A New Italian Cultural Heritage Data Set: Detecting Fake Reviews With BERT and ELECTRA Leveraging the Sentiment

Consiglio Nazionale delle Ricerche-CARI-CARE-ITALY’ within the CRUI CARE Agreemen

MeWEHV: Mel and Wave Embeddings for Human Voice Tasks

[EN] A recent trend in speech processing is the use of embeddings created through machine learning models trained on a specific task with large datasets. By leveraging the knowledge already acquired, these models can be reused in new tasks where the amount of available data is small. This paper proposes a pipeline to create a new model, called Mel and Wave Embeddings for Human Voice Tasks (MeWEHV), capable of generating robust embeddings for speech processing. MeWEHV combines the embeddings generated by a pre-trained raw audio waveform encoder model, and deep features extracted from Mel Frequency Cepstral Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate the performance of MeWEHV on three tasks: speaker, language, and accent identification. For the first one, we use the VoxCeleb1, and VBHIR datasets and present YouSpeakers204, a new and publicly available dataset for English speaker identification that contains 19607 audio clips from 204 persons speaking in six different accents, allowing other researchers to work with a very balanced dataset, and to create new models that are robust to multiple accents. For evaluating the language identification task, we use the VoxForge, Common Language, and the LRE17 datasets. Finally, for accent identification, we use the Latin American Spanish Corpora (LASC), Common Voice, and the NISP datasets. Our approach allows a significant increase in the performance of state-of-the-art embedding generation models on all the tested datasets, with a low additional computational cost.SIUnión EuropeaJunta de Castilla y León (EDU/875/2021)This work was supported in part by the European Union's Horizon 2020 Research and Innovation Framework Programme under the Global Response Against Child Exploitation (GRACE) Project under Grant 883341; and in part by the "Ayudas para financiar la contratacion predoctoral de personal investigador" grant of the Government of Castilla y Leon, Spain, under Grant EDU/875/202

Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples

Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations. Their eXplainable Artificial Intelligence (XAI) capabilities are typically measured in a two-dimensional space of explainability and accuracy ignoring the accountability aspects. During system evaluations, measures of comprehensibility, predictive accuracy and accountability remain inseparable. We propose an Accountable eXplainable Artificial Intelligence (AXAI) capability framework for facilitating separation and measurement of predictive accuracy, comprehensibility and accountability. The proposed framework, in its current form, allows assessing embedded levels of AXAI for delineating ML systems in a three-dimensional space. The AXAI framework quantifies comprehensibility in terms of the readiness of users to apply the acquired knowledge and assesses predictive accuracy in terms of the ratio of test and training data, training data size and the number of false-positive inferences. For establishing a chain of responsibility, accountability is measured in terms of the inspectability of input cues, data being processed and the output information. We demonstrate applying the framework for assessing the AXAI capabilities of three ML systems. The reported work provides bases for building AXAI capability frameworks for other genres of AI systems

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline