Traditional Tasks Research Articles

Learners' causal intuitions explain behavior in control of variables tasks.

Self-directed learners are described as "intuitive scientists," yet they often struggle in assessments of their scientific reasoning skills. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry behavior. Specifically, we consider whether learners' documented failure to correctly apply the control of variables strategy might stem from a mismatch between task presentation and their intuitions as causal learners. In Experiment 1, children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that was modified to clarify ambiguous elements of the causal logic in the original design. In all age groups, a significant majority of participants selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. In Experiment 2, we replicate these results with real-world stimuli, and in Experiments 3 and 4, we provide direct evidence that self-directed learners apply specific causal intuitions to experimentation tasks. Together, these findings support a novel alternative interpretation of the apparently paradoxical gap between learners' success in informal exploration and their error-prone experimentation-both behaviors are consistent with an intuitively causal approach to scientific inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Čimbenici rizika i učestalost mišićno-koštanih tegoba radnika pri radu u tehnici batika uzrokovanih položajem tijela i rada mišića ruku

The batik industry as one of Malaysian cultural heritage is mainly located on the East Coast of Malaysia, particularly in the states of Kelantan and Terengganu. Most of the industry is home-based. Hence, the traditional batik work environment and tasks impose threats to the well-being of the artisans. This study investigates ergonomic issues and the prevalence of musculoskeletal disorders in two of Kelantan’s batik industries. The artisans’ natural working postures and the environments of the two places were observed and video recorded. Muscle activations on the wrist of the artisan doing the paint (canting) were imitated. Surface electromyography (sEMG) investigations were performed in the laboratory. A questionnaire survey was also distributed among craftsmen and artisans involved in the batik industry in Kelantan. Results illustrate evidence of the need to educate batik industries on ergonomic risk factors. According to the survey of 33 respondents, the most affected body parts are the waist (60%), back and feet (57%). The walkthroughs and site visits showed that batik making process demands awkward postures which include standing, precision handling of hand tools, trunk bending & rotating and overreaching repetitively between 4 to 8 hours. The Rapid Upper Limb Assessment (RULA) index obtained from the assessments ranges from 7-8. It was also shown from the sEMG data that trunk bending affects muscle contraction of the wrist during painting movement. Therefore, immediate interventions are required to improve batik artisans’ postural working conditions, especially on the neck, trunk, legs, wrists and arms.

Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval.

The traditional 3D object retrieval (3DOR) task is under the close-set setting, which assumes the categories of objects in the retrieval stage are all seen in the training stage. Existing methods under this setting may tend to only lazily discriminate their categories, while not learning a generalized 3D object embedding. Under such circumstances, it is still a challenging and open problem in real-world applications due to the existence of various unseen categories. In this paper, we first introduce the open-set 3DOR task to expand the applications of the traditional 3DOR task. Then, we propose the Hypergraph-Based Multi-Modal Representation (HGM 2 R) framework to learn 3D object embeddings from multi-modal representations under the open-set setting. The proposed framework is composed of two modules, i.e., the Multi-Modal 3D Object Embedding (MM3DOE) module and the Structure-Aware and Invariant Knowledge Learning (SAIKL) module. By utilizing the collaborative information of modalities derived from the same 3D object, the MM3DOE module is able to overcome the distinction across different modality representations and generate unified 3D object embeddings. Then, the SAIKL module utilizes the constructed hypergraph structure to model the high-order correlation among 3D objects from both seen and unseen categories. The SAIKL module also includes a memory bank that stores typical representations of 3D objects. By aligning with those memory anchors in the memory bank, the aligned embeddings can integrate the invariant knowledge to exhibit a powerful generalized capacity toward unseen categories. We formally prove that hypergraph modeling has better representative capability on data correlation than graph modeling. We generate four multi-modal datasets for the open-set 3DOR task, i.e., OS-ESB-core, OS-NTU-core, OS-MN40-core, and OS-ABO-core, in which each 3D object contains three modality representations: multi-view, point clouds, and voxel. Experiments on these four datasets show that the proposed method can significantly outperform existing methods. In particular, the proposed method outperforms the state-of-the-art by 12.12%/12.88% in terms of mAP on the OS-MN40-core/OS-ABO-core dataset, respectively. Results and visualizations demonstrate that the proposed method can effectively extract the generalized 3D object embeddings on the open-set 3DOR task and achieve satisfactory performance.

Memory deficit in patients with cerebral small vessel disease: evidence from eye tracking technology.

Cerebral small vessel disease is the one of the most prevalent causes of vascular cognitive impairment. We aimed to find objective and process-based indicators related to memory function to assist in the detection of memory impairment in patients with cerebral small vessel disease. Thirty-nine cerebral small vessel disease patients and 22 healthy controls were invited to complete neurological examinations, neuropsychological assessments, and eye tracking tasks. Eye tracking indicators were recorded and analyzed in combination with imaging features. The cerebral small vessel disease patients scored lower on traditional memory task and performed worse on eye tracking memory task performance compared to the healthy controls. The cerebral small vessel disease patients exhibited longer visit duration and more visit count within areas of interest and targets and decreased percentage value of total visit duration on target images to total visit duration on areas of interest during decoding stage among all levels. Our results demonstrated the cerebral small vessel disease patients performed worse in memory scale and eye tracking memory task, potentially due to their heightened attentional allocation to nontarget images during the retrieval stage. The eye tracking memory task could provide process-based indicators to be a beneficial complement to memory assessment and new insights into mechanism of memory impairment in cerebral small vessel disease patients.

A post-processing framework for class-imbalanced learning in a transductive setting

Traditional classification tasks suffer from the class-imbalanced problem, where some classes far outnumber others. To address this issue, existing class-imbalanced learning (CIL) methods either preprocess class-imbalanced datasets or adapt traditional classification algorithms to the imbalanced class distribution. Inspired by the idea of transductive learning, we propose a post-processing framework called PPF for CIL. Distinct from existing CIL methods, PPF directly adjusts the predicted labels of test data to fit the imbalanced class distribution. Specifically, we relabel some test data according to their prediction probabilities so that the class proportion of test data is close to that of training data. The underlying assumption is that training and test data, drawn independently from one data space, should obey the same class distribution. Furthermore, we propose a Compact Prototype-based Nearest Neighbor (CPNN) algorithm to assist the original classifier with the adjustment. Instead of training a classifier, CPNN classifies test data according to their distances to a set of prototypes estimated on labeled data. Thus, it is computationally simple and relatively robust to class imbalance. As a general framework, PPF can be easily applied to both traditional classification and CIL algorithms. To validate the effectiveness of the proposed method, we conducted extensive experiments on a variety of class-imbalanced datasets, using SVM and C4.5 as the original classifiers, respectively. Measured by F-measure, G-mean, and AUC, both PPF-SVM and PPF-C4.5 outperform 10 state-of-the-art CIL algorithms. Additionally, PPF further improved their performances when applied to 10 CIL algorithms.

Dynamic Merging for Optimal Onboard Resource Utilization: Innovating Mission Queue Constructing Method in Multi-Satellite Spatial Information Networks

The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task scheduling, aiming at optimizing the efficiency of single-task execution, while neglecting the complex dependencies that might exist between multiple tasks and payloads. Moreover, traditional task scheduling schemes are no longer suitable for large-scale tasks. To effectively reduce the number of tasks within the network, we introduce a network aggregation graph model based on multiple satellites and tasks, and propose a task aggregation priority dynamic calculation algorithm based on graph computations. Subsequently, we present a dynamic merging-based method for multi-satellite, multi-task aggregation, a novel approach for constructing onboard mission queues that can dynamically optimize the task queue according to real-time task demands and resource status. Simulation experiments demonstrate that, compared to baseline algorithms, our proposed task aggregation method significantly reduces the task size by approximately 25% and effectively increases the utilization rate of onboard resources.

NuScenes-QA: A Multi-Modal Visual Question Answering Benchmark for Autonomous Driving Scenario

We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated programmatically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://github.com/qiantianwen/NuScenes-QA.

ESRL: Efficient Sampling-Based Reinforcement Learning for Sequence Generation

Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (e.g., BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences. This is a computational challenge as presented by the practice of sequence generation problems, such as machine translation, where we often deal with a large action space (e.g., a vocabulary) and a long action sequence (e.g., a translation). In this work, we introduce two-stage sampling and dynamic sampling approaches to improve the sampling efficiency during training sequence generation models via RL. We experiment with our approaches on the traditional sequence generation tasks, including machine translation and abstractive summarization. Furthermore, we evaluate our approaches in RL from human feedback (RLHF) through training a large language model using the reward model. Experimental results show that the efficient sampling-based RL, referred to as ESRL, can outperform all baselines in terms of both training efficiency and memory consumption. Notably, ESRL yields consistent performance gains over the strong REINFORCE, minimum risk training, and proximal policy optimization methods. The code is available at https://github.com/wangclnlp/DeepSpeed-Chat-Extension/examples/esrl.

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the generalization ability of the model. Different from existing incomplete multi-view completion methods, we attempt to approximate the latent features of missing instances in embedding space according to cross-view joint attention, instead of recovering missing views in kernel space or original feature space. Accordingly, multi-view completed features are dynamically weighted by the confidence derived from joint attention in the late fusion phase. In addition, we propose a multi-view multi-label classification framework based on label-semantic feature learning, utilizing the statistical weak label correlation matrix and graph attention network to guide the learning process of label-specific features. Finally, our model is compatible with missing multi-view and partial multi-label data simultaneously and extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model.

Can Large Language Models Understand Real-World Complex Instructions?

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs’ ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO.

Aspect-Based Sentiment Analysis of Specific Targets

Different from traditional sentiment analysis tasks, Aspect-Based Sentiment Analysis (ABSA) aims to automatically identify the emotional polarity of different aspects in the same sentence, which helps to mine users' more delicate emotional expressions for different targets and has become a research hotspot in the field of natural language processing in recent years. Thanks to the rapid development of attention-based deep neural network models, the accuracy of aspect sentiment analysis has continuously made breakthroughs. However, existing works pay little attention to the performance bounds of different application scenarios, such as different text topics or text lengths. In response to the above issues, this paper conducts a comparative analysis of several classic end-to-end neural network models to explore topic-level sentiment polarity analysis. By quantitatively evaluating the accuracy and F1 score of each model at different fixed text lengths, this paper summarizes the strengths, weaknesses, and different aspects of each model's efficiency in use. In addition, this paper also discusses the existing problems with these models and puts forward suggestions for improvement, which can provide some new insights into the research of ABSA.

Development And Challenges of Generative Artificial Intelligence in Education and Art

Thanks to the rapid development of generative deep learning models, Artificial Intelligence Generated Content (AIGC) has attracted more and more research attention in recent years, which aims to learn models from massive data to generate relevant content based on input conditions. Different from traditional single-modal generation tasks that focus on content generation for a particular modality, such as image generation, text generation, or semantic generation, AIGC trains a single model that can simultaneously understand language, images, videos, audio, and more. AIGC marks the transition from traditional decision-based artificial intelligence to generative artificial intelligence, which has been widely applied in various fields. Focusing on the key technologies and representative applications of AIGC, this paper identifies several key technical challenges and controversies in the field. These include defects in cross-modal and multimodal generation, issues related to model stability and data consistency, privacy concerns, and questions about whether advanced generative models like ChatGPT can be considered general artificial intelligence (AGI). While this dissertation provides valuable insights into the revolution and challenge of generative AI in art and education, it acknowledges the sensitivity of generated content and the ethical dilemmas it may pose, and ownership rights for AI-generated works and the need for new intellectual property norms are subjects of ongoing discussion. To address the current technical bottlenecks in cross-modal and multimodal generation, future research aims to quantitatively analyze and compare existing models, proposing practical optimization strategies. With the rapid advancement of generative AI, we anticipate a transition from user-generated content (UGC) to artificial intelligence-generated content (AIGC) and, ultimately, a new era of human-computer co-creation with strong interactive potential in the near future.

Business without Management: MacIntyrean Accounting, Management, and Practice-Led Business

Alasdair MacIntyre’s critique of managerial capitalism is well known, with some arguing that MacIntyrean thought is antithetical to contemporary capitalist business. Nevertheless, substantial efforts have been taken to demonstrate how different business activities constitute MacIntyrean practices, which points to an incoherence at the heart of MacIntyrean business ethics scholarship. This article proposes a way of bridging these perspectives, suggesting a reimagined MacIntyrean approach to business that is thoroughly ‘practice-led.’ A detailed comparison of accounting and management shows that while neither are practices in ‘good order,’ they differ in significant ways: where management does not meet the criteria for a MacIntyrean practice, accounting is a ‘distorted’ practice. This leads to a categorisation of practice-led business activity, whereby the traditional tasks of management are subsumed, shared or subordinated to practices and practitioners. Insights on how this can be implemented are drawn from the ‘communities of practice’ literature and a consideration of professions.

A Distributed Deep Learning Network Based on Data Enhancement for Few-Shot Raman Spectral Classification of Litopenaeus vannamei Pathogens

Litopenaeus vannamei is a common species in aquaculture and has a high economic value. However, Litopenaeus vannamei are often invaded by pathogenic bacteria and die during the breeding process, so it is of great significance to study the identification of shrimp pathogenic bacteria. The wide application of Raman spectroscopy in identifying directions of inquiry provides a new means for this. However, the traditional Raman spectroscopy classification task requires a large amount of data to ensure the accuracy of its classification. Therefore, the question of how to obtain higher classification accuracy through the means of a small amount of Raman spectrum data is a difficult point in the research. This paper proposes a distributed deep learning network based on data enhancement for few-shot Raman spectral classification of Litopenaeus vannamei pathogens. The network consists of RSEM, RSDM, and DLCM modules. The RSEM module uses an improved generative adversarial network combined with transfer learning to generate a large amount of spectral data. The RSDM module uses improved U-NET to denoise the generated data. In addition, we designed a distributed learning classification model (DLCM) which significantly speeds up model training, improves the efficiency of the algorithm, and solves the network degradation problem that often occurs during deep learning model training. The average classification accuracy of our proposed network on four shrimp pathogenic bacteria reaches 98.9%, which is higher than several models commonly used in Raman spectroscopy classification tasks. The method proposed in this article only needs the Raman spectra of a small number of microorganisms to complete the efficient and rapid identification of shrimp pathogenic bacteria, and this method certainly has the potential to solve the problem of the spectral classification of other microorganisms.

The Long and Winding Road to Real-Life Experiments: Remote Assessment of Executive Functions with Computerized Games-Results from 8 Years of Naturalistic Interventions.

Mate Marote is an open-access cognitive training software aimed at children between 4 and 8 years old. It consists of a set of computerized games specifically tailored to train and evaluate Executive Functions (EF), a class of processes critical for purposeful, goal-directed behavior, including working memory, planning, flexibility, and inhibitory control. Since 2008, several studies were performed with this software at children's own schools in interventions supervised in-person by cognitive scientists. After 2015, we incorporated naturalistic, yet controlled, interventions with children's own teachers' help. The platform includes a battery of standardized tests, disguised as games, to assess children's EF. The main question that emerges is whether the results, obtained with these traditional tasks but conducted without the presence of researchers, are comparable to those widely reported in the literature, that were obtained in more supervised settings. In this study, we were able to replicate the expected difficulty and age effects in at least one of the analyzed dependent variables of each employed test. We also report important discrepancies between the expected and the observed response time patterns, specifically for time-constrained tasks. We hereby discuss the benefits and setbacks of a new possible strategy for this type of assessment in naturalistic settings. We conclude that this battery of established EF tasks adapted for its remote usage is appropriate to measure the expected mental processes in naturalistic settings, enriching opportunities to upscale cognitive training interventions at schools. These types of tools can constitute a concerted strategy to bring together educational neuroscience research and real-life practice.

Industrial Engineering Using IBM SPSS Statistics

Industrial Engineering, Most historians agree that the Industrial Revolution is when the profession of industrial engineering had its start. The invention, the spinning jenny, and—possibly most significantly—the steam engine were among the innovations that helped mechanize traditional manual tasks in the textile industry. These innovations also created scale savings that for the first time that made commercial production in centralized facilities appealing. The factories built as a result of these developments are where the idea of something like the production system originated. Also, it has been proposed that Leonardo Vinci may have been the first manufacturing engineer as there is proof that he used science to analyze human labor by observing how quickly a laborer could shovel soil approximately in 1500.Both the producers and the final consumers of a product value industrial engineering. To improve efficiency, industrial engineers enhance designs and procedures. This benefits businesses since it conserves people, raw materials, time, and money. IBM SPSS Statistics Your firm can easily derive useful insights from your data because to its user-friendly interface and extensive feature set. High accuracy and good decision-making are ensured by advanced statistical techniques. The entire analytics lifecycle is covered, from data administration and preparation to reporting and analysis. Perceived quality, Customer expectations, Perceived value, Customer satisfaction, Customer complaints, and Customer loyalty. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .581 which indicates 58% reliability. From the literature review, the above 46% Cronbach's Alpha value model can be considered for analysis. the outcome of Cronbach's Alpha Reliability. The model's total Cronbach's Alpha score is .581, which denotes a 58% dependability level. The 46% Cronbach's Alpha value model mentioned above from the literature review may be used for analysis.

A matter of precision? Scene imagery in individuals with high-functioning autism spectrum disorder.

The ability to create mental representations of scenes is essential for remembering, predicting, and imagining. In individuals with autism spectrum disorders (ASD) this ability may be impaired. Considering that autistic characteristics such as weak central coherence or reduced communication abilities may disadvantage autistic participants in traditional imagery tasks, this study attempted to use a novel task design to measure the ability of scene imagery. Thirty high-functioning adults with ASD and 27 non-autistic matched control adults were asked to describe imagined fictitious scenes using two types of scene imagery tasks. In a free imagery task, participants were asked to imagine a scene based on a given keyword. In a guided imagery task, participants had to imagine a scene based on a detailed description of the scene. Additionally, narrative abilities were assessed using the Narrative Scoring Scheme. Statistical analyses revealed no group effects in the free and guided imagery of fictional scenes. Participants with ASD performed worse than control participants in the narrative task. Narrative abilities correlated positively with performance in both imagery tasks in the ASD group only. Hence, individuals with ASD seem to show as good imagery abilities as non-autistic individuals. The results are discussed in the light of the differences between imagery and imagination and possible gender differences.

Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach

<p>Hyperparameter tuning plays a significant role when building a machine learning or a deep learning model. The tuning process aims to find the optimal hyperparameter setting for a model or algorithm from a pre-defined search space of the hyperparameters configurations. Several tuning algorithms have been proposed in recent years and there is scope for improvement in achieving a better exploration-exploitation tradeoff of the search space. In this paper, we present a novel hyperparameter tuning algorithm named adaptive Bayesian contextual hyperband (Adaptive BCHB) that incorporates a new sampling approach to identify best regions of the search space and exploit those configurations that produce minimum validation loss by dynamically updating the threshold in every iteration. The proposed algorithm is assessed using benchmark models and datasets on traditional machine learning tasks. The proposed Adaptive BCHB algorithm shows a significant improvement in terms of accuracy and computational time for different types of hyperparameters when compared with state-of-the-art tuning algorithms.</p>

Modeling and Control Strategies for Flight Service Vehicle Queue Behavior in Non Cooperative Games

From the perspective of the running process for service vehicles, based on the analysis of the benefits maximization of the special needs vehicles joining the queue as the scheduling goal, and aiming at the shortcomings of traditional task scheduling strategies, methods and models, a non-cooperative game behavior optimization scheduling model with security, economy and environmental protection as the optimization goals was proposed by using game theory. At the same time, the vehicle service capability, service times and import Angle are limited to ensure that the model is more in line with the actual operation scenario. In order to judge the deviation between the game result and the actual operation, the game efficiency matrix is constructed, and Nash equilibrium is used to prove that the model has an equilibrium point. On the basis of the game model, the strategy layer, the game layer and the optimization layer are divided into queue behavior control strategy methods. Finally, this paper verifies the feasibility of the model by using the actual parameter changes to determine the best parameter combination suitable for the model.

Bridging the Gap Between Self-Report and Behavioral Laboratory Measures: A Real-Time Driving Task With Inverse Reinforcement Learning

A major challenge in assessing psychological constructs such as impulsivity is the weak correlation between self-report and behavioral task measures that are supposed to assess the same construct. To address this issue, we developed a real-time driving task called the “highway task,” in which participants often exhibit impulsive behaviors mirroring real-life impulsive traits captured by self-report questionnaires. Here, we show that a self-report measure of impulsivity is highly correlated with performance in the highway task but not with traditional behavioral task measures of impulsivity (47 adults aged 18–33 years). By integrating deep neural networks with an inverse reinforcement learning (IRL) algorithm, we inferred dynamic changes of subjective rewards during the highway task. The results indicated that impulsive participants attribute high subjective rewards to irrational or risky situations. Overall, our results suggest that using real-time tasks combined with IRL can help reconcile the discrepancy between self-report and behavioral task measures of psychological constructs.