monitoring task
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Although the monitoring system has been widely used, the actual monitoring task still needs more manpower to complete. This paper takes yolov5l model and deep sort algorithm as the basic framework to identify and track the staff in kitchen environment. We apply a relation construction with detected items and people, then label the relation corresponding to behaviors violate the regulations of kitchen, such as the staff did not wear mask or hat. We train our model and the experimental results show that the model can correctly identify the inappropriate behaviors of staff. The model achieves the time-constrained accuracy of 95.32% in identifying whether the staff wear a hat or not, and the time-constrained accuracy of 96.32% in identifying whether the staff wear mask correctly. The result shows that the proposed model could fulfil monitoring task in this kitchen environment.

Previous findings in the literature point to the influence that speech perception has on word recognition. However, which specific aspects of the first (L1) and second language (L2) mapping play the most important role is still not fully understood. This study explores whether, and if so, how, L1-L2 syllable-structure differences affect word recognition. Spanish- and German-speaking English learners completed an AXB and a word-monitoring task in English that manipulated the presence of a vowel in words with /s/-initial consonant clusters—e.g., especially versus specially. The results show a clear effect of L1 on L2 learners’ perception and word recognition, with the German group outperforming the Spanish one. These results indicate that the similarity in the syllable structure between English and German fosters positive transfer in both perception and word recognition despite the inexact segmental mapping.

The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots' intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1 score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.

High stress levels among hospital workers could be harmful to both workers and the institution. Enabling the workers to monitor their stress level has many advantages. Knowing their own stress level can help them to stay aware and feel more in control of their response to situations and know when it is time to relax or take some actions to treat it properly. This monitoring task can be enabled by using wearable devices to measure physiological responses related to stress. In this work, we propose a smartwatch sensors based continuous stress detection method using some individual classifiers and classifier ensembles. The experiment results show that all of the classifiers work quite well to detect stress with an accuracy of more than 70%. The results also show that the ensemble method obtained higher accuracy and F1-measure compared to all of the individual classifiers. The best accuracy was obtained by the ensemble with soft voting strategy (ES) with 87.10% while the hard voting strategy (EH) achieved the best F1-measure with 77.45%.

The application scenarios and market shares of industrial robots have been increasing in recent years, and with them comes a huge market and technical demand for industrial robot-monitoring system (IRMS). With the development of IoT and cloud computing technologies, industrial robot monitoring has entered the cloud computing era. However, the data of industrial robot-monitoring tasks have characteristics of large data volume and high information redundancy, and need to occupy a large amount of communication bandwidth in cloud computing architecture, so cloud-based IRMS has gradually become unable to meet its performance and cost requirements. Therefore, this work constructs edge–cloud architecture for the IRMS. The industrial robot-monitoring task will be executed in the form of workflow and the local monitor will allocate computing resources for the subtasks of the workflow by analyzing the current situation of the edge–cloud network. In this work, the allocation problem of industrial robot-monitoring workflow is modeled as a latency and cost bi-objective optimization problem, and its solution is based on the evolutionary algorithm of the heuristic improvement NSGA-II. The experimental results demonstrate that the proposed algorithm can find non-dominated solutions faster and be closer to the Pareto frontier of the problem. The monitor can select an effective solution in the Pareto frontier to meet the needs of the monitoring task.

In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems’ sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems’ behavior and how it can aid domain experts in the systems’ maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain.

Human memory is prone to memory errors and distortion. Evidence from studies on cognitive functions in bilinguals indicates that they might be prone to different types of memory errors compared to monolinguals; however, the effect of language in false memories is still understudied. Source monitoring processes required for proper memory functioning, presumably, rely on inhibitory control, which is also heavily utilized by bilinguals. Moreover, it is suggested that thinking in a second language leads to more systematic and deliberate reasoning. All these results lead to expect that bilinguals are more analytical when processing information in their second language overcoming some memory errors depending on the language of information. To test this hypothesis, we run a classical misinformation experiment with an explicit source monitoring task with a sample of Russian–English bilinguals. The language of the misinformation presentation did not affect the degree of the misinformation effect between the Russian and English languages. Source monitoring demonstrated an overall higher accuracy for attributions to the English source over the Russian source. Furthermore, analysis on incorrect source attributions showed that when participants misattributed the sources of false information (English or Russian narrative), they favored the Russian source over the not presented condition. Taken together, these results imply that high proficiency in the second language does not affect misinformation and that information processing and memory monitoring in bilinguals can differ depending on the language of the information, which seems to lead to some memory errors and not others.

Source monitoring refers to the ability to identify the origin of a memory, for example, whether you remember saying something or thinking about it, and confusions of these sources have been associated with the experience of auditory verbal hallucinations (AVHs). Both AVHs and source confusions are reported to originate from dysfunctional brain activations in the prefrontal cortex (PFC) and the superior temporal gyrus (STG); specifically, it is assumed that a hypoactive PFC and a hyperactive STG gives rise to AVHs and source confusions. We set out to test this assumption by trying to mimic this hypertemporal/hypofrontal model in healthy individuals with transcranial direct current stimulation (tDCS): the inhibitory cathode was placed over the left PFC and the excitatory anode over the left dorsolateral STG. Participants completed a reality monitoring task (distinguishing between external and internal memory sources) and an internal source monitoring task (distinguishing between two or more internal memory sources) in two separate experiments (offline vs. online tDCS). In the offline experiment (n = 34), both source monitoring tasks were completed after tDCS stimulation, and in the online experiment (n = 27) source monitoring tasks were completed while simultaneously being stimulated with tDCS. We found that internal source monitoring abilities were significantly enhanced during active online tDCS, while reality monitoring abilities were unaffected by stimulation in both experiments. We speculate, based on combining the present findings with previous studies, that there might be different brain areas involved in reality and internal source monitoring. While internal source monitoring seems to involve speech production areas, specifically Broca’s area, as suggested in the present study, reality monitoring seems to rely more on the STG and DLPFC, as shown in other studies of the field.

Despite its rapid advancement, automation remains vulnerable to system failures. The reliability of automation may impact users’ trust and how they interact with it. Additionally, the type of error can uniquely redirect user behavior. This study investigated how reliability and error type impact operator trust and monitoring performance. Participants completed a monitoring task at either 50% or 90% reliability, experiencing either misses or false alarms from an automated alert system. It was hypothesized that automation reliability would impact trust, while error type would also impact reliance and compliance behaviors. Results indicated that misses had a greater impact on monitoring performance than false alarms, while reliability did not influence performance. Trust was not influenced by reliability or error type and showed no relationship with performance measures. These results can help further clarify the way automation failures shape how humans interact with automation and inform the design of future automated systems.