological examination (reference standard) varied between studies. Due to sparse data for each threshold reported, it was not possible to identify the best threshold for each test.Using data from 99 studies in an indirect test comparison, there was statistical evidence of a difference in diagnostic accuracy between urea breath test-13C, urea breath test-14C, serology and stool antigen test (P = 0.024). The diagnostic odds ratios for urea breath test-13C, urea breath test-14C, serology, and stool antigen test were 153 (95% confidence interval (CI) 73.7 to 316), 105 (95% CI 74.0 to 150), 47.4 (95% CI 25.5 to 88.1) and 45.1 (95% CI 24.2 to 84.1). The sensitivity (95% CI) estimated at a fixed specificity of 0.90 (median from studies across the four tests), was 0.94 (95% CI 0.89 to 0.97) for urea breath test-13C, 0.92 (95% CI 0.89 to 0.94) for urea breath test-14C, 0.84 (95% CI 0.74 to 0.91) for serology, and 0.83 (95% CI 0.73 to 0.90) for stool antigen test. This implies that on average, given a specificity of 0.90 and prevalence of 53.7% (median specificity and prevalence in the studies), out of 1000 people tested for H pylori infection, there will be 46 false positives (people without H pylori infection who will be diagnosed as having H pylori infection). In this hypothetical cohort, urea breath test-13C, urea breath test-14C, serology, and stool antigen test will give 30 (95% CI 15 to 58), 42 (95% CI 30 to 58), 86 (95% CI 50 to 140), and 89 (95% CI 52 to 146) false negatives respectively (people with H pylori infection for whom the diagnosis of H pylori will be missed).Direct comparisons were based on few head-to-head studies. The ratios of diagnostic odds ratios (DORs) were 0.68 (95% CI 0.12 to 3.70; P = 0.56) for urea breath test-13C versus serology (seven studies), and 0.88 (95% CI 0.14 to 5.56; P = 0.84) for urea breath test-13C versus stool antigen test (seven studies). The 95% CIs of these estimates overlap with those of the ratios of DORs from the indirect comparison. Data were limited or unavailable for meta-analysis of other direct comparisons. AUTHORS' CONCLUSIONS: In people without a history of gastrectomy and those who have not recently had antibiotics or proton ,pump inhibitors, urea breath tests had high diagnostic accuracy while serology and stool antigen tests were less accurate for diagnosis of Helicobacter pylori infection.This is based on an indirect test comparison (with potential for bias due to confounding), as evidence from direct comparisons was limited or unavailable. The thresholds used for these tests were highly variable and we were unable to identify specific thresholds that might be useful in clinical practice.We need further comparative studies of high methodological quality to obtain more reliable evidence of relative accuracy between the tests. Such studies should be conducted prospectively in a representative spectrum of participants and clearly reported to ensure low risk of bias. Most importantly, studies should prespecify and clearly report thresholds used, and should avoid inappropriate exclusions

Complexity, Education and Elements of Joy and Optimal Motivation

This presentation was given during the American Educational Research Association Annual Meeting

Pemodelan logika pemeriksaan temporal linier dengan buchi automata=Logic checking linear temporal with buchi automata

Correctness and reliability is two keywords successibilites of design both hardware and software systems. To get correct and reliability systems, it must be tight checking and testing overall components. Once of better checking method is modeling system behavioral, called Model Checking, this method possibly check system components part by part, so reliability of its overall components could be guarantee. The model checking mechanism is formulating system behavioral in logic sound and prepositional logic. Automata is an algebra model contents states, also transition between states. An automata model that accepting infinite states sequins transition or transition through final state infinitely often, is Buchi Automaton. Reactive systems characterized with its continuous reaction with environment and non-terminating. If checking does for behavioral system time to time, then the process in system considering like temporal habit, model which is satisfying this behavioral called Model Temporal Logic. If checking does for time to time as linear, then model called Model Linear Temporal Logic (MLTL) on topple M = (S, R, Label) . MLTL constructed using graph exploration and Buchi automaton then MLTL can be stated in an automata on topple Aw = (E, S, S° , F, p, Label) . This mechanism called Logic Checking Linear Temporal Modeling with Buchi Automata. Keywords: Modeling, Checking, Reactive system, Temporal Logic, Automaton