Selection Task Research Articles

On a dynamic and decentralized energy-aware technique for multi-robot task allocation

In the real world, multi-robot systems need to deal with on-the-fly (runtime) arrivals of new sets of tasks. This entails repeated adjustments of their current task allocations to include the newer ones while also ensuring that the overall performance does not degrade. This paper proposes a decentralized and distributed dynamic task allocation algorithm to handle this issue in a multi-robot scenario. The proposed work provides a conflict-free allocation of a set of tasks constituting a job to robots and minimizes the total execution time. These jobs can comprise multiple independent and/or dependent tasks or a combination thereof, which are injected on-the-fly into a network of robots. The dependent tasks of a job are related by precedence constraints that specify the ordering or dependencies between pairs of tasks. The work also describes a decentralized adaptive energy threshold mechanism for determining whether or not a robot needs to visit a battery stockpile after the execution of a task. Conflicting task selections among the robots in this decentralized set-up are resolved using mobile agents during runtime. Apart from allocating tasks to the robots, these mobile agents exploit the benefits of centralized and decentralized systems and provide an advantage over auction-based task allocation algorithms. The proposed algorithm takes into consideration the energy requirements, both during the task allocation process and actual execution. The proposed algorithm also caters to strategies to deal with delays caused by obstacles and congestion during the actual execution of the tasks. Experiments conducted using Webots, an open-source robot simulator, and Tartarus, a multi-agent platform, authenticate the efficacy of the proposed algorithm compared to other prominent task allocation algorithms in terms of minimization of average waiting time, total task allocation time, total job allocation time, and total execution time of an experiment.

The JavaScript Package Selection Task: A Comparative Experiment Using an LLM-based Approach

The JavaScript Package Selection Task: A Comparative Experiment Using an LLM-based Approach

NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactions

Natural gestures are crucial for mid-air interaction, but predicting and managing muscle fatigue is challenging. Existing torque-based models are limited in their ability to model above-shoulder interactions and to account for fatigue recovery. We introduce a new hybrid model, NICER , which combines a torque-based approach with a new term derived from the empirical measurement of muscle contraction and a recovery factor to account for decreasing fatigue during rest. We evaluated NICER in a mid-air selection task using two interaction methods with different degrees of perceived fatigue. Results show that NICER can accurately model above-shoulder interactions as well as reflect fatigue recovery during rest periods. Moreover, both interaction methods show a stronger correlation with subjective fatigue measurement ( ρ = 0.978/0.976) than a previous model, Cumulative Fatigue ( ρ = 0.966/0.923), confirming that NICER is a powerful analytical tool to predict fatigue across a variety of gesture-based interactive applications.

Spatio-temporal task pricing for shared electric micro-mobility battery-swapping platform with reinforcement learning

Spatial crowdsourcing has emerged in shared electric micro-mobility platforms, compensating occasional drivers (ODs) per task of swapping micro-mobility batteries. As ODs autonomously select tasks only when satisfied with predetermined compensation and travel distance, a traditional uniform pricing strategy results in possible low task completion. To resolve the imbalance between ODs and tasks, this study introduces a spatio-temporal pricing strategy where task prices differ by region and time interval. Considering the daily variations in task distribution and OD availability, the goal is to minimise the platform costs equal to the sum of total OD wages and penalties for uncompleted tasks. The reinforcement learning approach with proximal policy optimisation (PPO) is implemented to generate real-time continuous task prices. A domain-specific masking technique is incorporated to improve the learning process by disregarding the data from inactive grids in loss calculations. Computational results show that the PPO agent strategically raises prices in regions with insufficient ODs according to the OD density level. Further comparison with the mixed integer programming model with perfect information on ODs' willingness-to-accept parameters demonstrates the superior capability of our algorithm in navigating the uncertainties of OD task acceptance. A sensitivity analysis provides insights into the decision of system parameters.

Asymptotic teleportation scheme bridging between standard and port-based teleportation

Various modified quantum teleportation schemes are proposed to overcome experimental constraints or to meet specific application requirements for quantum communication. Hence, most schemes are developed and studied with unique methodologies, each with its own inherent challenges. Our research focuses on interconnecting these schemes, which appear to be unrelated to each other, based on the idea that the unique advantages of one scheme can compensate for the limitations of another. In this paper, we introduce an asymptotic teleportation scheme that requires the receiver to complete a classical selection task before performing a quantum correction. This scheme bridges standard teleportation with port-based teleportation through the transformation of joint measurements. Specifically, we categorize and analytically investigate protocols within this scheme for qubit systems. Given that the linear optics teleportation protocol without ancilla qubits is contained in the two non-trivial groups, we provide a novel perspective on its expansion. Furthermore, we discuss the potential application of a protocol from one of these groups as a universal programmable processor and extend these protocols to higher-dimensional systems while maintaining the same properties and potential, providing the analytic form of the joint measurement and its performance. These results thereby propose new avenues for developing a quantum network in higher-dimensional systems.

Do Children with Autism Spectrum Disorders Show Selective Trust in Social Robots?

Previous researches suggest that social robots can facilitate the learning of children with Autism Spectrum Disorder (ASD) by enhancing their interests, engagement, and attention. However, there is limited understanding regarding whether children with ASD can learn directly from the testimony of social robots and whether they can remain vigilant based on the perceived accuracy of these robots. Therefore, the present study was conducted to examine whether children with ASD demonstrated selective trust towards social robots. Twenty-nine children with ASD between ages of 4-7 years, and 38 typically-developing (TD) age and IQ-matched peers participated in classic selective trust tasks. During the tasks, they learned the names of novel objects from either a pair of social robots or a pair of human informants, where one informant had previously been established as accurate and the other inaccurate. Children with ASD trusted information from an accurate social robot over an inaccurate one, similar to their performance with human informants. However, compared to TD children, children with ASD exhibited lower levels of selective trust regardless of the type of informants they learned from. Our study suggests that children with ASD can selectively trust and acquire knowledge from social robots, shedding light on the potential use of social robots in supporting individuals with ASD.

Picker routing in scattered storage warehouses: an evaluation of solution methods based on TSP transformations

AbstractTo optimize their order fulfillment processes, many e-commerce warehouses employ a storage assignment strategy known as scattered or mixed-shelves storage. Under this approach, unit loads of homogeneous products are divided, and individual pieces are stored in various shelves throughout the warehouse. This arrangement ensures that products that appear together on unpredictable pick lists are stored in close proximity somewhere in the huge warehouses, reducing the travel distance for pickers. Despite these advancements, efficiently guiding pickers through the warehouse remains a significant planning challenge. Since the same products can be found in multiple storage positions, the traditional picker routing problem becomes more complex, as an additional selection task arises regarding which shelf to retrieve each requested product from. While previous research has developed several tailor-made solution algorithms, we demonstrate that known transformation schemes used for different variants of the well-known Traveling Salesman Problem (TSP) can be utilized to convert the single picker routing problem with scattered storage (SPRP-SS) into a classical TSP. This approach enables us to leverage the extensive array of state-of-the-art TSP solvers. The purpose of this paper is to explore the performance of these solvers when applied to solving the SPRP-SS. Through our computational study, we found that existing TSP solvers exhibit good performance, allowing near-optimal solutions to be obtained in less than a second for real-world scale SPRP-SS instances. Moreover, the efficiency of these TSP solvers remains unaffected by the number of cross aisles in the warehouse. Consequently, we exploit this flexibility to investigate the impact of cross aisles on picking performance in scattered storage warehouses.

Development and Validation of Phonological Processing Assessment Tool in Kannada Language

The research evidence in orthographically transparent language Kannada indicates that phonological processing significantly influences reading acquisition, and deficits increase the risk of dyslexia. A comprehensive assessment of phonological processing that includes phonological awareness, phonological memory, and phonological naming is crucial for implementing effective intervention strategies, thereby reducing the risk of dyslexia. However, there is a notable absence of phonological processing assessment tools specifically designed and validated for children learning to read alphasyllabary Kannada. The present study addresses this gap by developing and validating a phonological processing assessment tool for children between Grade I and Grade III learning to read alphasyllabary languages such as Kannada. The study was conducted in two distinct phases. The first phase consisted of developing and piloting a phonological processing assessment tool. It included the stages of task selection, item generation, content validation, pilot testing, and reliability analysis. In the second phase, the developed tool was validated by administering it to both typically developing children and children at-risk for dyslexia from Grade I through Grade III. Subsequently, the developmental appropriateness of the tool was tested by comparing the performance of typically developing children between the grades. Additionally, diagnostic validity, including sensitivity, specificity, and area under the curve, was established by comparing the performance of typically developing and at-risk children. The study makes a substantial contribution to research on reading in Akshara orthographies, offering a valuable clinical tool for identifying children at-risk for dyslexia.

Goal-directed attention transforms both working and long-term memory representations in the human parietal cortex.

The abundance of distractors in the world poses a major challenge to our brain's limited processing capacity, but little is known about how selective attention modulates stimulus representations in the brain to reduce interference and support durable target memory. Here, we collected functional magnetic resonance imaging (fMRI) data in a selective attention task in which target and distractor pictures of different visual categories were simultaneously presented. Participants were asked to selectively process the target according to the effective cue, either before the encoding period (i.e., perceptual attention) or the maintenance period (i.e., reflective attention). On the next day, participants were asked to perform a memory recognition task in the scanner in which the targets, distractors, and novel items were presented in a pseudorandom order. Behavioral results showed that perceptual attention was better at enhancing target memory and reducing distractor memory than reflective attention, although the overall memory capacity (memory for both target and distractor) was comparable. Using multiple-voxel pattern analysis of the neural data, we found more robust target representation and weaker distractor representation in working memory for perceptual attention than for reflective attention. Interestingly, perceptual attention partially shifted the regions involved in maintaining the target representation from the visual cortex to the parietal cortex. Furthermore, the targets and distractors simultaneously presented in the perceptual attention condition showed reduced pattern similarity in the parietal cortex during retrieval compared to items not presented together. This neural pattern repulsion positively correlated with individuals' recognition of both targets and distractors. These results emphasize the critical role of selective attention in transforming memory representations to reduce interference and improve long-term memory performance.

The role of cognitive biases involving selective interrogation of taste-based information in sugar-sweetened beverage consumption

Background and objectivesConsumption of sugar-sweetened beverages (SSBs) is a global public health issue. Consequently, there is strong interest in advancing understanding of the cognitive mechanisms that underpin excessive SSB consumption. This work proposes that selective interrogation of taste-based characteristics (e.g., flavour), rather than health-based characteristics (e.g., sugar content), of beverages is associated with greater SSB choice and consumption. MethodsTo evaluate this novel hypothesis, undergraduate students (n = 210) were recruited to complete self-report measures of SSB consumption, motivation and success in reducing SSB consumption and unhealthy eating more broadly, as well as a novel selective interrogation assessment task. This task provided participants with the opportunity to selectively interrogate taste-based and health-based characteristics of various mystery beverages. ResultsResults supported the hypothesis that greater selective interrogation of taste-based characteristics, rather than health-based characteristics, of beverages is associated with greater SSB choice and consumption. Additionally, results revealed that greater selective interrogation of taste-based characteristics was associated with decreased motivation to reduce SSB consumption and decreased motivation and success to reduce unhealthy consumption more broadly. LimitationsThe current study did not evaluate whether selective interrogation of taste-based characteristics of beverages predicted actual SSB consumption. ConclusionThese findings provide novel evidence for the potential role of selective interrogation of taste-based characteristics of beverages in SSB choice and consumption. An important implication of these findings is that in order for interventions focusing on front-of-package nutrition labels to be effective in reducing unhealthy consumption, it is necessary that individuals are selectively interrogating this ‘health-based’ information.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

The Effects of Reinforcing Task Alternations on Voluntary Task Selection

Everyday life often requires us to switch between states of cognitive flexibility and stability. However, little is known about what drives their regulation at the meta-control level. Based on current theories of cognitive control, we tested whether the regulation of cognitive flexibility and stability is guided by reinforcement learning. Using a task-switching paradigm with a double registration procedure – where people need to choose which task to perform before seeing the target, we systematically reinforced cued task switches or repetitions to test whether this led to more voluntary task switching (flexibility) or repeating (stability) on interspersed unrewarded free choice trials, respectively. While we did not find the hypothesized effect in an uninstructed version of the experiment (n=97), informing participants (n=58) on the reinforcement schedule on cued trials induced more switching or repeating on unrewarded free-choice trials. We speculate that people adapt their control strategies if they are aware of the respective benefits, even when they cease to be rewarded. Interestingly, choices were predominantly driven by task preference in both experiments and strongly correlated with task performance costs. This finding suggests that, when using an interspersed design with a double registration procedure, people mostly choose tasks based on task difficulty rather than switch avoidance. Together, these experiments help determine whether and when people can learn about the value of task selection strategies beyond the scope of a single task, and provide a self-regulating system to understand putative higher-order control processes.

Evaluating the effects of user motion and viewing mode on target selection in augmented reality

Target selection is a crucial task in augmented reality (AR). Recent evidence suggests that user motion can significantly influence target selection. However, no systematic research has been conducted on target selection within varied intensity user motions and AR settings. This paper was carried out to investigate the effects of four user motions (i.e., standing, walking, running, and jumping) and two viewing modes (i.e., viewpoint-dependent and viewpoint-independent) on user performance of target selection in AR. Two typical selection techniques (i.e., virtual hand and ray-casting) were utilized for short-range and long-range selection tasks, respectively. Our results indicate that the target selection performance decreased as the intensity of user motion increased, and users demonstrated better performance in the viewpoint-independent mode than in the viewpoint-dependent mode. We also observed that users took a longer amount of time to select targets when using the ray-casting technique than the virtual hand technique. We conclude with a set of design guidelines to improve the AR target selection performance of users while in motion.

Roulette wheel-based level learning evolutionary algorithm for feature selection of high-dimensional data

Feature selection in high-dimensional data is a large-scale sparse and discrete optimization problem. Most evolutionary algorithms are designed to tackle continuous optimization problems. However, when dealing with high-dimensional feature selection tasks, they often suffer from poor population diversity and are computationally expensive. To address these challenges, this work introduces a roulette wheel-based level learning evolutionary algorithm (RWLLEA). RWLLEA integrates two key components. Firstly, it employs a leveled population mode. Individuals from higher levels provide guidance to those at lower levels during the evolutionary process, thereby exploring the potential combinatorial effects among features. Secondly, recognizing the characteristics associated with the high-dimensional feature selection task, a roulette wheel-based update method is devised to dynamically reduce search space and harmonize the algorithm's exploitation and exploration capacities across different stages. The performance of the proposed method was evaluated by comparing it with six other feature selection techniques across a range of fifteen diverse datasets. The experimental findings demonstrate that the proposed method can achieve a reduced feature set with a shorter runtime and exhibit superior classification accuracy.

Influence of reinforcement learning on the inhibitory control of Internet gaming disorder.

Reward processing dysfunction and inhibition control deficiency have been observed in Internet gaming disorder (IGD). However, it is still unclear whether the previous reinforcement learning depends on reward/punishment feedback influences on the cognitive inhibitory control of IGD. This study compared the differences between an IGD group and healthy people without game experiences in the probability selection task and the subsequent stop signal task by the method of behavioral experiments, in order to explore whether the reward learning ability is impaired in the IGD group. We also discuss the influence of previous reward learning on subsequent inhibition control. The results showed that (1) during the reward learning phase, the IGD group's accuracy was significantly lower than that of the control group; (2) compared with the control group, the IGD group's reaction times were longer in the transfer phase; (3) for no-go trials of the inhibitory control phase after reward learning, the accuracy of the reward-related stimulation in the IGD group was lower than that of punishment-related or neutral stimulation, but there was no significant difference among the three conditions in the control group. These findings indicated that the reinforcement learning ability of the IGD group was impaired, which further caused the abnormal response to reinforcement stimuli.

Task potential in relation to teaching quality and teacher competence in secondary mathematics classrooms

The potential of tasks to foster mathematical learning and understanding is an important aspect of instruction and their implementation in teaching is thus often viewed to be positively related to the quality of instruction. Both the selection of tasks as well as their implementation in the classroom depend on many factors, with teachers’ knowledge and skills as one of the most important ones. The present study aims to analyze the relations between different aspects of task potential, the quality of instruction, and teachers’ competence in order to investigate whether task potential can be seen as an indicator for teaching quality, for teacher competence, or as an independent construct in models of educational effectiveness. To this end, we draw on data from the TEDS-Validate study, namely tests of mathematics teachers’ competence (n = 31) observations in their classrooms (n = 60), and an in-depth analysis of all tasks used in the respective lessons (n = 2490). Multiple regression analysis suggests that while some facets of task potential are related to either teaching quality or teacher competence, the potential of tasks emerges as an independent construct with some characteristics predicting the teaching quality of the respective lessons. Implications of these results for the role of tasks in educational effectiveness research are discussed.

Adversity and error-monitoring: Effects of emotional context.

This study tested whether self-reports of childhood adversity would predict altered error processing under emotional versus non-emotional task conditions. N = 99 undergraduates completed two selective attention tasks, a traditional color-word Stroop task and a modified task using emotional words, while EEG was recorded. Participants also completed self-report measures of adverse and positive childhood experiences, executive functioning, depression, current stress, and emotion regulation. Reports of adversity were robustly correlated with self-reported challenges in executive functioning, even when controlling for self-reported depression and stress, but adversity was not correlated with task performance. With regard to neural markers of error processing, adversity predicted an enhanced error-related negativity and blunted error-positivity, but only during the emotion-word blocks of the task. Moreover, error-related changes in alpha oscillations were predicted by adversity, in a pattern that suggested less error responsiveness in alpha patterns during the emotion block, compared to the color block, among participants with higher adversity. Overall, results indicate alterations in error monitoring associated with adversity, such that in an emotional context, initial error detection is enhanced and sustained error processing is blunted, even in the absence of overt performance changes.

Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm for feature selection

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The Chimp Optimisation Algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm’s performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

AO-SVM: a machine learning model for predicting water quality in the cauvery river

Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75 s, a precision of 93.9%, a recall rate of 95.1%, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.

Fast Sparse Discriminative K-Means for Unsupervised Feature Selection.

Embedded feature selection approach guides subsequent projection matrix (selection matrix) learning through the acquisition of pseudolabel matrix to conduct feature selection tasks. Yet the continuous pseudolabel matrix learned from relaxed problem based on spectral analysis deviates from reality to some extent. To cope with this issue, we design an efficient feature selection framework inspired by classical least-squares regression (LSR) and discriminative K-means (DisK-means), which is called the fast sparse discriminative K-means (FSDK) for the feature selection method. First, the weighted pseudolabel matrix with discrete trait is introduced to avoid trivial solution from unsupervised LSR. On this condition, any constraint imposed into pseudolabel matrix and selection matrix is dispensable, which is significantly beneficial to simplify the combinational optimization problem. Second, the l2,p -norm regularizer is introduced to satisfy the row sparsity of selection matrix with flexible p . Consequently, the proposed FSDK model can be treated as a novel feature selection framework integrated from the DisK-means algorithm and l2,p -norm regularizer to optimize the sparse regression problem. Moreover, our model is linearly correlated with the number of samples, which is speedy to handle the large-scale data. Comprehensive tests on various data terminally illuminate the effectiveness and efficiency of FSDK.