Despite the increasing use of large language models (LLMs) in education, concerns have emerged about their potential to reduce deep thinking and active learning. This study investigates the impact of generative artificial intelligence (AI) tools, specifically ChatGPT, on the cognitive engagement of students during academic writing tasks. The study employed an experimental design with participants randomly assigned to either an AI-assisted (ChatGPT) or a non-assisted (control) condition. Participants completed a structured argumentative writing task followed by a cognitive engagement scale (CES), the CES-AI, developed to assess mental effort, attention, deep processing, and strategic thinking. The results revealed significantly lower cognitive engagement scores in the ChatGPT group compared to the control group. These findings suggest that AI assistance may lead to cognitive offloading. The study contributes to the growing body of literature on the psychological implications of AI in education and raises important questions about the integration of such tools into academic practice. It calls for pedagogical strategies that promote active, reflective engagement with AI-generated content to avoid compromising self-regulated learning and deep cognitive involvement of students.

While research on using Artificial Intelligence (AI) through various applications to enhance foreign language pronunciation is expanding, it has primarily focused on aspects such as comprehensibility and intelligibility, largely neglecting the improvement of individual speech sounds in both perception and production. This study seeks to address this gap by examining the impact of training with an AI-powered mobile application on nonnative sound perception and production. Participants completed a pretest assessing their ability to discriminate the second language English heed-hid contrast and produce these vowels in sentence contexts. The intervention involved training with the Speakometer mobile application, which incorporated recording tasks featuring the English vowels, along with pronunciation feedback and practice. The posttest mirrored the pretest to measure changes in performance. The results revealed significant improvements in both discrimination accuracy and production of the target contrast following the intervention. However, participants did not achieve native-like competence. These findings highlight the effectiveness of AI-powered applications in facilitating speech acquisition and support their potential use for personalized, interactive pronunciation training beyond the classroom.

This article contains spoilers for Star Trek: Section 31. When last we saw our Star Trek: Discovery antihero--Her Most Imperial Majesty, Mother of the Fatherland, Overlord of Vulcan, Dominus of Qo'noS, Regina Andor, Philippa Georgiou Augustus Iaponius Centarius--back in 2020, she had just come through a particularly rough stretch. Georgiou (if you're nasty, and she certainly is) had … well, for starters, she'd been dragged from the fascist "mirror" universe where she was queen into the "prime" one, and then catapulted 930 years into the future to stop an evil A.I. from wiping out all sentient life in the galaxy. Got that done, thankfully, though not without some sassy shenanigans--but all the travel turned out to be a bit taxing, on both Georgiou's mind and molecules, which were straining like a multiversal rubber band to return backward and across, causing weird flashbacks and a nasty case of the decorporealizing shivers. Luckily, a mysterious sentient hard drive known as "the Sphere" that had been hanging out on her ship, the mushroom-fueled USS Discovery, was able to help locate a solution: a stout little man dressed in tweed and a bowler hat named Carl who was also, ahem, the "Guardian of Forever."

The purpose of this work is to develop a framework to calibrate signed datasets so as to be consistent with specified marginals by suitably extending the Schr\"odinger-Fortet-Sinkhorn paradigm. Specifically, we seek to revise sign-indefinite multi-dimensional arrays in a way that the updated values agree with specified marginals. Our approach follows the rationale in Schr\"odinger's problem, aimed at updating a "prior" probability measure to agree with marginal distributions. The celebrated Sinkhorn's algorithm (established earlier by R.\ Fortet) that solves Schr\"odinger's problem found early applications in calibrating contingency tables in statistics and, more recently, multi-marginal problems in machine learning and optimal transport. Herein, we postulate a sign-indefinite prior in the form of a multi-dimensional array, and propose an optimization problem to suitably update this prior to ensure consistency with given marginals. The resulting algorithm generalizes the Sinkhorn algorithm in that it amounts to iterative scaling of the entries of the array along different coordinate directions. The scaling is multiplicative but also, in contrast to Sinkhorn, inverse-multiplicative depending on the sign of the entries. Our algorithm reduces to the classical Sinkhorn algorithm when the entries of the prior are positive.

Biological networks often encapsulate promotion/inhibition as signed edge-weights of a graph. Nodes may correspond to genes assigned expression levels (mass) of respective proteins. The promotion/inhibition nature of co-expression between nodes is encoded in the sign of the corresponding entry of a sign-indefinite adjacency matrix, though the strength of such co-expression (i.e., the precise value of edge weights) cannot typically be directly measured. Herein we address the inverse problem to determine network edge-weights based on a sign-indefinite adjacency and expression levels at the nodes. While our motivation originates in gene networks, the framework applies to networks where promotion/inhibition dictates a stationary mass distribution at the nodes. In order to identify suitable edge-weights we adopt a framework of ``negative probabilities,'' advocated by P.\ Dirac and R.\ Feynman, and we set up a likelihood formalism to obtain values for the sought edge-weights. The proposed optimization problem can be solved via a generalization of the well-known Sinkhorn algorithm; in our setting the Sinkhorn-type ``diagonal scalings'' are multiplicative or inverse-multiplicative, depending on the sign of the respective entries in the adjacency matrix, with value computed as the positive root of a quadratic polynomial.

Human behavior refers to the way humans act and interact in response to a stimulus, internal or external. Understanding human behavior through observational study is one of the core methodologies in fields such as psychology and sociology (Margolin, Oliver, Gordis, O'hearn, Medina, Ghosh and Morland, 1998). Human behaviors encompass rich information: from emotional expression, processing, and regulation to the intricate dynamics of interactions, including the context and knowledge of interlocutors and their thinking and problem-solving intent (Li, Baucom and Georgiou, 2020). Furthermore, the behavioral constructs of interest are often dependent on the domain of interaction (Narayanan and Georgiou, 2013). Hence characterization of human behavior usually requires domain-specific knowledge and adequate windows of observation. Notably, across psychological health science and practice (Bone, Lee, Chaspari, Gibson and Narayanan, 2017) such as couple therapy (Christensen, Atkins, Berns, Wheeler, Baucom and Simpson, 2004), suicide cognition evaluation (Bryan, Rudd, Wertenberger, Etienne, Ray-Sannerud, Morrow, Peterson and Young-McCaughon, 2014) and addiction counseling (Xiao, Imel, Georgiou, Atkins and Narayanan, 2015), this is exemplified in the definition and derivation of a variety of domain-specific behavior constructs (e.g., blame and affect patterns exhibited by partners, suicidal ideation of an individual at risk, and empathy expressed by a therapist in the respective aforementioned domains) to support specific subsequent plan of action. Human speech offers rich information about the mental state and traits of the talkers. Vocal cues, including speech and spoken language as well as nonverbal vocalizations and disfluency patterns, have been shown to be informationally relevant in the context of human behavior (e.g., in marital interaction (Baucom, Atkins, Simpson and Christensen, 2009), in motivational interviewing (Amrhein, Miller, Yahne, Palmer and Fulcher, 2003; Imel, Barco, Brown, Baucom, Baer, Kircher and Atkins, 2014; Miller, Benefield and Tonigan, 1993)). Many automatic computational approaches that support measurement, analysis, and modeling of human behaviors from speech have been investigated in affective computing (Lee and Narayanan, 2005), social signal processing (Vinciarelli, Pantic and Bourlard, 2009) and behavioral signal processing (BSP) (Narayanan and Georgiou, 2013).

We propose a probabilistic enhancement of standard {\em kernel Support Vector Machines} for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e.g., error bounds) may be available on each datum. In the present paper, we specifically consider Gaussian distributions to model uncertainty. Thereby, our data consist of pairs $(x_i,\Sigma_i)$, $i\in\{1,\ldots,N\}$, along with an indicator $y_i\in\{-1,1\}$ to declare membership in one of two categories for each pair. These pairs may be viewed to represent the mean and covariance, respectively, of random vectors $\xi_i$ taking values in a suitable linear space (typically ${\mathbb R}^n$). Thus, our setting may also be viewed as a modification of Support Vector Machines to classify distributions, albeit, at present, only Gaussian ones. We outline the formalism that allows computing suitable classifiers via a natural modification of the standard ``kernel trick.'' The main contribution of this work is to point out a suitable kernel function for applying Support Vector techniques to the setting of uncertain data for which a detailed uncertainty description is also available (herein, ``Gaussian points'').

The use of distances based on optimal transportation has recently shown promise for discrimination of power spectra. In particular, spectral estimation methods based on l1 regularization as well as covariance based methods can be shown to be robust with respect to such distances. These transportation distances provide a geometric framework where geodesics corresponds to smooth transition of spectral mass, and have been useful for tracking. In this paper, we investigate the use of these distances for automatic target recognition. We study the use of the Monge-Kantorovich distance compared to the standard l2 distance for classifying civilian vehicles based on SAR images. We use a version of the Monge-Kantorovich distance that applies also for the case where the spectra may have different total mass, and we formulate the optimization problem as a minimum flow problem that can be computed using efficient algorithms.

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.