A wearable group-synchronized EEG system for multi-subject brain-computer interfaces.

Objective: The multi-subject brain-computer interface (mBCI) is becoming a key tool for the analysis of group behaviors. It is necessary to adopt a neural recording system for collaborative brain signal acquisition, which is usually in the form of a fixed wire. Approach: In this study, we designed a wireless group-synchronized neural recording system that supports real-time mBCI and event-related potential (ERP) analysis. This system uses a wireless synchronizer to broadcast events to multiple wearable EEG amplifiers. The simultaneously received broadcast signals are marked in data packets to achieve real-time event correlation analysis of multiple targets in a group. Main results: To evaluate the performance of the proposed real-time group-synchronized neural recording system, we conducted collaborative signal sampling on 10 wireless mBCI devices. The average signal correlation reached 99.8%, the amplitude of average noise was 0.87 µV, and the average common mode rejection ratio (CMRR) reached 109.02 dB. The minimum synchronization error is 237 µs. We also tested the system in real-time processing of the steady-state visual-evoked potential (SSVEP) ranging from 8 to 15.8 Hz. Under 40 target stimulators, with 2 s data length, the average information transfer rate (ITR) reached 150 ± 20 bits/min, and the highest reached 260 bits/min, which was comparable to the marketing leading EEG system (the average: 150 ± 15 bits/min; the highest: 280 bits/min). The accuracy of target recognition in 2 s was 98%, similar to that of the Synamps2 (99%), but a higher signal-to-noise ratio (SNR) of 5.08 dB was achieved. We designed a group EEG cognitive experiment; to verify, this system can be used in noisy settings. Significance: The evaluation results revealed that the proposed real-time group-synchronized neural recording system is a high-performance tool for real-time mBCI research. It is an enabler for a wide range of future applications in collaborative intelligence, cognitive neurology, and rehabilitation.

A Human Digital-Twin-Based Framework Driving Human Centricity towards Industry 5.0.

This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory's digital twin, which instead does not adequately consider the human component. The overall goal of the authors is to provide a reference architecture to manufacturing companies for a digital-twin-based platform that promotes harmonization and orchestration between humans and (physical and virtual) machines through the monitoring, simulation, and optimization of their interactions. In addition, the platform enhances the interactions of the stakeholders with the digital twin, considering that the latter cannot always be fully autonomous, and it can require human intervention. The paper also presents an implemented scenario adhering to the proposed framework's specifications, which is also validated with a real case study set in a factory plant that produces wooden furniture, thus demonstrating the validity of the overall proposed approach.

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.

Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic.

During the coronavirus disease 2019 (COVID-19) pandemic, it is difficult for travelers to choose suitable nature-based leisure travel destinations because many factors are related to health risks and are highly uncertain. This research proposes a type-II fuzzy approach with explainable artificial intelligence to overcome this difficulty. First, an innovative type-II alpha-cut operations fuzzy collaborative intelligence method was used to derive the fuzzy priorities of factors critical for nature-based leisure travel destination selection. Subsequently, a type-II fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje method, which is also novel, was employed to evaluate and compare the overall performance of nature-based leisure travel destinations. Furthermore, several measures were taken to enhance the explainability of the selection process and result. The effectiveness of the proposed type-II fuzzy approach was evaluated in a regional experiment conducted in Taichung City, Taiwan, during the COVID-19 pandemic.

A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant Transformation.

In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker's fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.

BBNet: A Novel Convolutional Neural Network Structure in Edge-Cloud Collaborative Inference.

Edge-cloud collaborative inference can significantly reduce the delay of a deep neural network (DNN) by dividing the network between mobile edge and cloud. However, the in-layer data size of DNN is usually larger than the original data, so the communication time to send intermediate data to the cloud will also increase end-to-end latency. To cope with these challenges, this paper proposes a novel convolutional neural network structure-BBNet-that accelerates collaborative inference from two levels: (1) through channel-pruning: reducing the number of calculations and parameters of the original network; (2) through compressing the feature map at the split point to further reduce the size of the data transmitted. In addition, This paper implemented the BBNet structure based on NVIDIA Nano and the server. Compared with the original network, BBNet's FLOPs and parameter achieve up to 5.67× and 11.57× on the compression rate, respectively. In the best case, the feature compression layer can reach a bit-compression rate of 512×. Compared with the better bandwidth conditions, BBNet has a more obvious inference delay when the network conditions are poor. For example, when the upload bandwidth is only 20 kb/s, the end-to-end latency of BBNet is increased by 38.89× compared with the cloud-only approach.

Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things.

Due to limited resources of the Internet of Things (IoT) edge devices, deep neural network (DNN) inference requires collaboration with cloud server platforms, where DNN inference is partitioned and offloaded to high-performance servers to reduce end-to-end latency. As data-intensive intermediate feature space at the partitioned layer should be transmitted to the servers, efficient compression of the feature space is imperative for high-throughput inference. However, the feature space at deeper layers has different characteristics than natural images, limiting the compression performance by conventional preprocessing and encoding techniques. To tackle this limitation, we introduce a new method for compressing DNN intermediate feature space using a specialized autoencoder, called auto-tiler. The proposed auto-tiler is designed to include the tiling process and provide multiple input/output dimensions to support various partitioned layers and compression ratios. The results show that auto-tiler achieves 18% to 67% higher percent point accuracy compared to the existing methods at the same bitrate while reducing the process latency by 73% to 81%. The dimension variability of an auto-tiler also reduces the storage overhead by 62% with negligible accuracy loss.

Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach.

The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. To fulfill this task, this research proposes a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In the proposed methodology, first, the cFGM method is proposed to accurately derive the priorities of criteria. Subsequently, each expert applies the FTOPSIS method to compare the overall performances of alternative suppliers in the COVID-19 pandemic. The sensitivity of an expert to any change in the overall performance of the alternative supplier is also considered. Finally, the FWI operator is used to aggregate the comparison results by all experts, for which an expert's authority level is set to a value proportional to the consistency of his/her pairwise comparison results. The cFGM-FTOPSIS-FWI approach has been applied to select suitable alternative suppliers for a Taiwanese foundry in the COVID-19 pandemic.

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach.

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

Plan de comunicación del Sistema de Inteligencia Colaborativa para el Grupo Empresarial BioCubaFarma / Communication plan for the Collaborative Intelligence System at BioCubaFarma Business Group

El presente trabajo tuvo como objetivo diseñar un plan de comunicación para el Sistema de Inteligencia Colaborativa del Grupo Empresarial BioCubaFarma correspondiente al período 2019-2020. Se utilizó una metodología basada en la propuesta de Paul Capriotti en torno a la planificación de la comunicación. Se emplearon el análisis documental, la encuesta y la observación no participante como técnicas, así como el cuestionario y la guía de observación como instrumentos de recopilación de información. Se incorporó el enfoque de la Norma española UNE 166006:2018 para la gestión de la vigilancia e inteligencia, que reconoce a la comunicación como uno de los componentes de este sistema de gestión, y se resaltó su importancia en la generación, conservación, diseminación y uso de la información en relación con sus públicos. A partir de un diagnóstico del proceso de vigilancia e inteligencia en el sector se definieron los públicos, los canales de comunicación, la temporalidad y el alcance del plan de comunicación. Se estableció un sistema de objetivos y se especificaron sus estrategias y tácticas. Se definieron las formas de evaluación y se analizó la comunicación desde su dinamismo, complejidad, intersubjetividad e interacción social, aspectos que contribuyen a la gestión del sistema de inteligencia colaborativa para la co-creación, difusión y promoción de productos y servicios informacionales de vigilancia e inteligencia como sustento de la toma de decisiones(AU)

The current paper had as objective to design a communication plan for the Collaborative Intelligence System of BioCubaFarma Enterprises Group, (period 2019-2020). A methodology based on Paul Capriotti's proposal related to the communication planning was used. Documental analysis, survey and non-participatory observation as techniques, and questionnaire and observation guide as instruments of data collection, were applied. The approach of the Spanish standard UNE 166006:2018 on surveillance and intelligence management was incorporated. The standard recognizes communication as one of the components of this system, highlighting its importance for generation, conservation, dissemination and use of information in relation to its audiences. A diagnosis of surveillance and intelligence processes in the sector was done in order to define audiences, communication channels, temporality and the scope of the Communication Plan. A target system was established, and its corresponding strategies with their tactics were specified and described. Based on the specific objectives and their strategies, the forms of assessment were defined. The communication was analyzed from its dynamism, complexity, inter-subjectivity and social interaction. These aspects contribute to the collaborative intelligence system management for co-creation, dissemination and promotion of information products and services related to surveillance and intelligence, which support decision-making processes(AU)

Collaborative sensorimotor intelligence: the scrum as a model.

AIM: Using M-Rex, a rugby scrum simulator, we developed tools to describe scrummaging forces and to prevent accident. METHODS: We tested three groups of frontliners at national level. The simulator was passive or responded to the player(s) to simulate the reaction of opposite players. Sensors in the beam measured the force exerted by each of the players. Their movements were recorded with a Codamotion system. RESULTS: The force signals exhibited two phases: a transient phase, similar to a damped sinusoid with a dominant frequency around 5 Hz when the players scrummaged alone and with a wider range when playing together; then, a sustained phase could be decomposed in two components: a DC component remained stable whether frontliners played alone or together. In contrast, its variability decreased when the frontliners played together compared with when they played alone. As for the oscillations, the frontliners exhibited a large variability in their ability to synchronise their efforts during the sustained phase. The synchronisation between the hooker and the props was quite efficient, while it was always missing between two props. Finally, we were able to study postural readjustments and their synchronisation among players during the sustained phase. CONCLUSION: This study shows that by using adequate methods, it is possible to assess the frontline collective intelligence. These findings may pave the way for innovative methods of training to improve players' collective behaviour.

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges.

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.

Interdisciplinary Education from a College of Nursing and School of Medicine.

We examine a newly designed, interdisciplinary education program and clinical rotation for the first-year obstetrics and gynecology resident, implemented at the University of Colorado, Denver, Colorado, between the College of Nursing midwifery faculty and the School of Medicine Department of Obstetrics and Gynecology. The barriers to program development, along with the advantages and disadvantages of collaboration between nursing and medical schools, are reviewed. The clinical experience, consisting of 5 clinical shifts, was designed using the conceptual model of collaborative intelligence. A formal rotation with the midwife was constructed for the first-year resident on the labor and delivery unit, providing care to intrapartum and postpartum women and families. The program included didactic and clinical teaching, with an emphasis on the normal physiologic process of birth and introduction to the midwifery scope of practice and philosophy of care. Formative evaluation of the clinical rotation demonstrated strong interest for continuation of the program and an ability to appreciate midwifery components of care in a limited exposure. Moreover, program development was successful without requiring large curricular changes for the resident. Future planning includes expansion of the program with increased emphasis on the postpartum and breastfeeding woman and continued program evaluation. The long-term success of such collaborations will depend on the continued support of the American College of Nurse-Midwives and the American Congress of Obstetricians and Gynecologists in developing and improving interdisciplinary educational teams. This article is part of a special series of articles that address midwifery innovations in clinical practice, education, interprofessional collaboration, health policy, and global health.

Procesos de inteligencia colectiva y colaborativa en el marco de tecnologías web 2.0: conceptos, problemas y aplicaciones / Collective and collaborative intelligence processes developed in the framework of web 2.0 technologies: concepts, issues and applications

El presente trabajo consiste, por un lado, en una revisión y análisis de la literatura científica referente a los conceptos de inteligencia emergente, inteligencia colectiva e inteligencia colaborativa. Además, en un esbozo de marco teórico, se encamina a dar cuenta de la problemática de los referidos conceptos en el ámbito de la denominada Web 2.0 o Web Colaborativa. En un primer apartado, se realiza una conceptualización y diferenciación de los referidos constructos para, posteriormente, contextualizarlos en el marco de las mencionadas tecnologías. Fuentes pertinentes han sido seleccionadas a través de las bases de datos EBSCO y PsycINFO, así como de Conferencias Online o Journals de publicación digital. Si bien se destacan resultados a favor de la productividad del trabajo colectivo y colaborativo suscitado por estas tecnologías, se señala la ausencia de suicientes estudios exhaustivos que evalúen los alcances de este fenómeno y den cuenta de sus múltiples áreas de aplicación.

This article consists, in the one hand, in a review and analysis of the scientific literature concerning Emergent Intelligence, Collective Intelligence and Collaborative Intelligence concepts. On the other hand, in order to promote a conceptual framework, is routed to give an account of the problem concerning the referred concepts in the scope of the Web 2.0. In the irst section, these constructs are conceptualized and differentiated in order to then contextualize them to the framework of these technologies. Relevant sources have been selected through EBSCO and PsycINFO databases, as well as Online Conferences and digital publication journals. Although some results recorded show the productivity of collective and collaborative work reached through these technologies, this paper reports the lack of enough systematic and exhaustive studies assessing the scopes of this phenomenon and showing its multiple implementation areas.

Procesos de inteligencia colectiva y colaborativa en el marco de tecnologías web 2.0: conceptos, problemas y aplicaciones / Collective and collaborative intelligence processes developed in the framework of web 2.0 technologies: concepts, issues and applications

El presente trabajo consiste, por un lado, en una revisión y análisis de la literatura científica referente a los conceptos de inteligencia emergente, inteligencia colectiva e inteligencia colaborativa. Además, en un esbozo de marco teórico, se encamina a dar cuenta de la problemática de los referidos conceptos en el ámbito de la denominada Web 2.0 o Web Colaborativa. En un primer apartado, se realiza una conceptualización y diferenciación de los referidos constructos para, posteriormente, contextualizarlos en el marco de las mencionadas tecnologías. Fuentes pertinentes han sido seleccionadas a través de las bases de datos EBSCO y PsycINFO, así como de Conferencias Online o Journals de publicación digital. Si bien se destacan resultados a favor de la productividad del trabajo colectivo y colaborativo suscitado por estas tecnologías, se señala la ausencia de suicientes estudios exhaustivos que evalúen los alcances de este fenómeno y den cuenta de sus múltiples áreas de aplicación.(AU)

This article consists, in the one hand, in a review and analysis of the scientific literature concerning Emergent Intelligence, Collective Intelligence and Collaborative Intelligence concepts. On the other hand, in order to promote a conceptual framework, is routed to give an account of the problem concerning the referred concepts in the scope of the Web 2.0. In the irst section, these constructs are conceptualized and differentiated in order to then contextualize them to the framework of these technologies. Relevant sources have been selected through EBSCO and PsycINFO databases, as well as Online Conferences and digital publication journals. Although some results recorded show the productivity of collective and collaborative work reached through these technologies, this paper reports the lack of enough systematic and exhaustive studies assessing the scopes of this phenomenon and showing its multiple implementation areas.(AU)