Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective

Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life settings due to data scarcity. Few publicly available DD datasets exist and the creation of new datasets is hindered by the conceptual distinction between low-stakes and high-stakes lies. Theoretically, the two kinds of lies are so distinct that a dataset of one kind could not be used for applications for the other kind. Even though it is easier to acquire data on low-stakes deception since it can be simulated (faked) in controlled settings, these lies do not hold the same significance or depth as genuine high-stakes lies, which are much harder to obtain and hold the practical interest of automated DD systems. To investigate whether this distinction holds true from a practical perspective, we design several experiments comparing a high-stakes DD dataset and a low-stakes DD dataset evaluating their results on a Deep Learning classifier working exclusively from video data. In our experiments, a network trained in low-stakes lies had better accuracy classifying high-stakes deception than low-stakes, although using low-stakes lies as an augmentation strategy for the high-stakes dataset decreased its accuracy.Comment: 11 pages, 3 figure

ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition. The goal of the competition was to automatically evaluate five “apparent” personality traits (the so-called “Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the final phase. Despite the difficulty of the task, the teams made great advances in this round of the challenge

Sustainable Human Resource Management

The concept of sustainability is important for companies both in the case of SMEs and worldwide multinational companies. Some key factors to help a company achieve its sustainability objectives are based on human resource management. Sustainable human resource management is a typical cross-functional task that becomes increasingly important at the strategic level of a company. Industry 4.0 technologies, Internet of Things, and competitive demands, as signs of globalization, have led to significant changes across the organizational structures and human resource strategies of companies. The increasing importance of sophisticated human resource strategies in the life of companies and the intention to find optimal design and operation strategies for sustainable human resource management were a motivation for launching this book. This book offers a selection of papers which explain the impact of smart human resource management on economy. Authors from 14 countries published working examples and case studies resulting from their research in this field. The aim of this book is to help students at the level of BSc, MSc, and PhD level, as well as managers and researchers, to understand and appreciate the concept, design, and implementation of sustainable human resource management solutions

Towards personalized medicine in psychosis: the roles of social cognition and metacognition

[eng] People with psychosis experience a range of symptoms and impairments that significantly impact their lives and often concur with disability. The best predictors of functional outcome are social cognition and metacognition, which are often impaired in psychosis. Interventions to improve both domains are effective, but this efficacy does not always translate into better functioning. Delivering early, and targeted social cognitive or metacognitive interventions to patients with psychosis could be instrumental in preventing poor functional outcome and preventing relapse, but the grounds on how to personalize these interventions are unknown. Although it has been suggested that the approach should take sex differences, the refining of measurement instruments and the use of sophisticated statistical models, these have not been explored yet. Under this rationale, the present doctoral dissertation aims to: 1) validate a test of facial emotion recognition (Baron Cohen’s Face Test) in healthy population, with the aim of detecting whether it is an appropriate tool to use in clinical research, 2) detect whether patients with first episode psychosis have different, clinically meaningful profiles of performance in social cognition and metacognition, 3) explore the sociodemographic, clinical, and neurocognitive characteristics of each profile, 4) examine if males and females with first episode psychosis are similar in their heterogeneity in social cognition and metacognition, 5) explore the role of social cognition and sex in functional outcome in people with established psychosis (schizophrenia). This broad aim yielded four research articles. The main findings of this doctoral dissertation are a) Baron Cohen’s Face Test is an adequate and reliable instrument to measure facial emotion recognition in Spanish population but it presents a ceiling effect, b) People with first-episode psychosis have distinct profiles of social cognition and metacognition that have specific clinical and neurocognitive correlates. Having worse social cognition is associated with worse clinical presentation, even if metacognition is preserved, c) Men and women with first-episode psychosis have similar configurations of social cognition and metacognition. However, there are sex- specific profiles that should be considered when delivering treatment. Sex-specific profiles seem to be associated with more severity of the disorder than the common profiles. These results suggest that people with psychosis can receive social cognitive or metacognitive targeted treatment as early as after the first episode, but these should be chosen considering the profile of each individual and their biological sex. Thus, patients with psychosis should always be carefully assessed for social cognitive and metacognitive performance.[spa] Las personas con psicosis experimentan una serie de síntomas y déficits que afectan significativamente a sus vidas y que a menudo concurren con la discapacidad. Los mejores predictores de funcionamiento son la cognición social y la metacognición, que a menudo presentan deterioro en personas con psicosis. Diversas intervenciones para mejorar ambos dominios son eficaces, pero esto no siempre se traduce en un mejor funcionamiento. Para ello, se ha propuesto que intervenciones en cognición social y metacognición tempranas y dirigidas podrían maximizar su efecto sobre el funcionamiento y la prevención de recaídas. No obstante, se desconocen los fundamentos que debería guiar su personalización. Aunque se ha sugerido que el enfoque debería tener en cuenta las diferencias de sexo, el perfeccionamiento de los instrumentos de medida y el uso de modelos estadísticos sofisticados, éstos aún no se han explorado en la literatura. Bajo este razonamiento, la presente tesis doctoral pretende: 1) validar una prueba de reconocimiento facial de emociones (Test de Caras de Baron Cohen) en población sana, con el objetivo de detectar si es un instrumento adecuado para utilizar en la investigación clínica, 2) detectar si los pacientes con primer episodio de psicosis tienen perfiles diferentes y clínicamente significativos de rendimiento en cognición social y metacognición, 3) explorar las características sociodemográficas, clínicas y neurocognitivas de cada perfil, 4) examinar si los hombres y las mujeres con primer episodio psicótico son similares en su heterogeneidad en la cognición social y la metacognición, 5) explorar el papel de la cognición social y el sexo en el resultado funcional en personas con psicosis establecida (esquizofrenia). Este amplio objetivo dio lugar a cuatro artículos de investigación. Los principales hallazgos de esta tesis doctoral son: a) El Test de Caras de Baron Cohen es un instrumento adecuado y fiable para medir el reconocimiento de emociones faciales en población española, pero presenta un efecto techo, b) Las personas con primer episodio psicótico tienen perfiles distintos de cognición social y metacognición, con correlatos clínicos y neurocognitivos específicos asociados. Tener una peor cognición social se asocia con una peor presentación clínica, incluso si la metacognición está preservada, c) Los hombres y las mujeres con primer episodio psicótico tienen configuraciones similares de cognición social y metacognición. Sin embargo, existen perfiles específicos de cada sexo que deben tenerse en cuenta a la hora de aplicar el tratamiento, ya que éstos parecen estar asociados a una mayor gravedad del trastorno que los perfiles comunes. Estos resultados sugieren que las personas con psicosis pueden recibir tratamiento en cognición social o metacognición específico desde el primer episodio psicótico, pero éste debe elegirse teniendo en cuenta el perfil de cada individuo y su sexo biológico. Para ello, se pone de manifiesto la necesidad de una correcta evaluación de sus habilidades cognitivo-sociales y metacognitivas

Selective de-identification of ECGs, The

Includes bibliographical references.2022 Fall.Biometrics are often used for immigration control, business applications, civil identity, and healthcare. Biometrics can also be used for authentication, monitoring (e.g., subtle changes in biometrics may have health implications), and personalized medical concerns. Increased use of biometrics creates identity vulnerability through the exposure of personal identifiable information (PII). Hence an increasing need to not only validate but secure a patient's biometric data and identity. The latter is achieved by anonymization, or de-identification, of the PII. Using Python in collaboration with the PTB-XL ECG database from Physionet, the goal of this thesis is to create "selective de-identification." When dealing with data and de-identification, clusters, or groupings, of data with similarity of content and location in feature space are created. Classes are groupings of data with content matching that of a class definition within a given tolerance and are assigned metadata. Clusters start without derived information, i.e., metadata, that is created by intelligent algorithms, and are thus considered unstructured. Clusters are then assigned to pre-defined classes based on the features they exhibit. The goal is to focus on features that identify pathology without compromising PII. Methods to classify different pathologies are explored, and the effect on PII classification is measured. The classification scheme with the highest "gain," or (improvement in pathology classification)/ (improvement in PII classification), is deemed the preferred approach. Importantly, the process outlined can be used in many other systems involving patient recordings and diagnostic-relevant data collection

THE INTERPLAY BETWEEN PRIVACY AND FAIRNESS IN LEARNING AND DECISION MAKING PROBLEMS

The availability of large datasets and computational resources has driven significant progress in Artificial Intelligence (AI) and, especially,Machine Learning (ML). These advances have rendered AI systems instrumental for many decision making and policy operations involving individuals: they include assistance in legal decisions, lending, and hiring, as well determinations of resources and benefits, all of which have profound social and economic impacts. While data-driven systems have been successful in an increasing number of tasks, the use of rich datasets, combined with the adoption of black-box algorithms, has sparked concerns about how these systems operate. How much information these systems leak about the individuals whose data is used as input and how they handle biases and fairness issues are two of these critical concerns. While some people argue that privacy and fairness are in alignment, the majority instead believe these are two contrasting metrics. This thesis firstly studies the interaction between privacy and fairness in machine learning and decision problems. It focuses on the scenario when fairness and privacy are at odds and investigates different factors that can explain for such behaviors. It then proposes effective and efficient mitigation solutions to improve fairness under privacy constraints. In the second part, it analyzes the connection between fairness and other machine learning concepts such as model compression and adversarial robustness. Finally, it introduces a novel privacy concept and an initial implementation to protect such proposed users privacy at inference time

CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models