Correction: Effect of the "Art Coloring" Online Coloring Game on Subjective Well-Being Increase and Anxiety Reduction During the COVID-19 Pandemic: Development and Evaluation.

[This corrects the article DOI: 10.2196/37026.].

A Lightweight Framework for Perception Analysis Based on Multimodal Cognition-Aware Computing.

The VUCA environment challenged neuropsychological research conducted in conventional laboratories. Researchers expected to perform complex multimodal testing tasks in natural, open, and non-laboratory settings. However, for most neuropsychological scientists, the independent construction of a multimodal laboratory in a VUCA environment, such as a construction site, was a significant and comprehensive technological challenge. This study presents a generalized lightweight framework for perception analysis based on multimodal cognition-aware computing, which provided practical updated strategies and technological guidelines for neuromanagement and automation. A real-life test experiment on a construction site was provided to illustrate the feasibility and superiority of the method. The study aimed to fill a technology gap in the application of multimodal physiological and neuropsychological techniques in an open VUCA environment. Meanwhile, it enabled the researchers to improve their systematic technological capabilities and reduce the threshold and trial-and-error costs of experiments to conform to the new trend of VUCA.

Effect of the "Art Coloring" Online Coloring Game on Subjective Well-Being Increase and Anxiety Reduction During the COVID-19 Pandemic: Development and Evaluation.

BACKGROUND: COVID-19 has spread worldwide and generated tremendous stress on human beings. Unfortunately, it is often hard for distressed individuals to access mental health services under conditions of restricted movement or even lockdown. OBJECTIVE: The study first aims to develop an online digital intervention package based on a commercially released coloring game. The second aim is to test the effectiveness of difference intervention packages for players to increase subjective well-being (SWB) and reduce anxiety during the pandemic. METHODS: An evidence-based coloring intervention package was developed and uploaded to an online coloring game covering almost 1.5 million players worldwide in January 2021. Players worldwide participated to color either 4 rounds of images characterized by awe, pink, nature, and blue or 4 rounds of irrelevant images. Participants' SWB and anxiety and the perceived effectiveness of the game in reducing anxiety (subjective effectiveness [SE]) were assessed 1 week before the intervention (T1), after the participants completed pictures in each round (T2-T5), and after the intervention (T6). Independent 2-tailed t tests were conducted to examine the general intervention (GI) effect and the intervention effect of each round. Univariate analysis was used to examine whether these outcome variables were influenced by the number of rounds completed. RESULTS: In total, 1390 players worldwide responded and completed at least 1 assessment. Overall, the GI group showed a statistical significantly greater increase in SWB than the general control (GC) group (N=164, t162=3.59, Cohen d=0.59, 95% CI 0.36-1.24, P<.001). Compared to the control group, the best effectiveness of the intervention group was seen in the awe round, in which the increase in SWB was significant (N=171, t169=2.51, Cohen d=0.39, 95% CI 0.10-0.82, P=.01), and players who colored all 4 pictures had nearly significant improvements in SWB (N=171, F4,170=2.34, partial ŋ2=0.053, P=.06) and a significant decrease in anxiety (N=171, F4,170=3.39, partial ŋ2=0.075, P=.01). CONCLUSIONS: These data indicate the effectiveness of online psychological interventions, such as coloring games, for mental health in the specific period. They also show the feasibility of applying existing commercial games embedded with scientific psychological interventions that can fill the gap in mental crises and services for a wider group of people during the pandemic. The results would inspire innovations to prevent the psychological problems caused by public emergencies and encourage more games, especially the most popular ones, to take more positive action for the common crises of humankind.

Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer Vision.

As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects' activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant's blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.

Cost-sensitive learning for emotion robust speaker recognition.

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Modeling and computing of stock index forecasting based on neural network and Markov chain.

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

Simplified process model discovery based on role-oriented genetic mining.

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

Vocal emotion of humanoid robots: a study from brain mechanism.

Driven by rapid ongoing advances in humanoid robot, increasing attention has been shifted into the issue of emotion intelligence of AI robots to facilitate the communication between man-machines and human beings, especially for the vocal emotion in interactive system of future humanoid robots. This paper explored the brain mechanism of vocal emotion by studying previous researches and developed an experiment to observe the brain response by fMRI, to analyze vocal emotion of human beings. Findings in this paper provided a new approach to design and evaluate the vocal emotion of humanoid robots based on brain mechanism of human beings.

Information spread of emergency events: path searching on social networks.

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.