Selection Task Research Articles

"Wait, how did you call this?": Speaker-specific word choices are stored and generalized.

It has been repeatedly shown that individuals track speaker-specific language use during interaction. Most studies focused on how this facilitates meaning inference when interspeaker variation differentiates between two or more alternatives, or how it allows for successful lexical alignment. However, it has been unclear whether mapping interspeaker variation is stored actively, and if so, what purposes this storage serves. In a pseudointeractive experiment, we created interspeaker variation in naming preferences, such that one speaker (the common speaker) consistently produced favored words, and the other speaker consistently produced less-favored/disfavored words (the uncommon speaker), across two conditions-one where both speakers were relatively common, and one where one of the speakers was highly uncommon. Participants engaged in a picture selection task, at first as matchers (where they were instructed by one of the speakers-each in his/her turn-which image to choose), and then as directors (where they were the instructors). They were then tested on how well they mapped interspeaker variation and how they generalized it linguistically and socially. Participants were successful at directly mapping interspeaker variation in naming preferences. Furthermore, they used this information in (a) lexically aligning with their interlocutors, (b) hypothesizing about unexposed word choices by these speakers, and (c) creating social representations of the speakers as individuals. In line with surprisal-driven learning accounts, these effects were larger for a speaker that used highly uncommon words. Our results suggest that individuals store interspeaker variation explicitly, which in turn helps them to predict their interlocutors' future linguistic and social behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hybrid metaheuristics for selective inference task offloading under time and energy constraints for real-time IoT sensing systems

The recent widespread of AI-powered real-time applications necessitates the use of edge computing for inference task offloading. Power constrained edge devices are required to balance between processing inference tasks locally or offload to edge servers. This decision is determined according to the time constraint demanded by the real-time nature of applications, and the energy constraint dictated by the device’s power budget. This problem is further exacerbated in the case of systems leveraging multiple local inference models varying in size and accuracy. In this work, we tackle the problem of assigning inference models to inference tasks either using local inference models or by offloading to edge servers under time and energy constraints while maximizing the overall accuracy of the system. This problem is shown to be strongly NP-hard and therefore, we propose a hybrid genetic algorithm (HGSTO) to solve this problem. We leverage the speed of simulated annealing (SA) with the accuracy of genetic algorithms (GA) to develop a hybrid, fast and accurate algorithm compared with classic GA, SA and Particle Swarm Optimization (PSO). Experiment results show that HGSTO achieved on-par or higher accuracy than GA while resulting in significantly lower scheduling times compared to other schemes.

Impact of nutrient warning labels on Colombian consumers' selection and identification of food and drinks high in sugar, sodium, and saturated fat: A randomized controlled trial.

This study assessed the impact of nutrient warnings on product selection and ability to identify food products high in nutrients of concern in Colombia. In an online experiment (May-June 2023), Colombian adults were randomized to a nutrient warning, guideline daily amounts (GDA), Nutri-Score, or no-label condition (n = 8,004). Participants completed selection tasks between two fruit drinks labeled according to their condition, one high in sugar and one not. Next, participants answered questions about products high in sugar, sodium, and/or saturated fat ("high-in" product). Finally, they selected which label would most discourage them from consuming a high-in product. Fewer participants (17%) exposed to the nutrient warning indicated they would purchase the high-sugar fruit drink compared to Nutri-Score (27%, Holm-adjusted (adj) p<0.001) and no label conditions (31%, adj p<0.001); there were no differences between the nutrient warning and GDA label (14%, adj p = 0.087). Compared to the nutrient warning, the GDA label was slightly more effective at helping consumers identify which drink was high in sugar (89% versus 92%, adj p<0.001), while the Nutri-Score and no-label conditions were less effective. Compared to all other conditions, nutrient warnings were more effective at helping participants identify that products were high in nutrients of concern, were more effective at decreasing intentions to purchase these high-in products and were perceived as more effective. Nutrient warnings were most often selected as the label that most discouraged consumption. Nutrient warnings are a promising policy to help consumers identify and discourage consumption of products high in nutrients of concern. Trial Registration: NCT05783726.

A domain-independent agent architecture for adaptive operation in evolving open worlds

1Angry Birds Competition. Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed discrete-continuous worlds that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents' models to perform effectively. HYDRA is based upon PDDL+, a rich modeling language for planning in mixed, discrete-continuous environments. It augments the planning module with visual reasoning, task selection, and action execution modules for closed-loop interaction with complex environments. HYDRA implements a novel meta-reasoning process that enables the agent to monitor its own behavior from a variety of aspects. The process employs a diverse set of computational methods to maintain expectations about the agent's own behavior in an environment. Divergences from those expectations are useful in detecting when the environment has evolved and identifying opportunities to adapt the underlying models. HYDRA builds upon ideas from diagnosis and repair and uses a heuristics-guided search over model changes such that they become competent in novel conditions. The HYDRA framework has been used to implement novelty-aware agents for three diverse domains - CartPole++ (a higher dimension variant of a classic control problem), Science Birds (an IJCAI competition problem1), and PogoStick (a specific problem domain in Minecraft). We report empirical observations from these domains to demonstrate the efficacy of various components in the novelty meta-reasoning process.

Rule-based modulation of a sensorimotor transformation across cortical areas.

Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.

A one-man bilingual cocktail party: linguistic and non-linguistic effects on bilinguals’ speech recognition in Mandarin and English

Multilingual speakers can find speech recognition in everyday environments like restaurants and open-plan offices particularly challenging. In a world where speaking multiple languages is increasingly common, effective clinical and educational interventions will require a better understanding of how factors like multilingual contexts and listeners’ language proficiency interact with adverse listening environments. For example, word and phrase recognition is facilitated when competing voices speak different languages. Is this due to a “release from masking” from lower-level acoustic differences between languages and talkers, or higher-level cognitive and linguistic factors? To address this question, we created a “one-man bilingual cocktail party” selective attention task using English and Mandarin speech from one bilingual talker to reduce low-level acoustic cues. In Experiment 1, 58 listeners more accurately recognized English targets when distracting speech was Mandarin compared to English. Bilingual Mandarin–English listeners experienced significantly more interference and intrusions from the Mandarin distractor than did English listeners, exacerbated by challenging target-to-masker ratios. In Experiment 2, 29 Mandarin–English bilingual listeners exhibited linguistic release from masking in both languages. Bilinguals experienced greater release from masking when attending to English, confirming an influence of linguistic knowledge on the “cocktail party” paradigm that is separate from primarily energetic masking effects. Effects of higher-order language processing and expertise emerge only in the most demanding target-to-masker contexts. The “one-man bilingual cocktail party” establishes a useful tool for future investigations and characterization of communication challenges in the large and growing worldwide community of Mandarin–English bilinguals.

Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior.

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.

Leveraging federated learning for boosting data privacy and performance in IVF embryo selection.

To study the effectiveness of federated learning in in vitro fertilization on embryo evaluation tasks. This is a retrospective cohort analysis. Two datasets were used in this study. The ploidy status dataset consisted of 10,065 embryo records, 3760 treatments, and 2479 infertile couples from 5 hospitals. The clinical pregnancy dataset consisted of 4495 embryo records, 4495 treatments, and 3704 infertile couples from 4 hospitals. Federated learning and the gradient boosting decision tree algorithm were utilized for modeling. On the ploidy status dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 71.78%, 73.10%, 69.39%, 69.72%, and 73.46% for 5 hospitals respectively, showing an average increase of 2.5% compared to those of our model trained without federated learning. On the clinical pregnancy dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 72.03%, 56.77%, 61.63%, and 58.58% for 4 hospitals respectively, showing an average increase of 3.08%. Federated learning can improve data privacy and data security and meanwhile improve the performance of embryo selection tasks by leveraging data from multiple sources. This study demonstrates the effectiveness of federated learning in embryo evaluation, and the results show the promise for future application.

Balance, Landing Biomechanics, and Functional Movement Screen Characteristics With and Without Knee Exoskeleton in Military Soldiers.

A light-weight pneumatic-powered knee exoskeleton could augment mobility and lifting capabilities for a variety of occupational settings. However, added weight/bulkiness and artificially produced knee extension torque could compromise sensorimotor characteristics. Ten healthy participants conducted 3 visits within 10 days to the biomechanics laboratory. Participants were asked to complete the following tasks on each visit: single-leg balance, single-leg drop-landing, and select functional movement tasks. Balance characteristics (the ground reaction forces variability and center-of-pressure velocity) were derived from force plates while knee flexion angles during drop-landing and functional movement tasks were captured using a motion capture system. Descriptive statistics as well as paired t-tests or Wilcoxon signed-rank tests were used to compare between conditions. Significance was set at P < .05 a priori. During single-leg balance, the ground reaction force variabilities were significantly increased (P = .013-.019) and the center of pressure velocity was decreased (P = .001-.017) when wearing knee exoskeleton. During single-leg drop-landing, the exoskeleton condition showed lower knee flexion angles at the initial contact (P = .004-.021) and peak (P = .006-.010). Additionally, the peak vertical ground reaction force was higher in the exoskeleton condition (P = .007). During functional movement tasks, the exoskeleton condition showed less knee flexion range-of-motion during the overhead squat (P = .007-.033) and hurdle step-over (P = .004-.005). Participants exhibited stiffer landing technique with the exoskeleton. Given that these compromised sensorimotor characteristics have been associated with musculoskeletal injury risk, modifications to exoskeletons to promote softer landing and greater knee flexion range-of-motion during dynamic activities may be warranted.

FORMATION OF LOGICAL UNIVERSAL ACTIONS AMONG YOUNGER STUDENTS IN MATHEMATICS LESSONS

The state educational standard of primary general education is based on the concept of the formation of universal educational activities. Logical actions belong to one of the types of universal learning activities. Their formation is an important component of the work of primary school teachers, since logical operations contribute to the development of an active cognitive position in younger schoolchildren. People with logical thinking are able to solve the problems that arise in front of them. In elementary school, the subject of mathematics has the greatest opportunity for the formation of logical actions. In order for this process to be successful, teachers must comply with a number of conditions when preparing and conducting math lessons. In the lessons, it is important to organize "logical five minutes", select variable tasks and ensure a change of student activities. It is important to take into account the age characteristics of children, all tasks should be selected in accordance with the capabilities of younger students. Such work increases the effectiveness of the educational process. Students become active and interested in acquiring new knowledge that they can apply both in educational activities and in real life. This is the universality of logical actions. This article describes in detail the features of the work on the formation of logical actions in younger schoolchildren in mathematics lessons.

An EEG channel selection method for motor imagery based on Fisher score and local optimization

Objective. Multi-channel electroencephalogram (EEG) technology in brain–computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness. Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection. Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%. Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

Humans do not avoid reactively implementing cognitive control.

The ability to exert cognitive control allows us to achieve goals in the face of distraction and competing actions. However, control is costly-people generally aim to minimize its demands. Because control takes many forms, it is important to understand whether such costs apply universally. Specifically, reactive control, which is recruited in response to stimulus or contextual features, is theorized to be deployed automatically, and not depend on attentional resources. Here, we investigated whether people avoided implementing reactive control in three experiments. In all, participants performed a Stroop task in which certain items were mostly incongruent (MI), that is, associated with a high likelihood of conflict (triggering a focused control setting). Other items were mostly congruent, that is, associated with a low likelihood of conflict (triggering a relaxed control setting). Experiment 1 demonstrated that these control settings transfer to a subsequent unbiased transfer phase. In Experiments 2-3, we used a demand selection task to investigate whether people would avoid choice options that yielded items that were previously MI. In all, participants continued to retrieve focused control settings for previously MI items, but they did not avoid them in the demand selection task. Critically, we only found demand avoidance when there was an objective difference in demand between options. These findings are consistent with the idea that implementing reactive control does not register as costly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sparsity-Preserved Pareto Optimization for Subset Selection

Subset selection that selects a limited number of variables optimizing many given criteria, is a fundamental problem with various applications such as sparse regression and unsupervised feature selection. Among the existing algorithms for subset selection, evolutionary algorithms (EAs) have achieved great performance in obtaining the subsets with high quality. However, as the selected subsets are sparse, i.e., most variables of these solutions are zero, most existing EAs for subset selection problems pose great challenges to find the optimal solutions effectively and efficiently, especially when the size of original set is large. To this end, we propose the SPESS approach which is a sparsity preserved evolutionary algorithm for subset selection. To be specific, we design two sparsity preserved operators (i.e., sparsity preserved crossover operator and sparsity preserved mutation operator), which can be used in SPESS to ensure the sparsity of the generated solutions in the process of Pareto optimization. The main advantages of the proposed sparsity preserved operators are that they can obtain high quality solutions and improve the search efficiency of the algorithm. According to the experimental results on 12 sparse regression problems and 12 unsupervised feature selection problems, the proposed SPESS is superior over the state-of-the-arts in solving subset selection tasks and the effectiveness of the proposed two sparsity preserved operators is also verified.

Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental Machine Learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.

LBATSM: Load Balancing Aware Task Selection and Migration Approach in Fog Computing Environment

LBATSM: Load Balancing Aware Task Selection and Migration Approach in Fog Computing Environment

A YOLO-based deep learning model for Real-Time face mask detection via drone surveillance in public spaces

Automating face mask detection in public areas is paramount to maintaining public health, especially in the context of the COVID-19 pandemic. Utilization of technologies such as deep learning and computer vision systems enables effective monitoring of mask compliance, thereby minimizing the risk of virus spread. Real-time detection helps in prompt intervention for and enforcement of the use of masks, thereby preventing potential outbreaks and ensuring compliance with public health guidelines. This method helps save human resources and makes the reinforcement of wearing masks in public areas consistent and objective. Automatic detection of face masks serves as a key tool for preventing the spread of contagious diseases, protecting public health, and creating a safer environment for every person. This study addresses the challenges of real-time face mask detection via drone surveillance in public spaces, with reference to three categories: wearing of mask, incorrect wearing of mask, and no mask. Addressing these challenges entails an efficient and robust object detection and recognition algorithm. This algorithm can deal with a crowd of multiple faces via a mobile camera carried by a mini drone, and performs real-time video processing. Accordingly, this study proposes a You Only Look Once (YOLO) based deep learning C-Mask model for real-time face mask detection and recognition via drone surveillance in public spaces. The C-Mask model aims to operate within a mini drone surveillance system and provide efficient and robust face mask detection. The C-Mask model performs preprocessing, feature extraction, feature generation, feature enhancement, feature selection, and multivariate classification tasks for each face mask detection cycle. The preprocessing task prepares the training and testing data in the form of images for further processing. The feature extraction task is performed using a Convolutional Neural Network (CNN). Moreover, Cross-Stage Partial (CSP) DarkNet53 is used to improve the feature extraction and to facilitate the model’s object detection ability. A data augmentation algorithm is used for feature generation to enhance the model’s training robustness. The feature enhancement task is performed by applying the Path Aggregation Network (PANet) and Spatial Pyramid Pooling Network (SPPNet) algorithms, which are deployed to enhance the extracted and generated features. The classification task is performed through multi-label classification, wherein each object in an image can belong to multiple classes simultaneously, and the network generates a grid of bounding boxes and corresponding confidence scores for each class. The YOLO-based C-Mask model testing is performed by experimenting with various face mask detection scenarios and with varying mask colors and types, to ensure the efficiency and robustness of the proposed model. The C-Mask model test results show that this model can correctly and effectively detect face masks in real-time video streams under various conditions with an overall accuracy of 92.20%, precision of 92.04, recall of 90.83%, and F1-score of 89.95%, for all the three classes. These high scores have been obtained despite mini drone mobility and camera orientation adjustment substantially affecting face mask detection performance.

GB-AFS: graph-based automatic feature selection for multi-class classification via Mean Simplified Silhouette

This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks. The method determines the minimum combination of features required to sustain prediction performance while maintaining complementary discriminating abilities between different classes. It does not require any user-defined parameters such as the number of features to select. The minimum number of features is selected using our newly developed Mean Simplified Silhouette (abbreviated as MSS) index, designed to evaluate the clustering results for the feature selection task. To illustrate the effectiveness and generality of the method, we applied the GB-AFS method using various combinations of statistical measures and dimensionality reduction techniques. The experimental results demonstrate the superior performance of the proposed GB-AFS over other filter-based techniques and automatic feature selection approaches, and demonstrate that the GB-AFS method is independent of the statistical measure or the dimensionality reduction technique chosen by the user. Moreover, the proposed method maintained the accuracy achieved when utilizing all features while using only 7–30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30\\%$$\\end{document} of the original features. This resulted in an average time saving ranging from 15%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15\\%$$\\end{document} for the smallest dataset to 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} for the largest. Our code is available at https://github.com/davidlevinwork/gbfs/.

Priority influences task selection decisions in multi-task management

The role of task priority on task selection in multi-task management is unclear based on prior work, leading to a common finding of ‘priority neglect’. However, properties such as urgency and conflict may influence whether operators weigh priority in their decision. We examined the role of instructed task prioritization, bolstered by more urgent and conflicting conditions, on how operators select among emergent, concurrent tasks when multitasking. Using the Multi-Attribute Task Battery (MATB) multitasking platform we tested both an auditory communications task and a manual tracking task as the priority tasks. Results showed that instructed priority significantly increased target task selection under the conflicting task conditions for both tasks. Urgency itself may modulate whether instructions to prioritize affect task selection choices when multitasking, and therefore counter to prior results instructions may yet be useful for helping operators select a higher priority task under conflict, a generalizable effect to be further explored.

A New Approach to Interior Design: Generating Creative Interior Design Videos of Various Design Styles from Indoor Texture-Free 3D Models

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence.

PCB Image Defects Detection by Artificial Neural Networks and Resistance Analysis

The approach contains the sequence of algorithms and formulas for image processing. They are single-layer neural networks, thinning, clustering, mathematical image comparison, and measurements of the trace length and width. All these procedures solve the task of selection and separation of the main objects in the printed circuit board: contacts, traces, and defects. The calculated features connect the conductance resistance of traces with the characteristics of defects. Imposing a tolerance on the distributed or concentrated changes of resistance it is possible to mark the defective and suspicious printed circuit boards.