This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices.

This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return.

Intelligent cognitive assistant (ICA) technology is used in various domains to emulate human behavior expressed through synchronous communication, especially written conversation. Due to their ability to use individually tailored natural language, they present a powerful vessel to support attitude and behavior change. Behavior change support systems are emerging as a crucial tool in digital mental health services, and ICAs exceed in effective support, especially for stress, anxiety and depression (SAD), where ICAs guide people’s thought processes and actions by analyzing their affective and cognitive phenomena. Currently, there is no comprehensive review of such ICAs from a technical standpoint, and existing work is conducted exclusively from a psychological or medical perspective. This technical state-of-the-art review tried to discern and systematize current technological approaches and trends as well as detail the highly interdisciplinary landscape of intersections between ICAs, attitude and behavior change, and mental health, focusing on text-based ICAs for SAD. Ten papers with systems, fitting our criteria, were selected. The systems varied significantly in their approaches, with the most successful opting for comprehensive user models, classification-based assessment, personalized intervention, and dialogue tree conversational models.

The increasing trend of mental health issues, especially among younger people, is not a new phenomenon. World organizations, leaders, and decision-makers are recognizing its devastating effect, resulting in mental health well-being appearing in goal 3 of the 17 UN sustainable development goals (SDGs) [1] . Among mental health issues, stress, anxiety, and depression (SAD) seem to be on the forefront—reaching 74% for disabling stress [2] , 28% for anxiety disorder [3] , and 48% for depression [4] , in some groups. What is more, between 76% and 85% of people in low- and middle-income countries receive no treatment for their disorder [5] , whereas the treatment coverage for, for example, depression is only 33% in high-income countries [6] . Mental health issues have large, multifaceted effects on the patient, on their immediate surroundings (family or caretakers), and on the wider society [7] . Individuals face decreased quality of life, poor educational outcomes, lowered productivity and potential poverty, social problems, abuse vulnerabilities, and additional health problems. Caretakers face increased emotional and physical challenges as well as decreased household income and increased financial costs. Society faces the loss of several gross domestic product (GDP) percentage points and billions of dollars annually, alongside with exacerbating public health issues and corrosion of social cohesion. All these lead to an increasingly stronger positive reinforcement loop—SAD increasingly perpetuates SAD. Too often, mental health issues directly result in the worst possible outcome and loss of human life, as many countries struggle with a high suicide rate [8] . It has been recognized that the reasons for increasing SAD include a severe lack of mental health professionals and regulations [9] as well as unequal access to mental health care [10] . These factors make the field ripe for technological and other scientific therapy-based interventions, as individuals with mental health issues prefer therapies to medication [11] .

To further extend the applicability of wearable sensors, methods for accurately extracting subtle psychological information from the sensor data are required. However, accessing subjective information in everyday life, such as cognitive load, remains challenging. To bring consensus on methods for cognitive load monitoring, a machine learning challenge is organized. The participants developed machine learning methods for cognitive load classification using wrist-worn physiological sensors' data, namely heart rate, R-R intervals, skin conductance, and skin temperature. The data from subjects solving cognitive tasks of varying difficulty is used for the challenge. This article presents a systematic comparison and multi-strategic performance evaluation of the thirteen methods submitted to this challenge. A systematic comparison of preprocessing techniques, classification algorithms, and implementation techniques is presented. Performance variations for different task difficulty levels, different subjects, and different experiment periods are evaluated. The results indicate that the most robust methods used multimodal sensor data, classical classification approaches such as decision trees and support vector machines or their ensembles, and Bayesian hyperparameter optimization for hyperparameter tuning. The most accurate models used handcrafted features that are further selected using sequential backward floating search and evaluated using stratified person-aware cross-validation strategy. Moreover, the results indicated better classification performance for specific test subjects, the tasks with the highest difficulty, and in some cases, the time elapsed since the start of the experiment. This dependency is likely due to model overfitting or due to the subjective nature of the psychophysiological process. The intersubject variability in responses is challenging to be captured through objective binary labels for cognitive load, thereby warranting more sophisticated annotation approaches.