STUDENTS’ PERCEPTIONS OF THE TEACHERS’ TEACHING CONCEPTIONS: A CASE OF ENGLISH TEACHER TRAINING AND EDUCATION PROGRAM

This study reported students’ perceptions of the teacher’s teaching conceptions in an English Teacher Training and Education Program in Indonesia. The teaching conceptions were based on Freeman and Richardson’s (1993) three types of teaching conceptions: scientifically based, theory-and-values based, and art or craft based conceptions. The study involved 218 participants from three different batches (2008, 2009, and 2010). Obtaining the data from the questionnaires and interviews, this study found that the three batches shared the same perceptions of several items in each teaching conception. In the science/research conception, they agreed on the teachers’ applying learning principles and using an effective classroom practice model. As for the theory/philosophy conception, they similarly perceived the teachers’ incorporating values into teaching, conveying communicative approach, referring to theories, giving moral values through teaching, presenting themselves as a role model in morality, and presenting their teaching rationally. Their perceptions were also confirmed by the interview revealing that the students tended to see their teachers’ teaching conceptions as theory/philosophy based. Regarding the art/craft conception, they similarly noted the teachers’ helping them to understand how to develop their ability and skills as future teachers. The analysis using a one-way analysis of variance (ANOVA) further confirmed that the three batches were not significantly different with P = .658. They expected the teachers to hold art/craft based teaching conception because they believed that understanding them as learners was the key for successful learning.Keywords:  students’ perceptions, teachers’ teaching conceptions, scientifically based, theory-and-values based, and art or craft based conceptions

Statistics-aware Audio-visual Deepfake Detector

peer reviewedIn this paper, we propose an enhanced audio-visual deep detection method. Recent methods in audio-visual deepfake detection mostly assess the synchronization between audio and visual features. Although they have shown promising results, they are based on the maximization/minimization of isolated feature distances without considering feature statistics. Moreover, they rely on cumbersome deep learning architectures and are heavily dependent on empirically fixed hyperparameters. Herein, to overcome these limitations, we propose: (1) a statistical feature loss to enhance the discrimination capability of the model, instead of relying solely on feature distances; (2) using the waveform for describing the audio as a replacement of frequency-based representations; (3) a post-processing normalization of the fakeness score; (4) the use of shallower network for reducing the computational complexity. Experiments on the DFDC and FakeAVCeleb datasets demonstrate the relevance of the proposed method.U-AGR-7133 - BRIDGES2021/IS/16353350/FakeDeTeR_Post - AOUADA Djamil

Targeted Augmented Data for Audio Deepfake Detection

peer reviewedThe availability of highly convincing audio deepfake generators highlights the need for designing robust audio deepfake detectors. Existing works often rely solely on real and fake data available in the training set, which may lead to overfitting, thereby reducing the robustness to unseen manipulations. To enhance the generalization capabilities of audio deepfake detectors, we propose a novel augmentation method for generating audio pseudo-fakes targeting the decision boundary of the model. Inspired by adversarial attacks, we perturb original real data to synthesize pseudo-fakes with ambiguous prediction probabilities. Comprehensive experiments on two well-known architectures demonstrate that the proposed augmentation contributes to improving the generalization capabilities of these architectures

Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies

peer reviewedExisting methods on audio-visual deepfake detection mainly focus on high-level features for modeling inconsistencies between audio and visual data. As a result, these approaches usually overlook finer audio-visual artifacts, which are inherent to deepfakes. Herein, we propose the introduction of fine-grained mechanisms for detecting subtle artifacts in both spatial and temporal domains. First, we introduce a local audio-visual model capable of capturing small spatial regions that are prone to inconsistencies with audio. For that purpose, a fine-grained mechanism based on a spatially-local distance coupled with an attention module is adopted. Second, we introduce a temporally-local pseudo-fake augmentation to include samples incorporating subtle temporal inconsistencies in our training set. Experiments on the DFDC and the FakeAVCeleb datasets demonstrate the superiority of the proposed method in terms of generalization as compared to the state-of-the-art under both in-dataset and cross-dataset settings.U-AGR-7133 - BRIDGES2021/IS/16353350/FakeDeTeR_Post - AOUADA Djamil

Development and characterization of an in vitro model of colorectal adenocarcinoma with MDR phenotype

The major cause of failure in cancer chemotherapy is the development of multidrug resistance (MDR), and the characterization of biological factors involved in this response to therapy is particularly needed. A doxorubicin‐resistant HCT‐8/R clone was selected from sensitive parental cells and characterized analyzing several parameters (cell cycle phase distribution, apoptotic activity, expression, distribution and functionality of the P‐gp efflux pump, the response to other chemotherapy agents, its ultrastructural features, invasiveness, and transcriptomic profile). HCT‐8/R cells showed a peculiar S phase distribution, characterized by a single pulse of proliferation, resistance to drug‐mediated apoptosis, increased expression and functionality of P‐gp and overexpression of stem cell markers (CD44 and aldehyde dehydrogenase 1A2). At the ultrastructural level, HCT‐8/R presented a greater cell volume and several intracytoplasmic vesicles respect to HCT‐8. Moreover, the resistant clone was characterized by cross resistance to other cytotoxic drugs and a greater capacity for migration and invasion, compared to parental cells. Our data reinforce the concept that the MDR phenotype in HCT‐8/R cells is multifactorial and involves multiple mechanisms, representing an interesting tool to understand the biological basis of MDR and to test strategies that overcome resistance to chemotherapy

Interaction of the Psychiatric Risk Gene Cacna1c With Post-weaning Social Isolation or Environmental Enrichment Does Not Affect Brain Mitochondrial Bioenergetics in Rats

The pathophysiology of neuropsychiatric disorders involves complex interactions between genetic and environmental risk factors. Confirmed by several genome-wide association studies, Cacna1c represents one of the most robustly replicated psychiatric risk genes. Besides genetic predispositions, environmental stress such as childhood maltreatment also contributes to enhanced disease vulnerability. Both, Cacna1c gene variants and stressful life events are associated with morphological alterations in the prefrontal cortex and the hippocampus. Emerging evidence suggests impaired mitochondrial bioenergetics as a possible underlying mechanism of these regional brain abnormalities. In the present study, we simulated the interaction of psychiatric disease-relevant genetic and environmental factors in rodents to investigate their potential effect on brain mitochondrial function using a constitutive heterozygous Cacna1c rat model in combination with a four-week exposure to either post-weaning social isolation, standard housing, or social and physical environmental enrichment. Mitochondria were isolated from the prefrontal cortex and the hippocampus to evaluate their bioenergetics, membrane potential, reactive oxygen species production, and respiratory chain complex protein levels. None of these parameters were considerably affected in this particular gene-environment setting. These negative results were very robust in all tested conditions demonstrating that Cacna1c depletion did not significantly translate into altered bioenergetic characteristics. Thus, further investigations are required to determine the disease-related effects on brain mitochondria

LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection

editorial reviewedThis paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detection are mainly based on a supervised binary classifier coupled with an implicit attention mechanism. As a result, they do not generalize well to unseen manipulations. To handle this issue, two main contributions are made. First, an explicit attention mechanism within a multi-task learning framework is proposed. By combining heatmap-based and self-consistency attention strategies, LAA-Net is forced to focus on a few small artifact-prone vulnerable regions. Second, an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and effective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy. Experiments performed on several benchmarks show the superiority of our approach in terms of Area Under the Curve (AUC) and Average Precision (AP). The code is available at https://github. com/10Ring/LAA-Net