Original Task Research Articles

Scaffolding learning through reflection: Finnish language students recycling, negotiating, and reinterpreting instructions in a portfolio assignment

Students’ everyday life interactions in the wild are an important resource for their language learning, and reflection helps in utilizing the learning potential of these experiences. Students need scaffolding to benefit from reflection, and task design must support learners’ agency. These requirements suggest a need to examine and develop such reflection tasks. This paper examines a portfolio task developed by the teacher-researcher to enhance students’ learning in the wild as part of an U.S. university-level Finnish Studies program. Drawing on nexus analysis and using discourse analysis, the paper maps and analyzes how phrases from the instructions circulate to the subsequent reflections, and what the implications of this circulation are for the discourses created as well as for learning. The analysis reveals how the students recycle, negotiate, and reinterpret phrases from the original task, and how the task scaffolds the reflections. Pedagogical implications focus on how the task can be developed. The author advocates for the use of nexus analysis in teacher research to bridge research practice with task development and instructional change. As part of their training, pre-service teachers are recommended to collect and analyze student data to study the implications of the learning tasks they develop and use.

Auxiliary Feature Fusion and Noise Suppression for HOI Detection

In recent years, one-stage HOI (Human–Object Interaction) detection methods tend to divide the original task into multiple sub-tasks by using a multi-branch network structure. However, there is no sufficient attention to information communication between these branches. The inference approach in the cascaded structure is singular, while fully parallel methods will disrupt the associations between different pieces of information. Besides, noise interference may occur during the fusion of different features and thus affect the detection performance. To address these issues, this article proposes a one-stage three-branch parallel HOI detection method, which treats HOI as three separate sub-tasks (human detection, object detection, and interaction detection) and leverages three distinct reasoning relationships to generate richer relational information. Firstly , an auxiliary feature fusion (AFF) module is introduced, which integrates features originally extracted independently to form fused features enriched with supplementary information. This approach strengthens communication between branches in the network while handling the three sub-tasks concurrently, thereby facilitating the exchange of more contextual information. Secondly , to mitigate noise interference generated during the fusion process, a fusion noise suppression (FNS) module is introduced, which effectively suppresses noise and enhances the model’s performance in interaction detection tasks. Finally , experiments are conducted on two major benchmark datasets, and experimental results show that our HOI detection method is superior to previous methods. Also, ablation studies confirm the effectiveness of all the components in our proposed method.

A feedback matrix based evolutionary multitasking algorithm for high-dimensional ROC convex hull maximization

Multi-objective evolutionary algorithms have shown their competitiveness in solving ROC convex hull maximization. However, due to “the curse of dimensionality”, few of them focus on high-dimensional ROCCH maximization. Therefore, in this paper, a feedback matrix (FM)-based evolutionary multitasking algorithm, termed as FM-EMTA, is proposed. In FM-EMTA, to tackle “the curse of dimensionality”, a feature importance based low-dimensional task construction strategy is designed to transform the high-dimensional ROCCH maximization task into several low-dimensional tasks. Then, each low-dimensional task evolves with a population. To ensure that the low-dimensional task achieves a better ROCCH, an FM-based evolutionary multitasking operator is proposed. Specifically, for each low-dimensional task i, the element FM(i,j) in feedback matrix is defined to measure the degree that the low-dimensional task j could assist task i. Based on it, an FM-based assisted task selection operator and an FM-based knowledge transfer operator are developed to constitute the evolutionary multitasking operator, with which the useful knowledge is transferred among the low-dimensional tasks. After the evolution, the best ROCCHs obtained by the low-dimensional tasks are combined together to achieve the final ROCCH on the original high-dimensional task. Experiments on twelve high-dimensional datasets with different characteristics demonstrate the superiority of the proposed FM-EMTA over the state-of-the-arts in terms of the area under ROCCH, the hypervolume indicator and the running time.

Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis.

Multistate models provide a useful framework for modelling complex event history data in clinical settings and have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, we introduce novel Bayesian inference approaches for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. These approaches decompose the original estimation task into smaller inference blocks, leveraging parallel computing and facilitating flexible model specification and comparison. Using extensive simulation studies, we show that the proposed approaches achieve satisfactory estimation accuracy, with notable gains in computational efficiency compared to the standard Bayesian estimation strategy. We illustrate our approaches by analysing the coevolution of routinely measured systolic blood pressure and the progression of three important chronic conditions, using a large dataset from the Clinical Practice Research Datalink Aurum database. Our analysis reveals distinct and previously lesser-known association structures between systolic blood pressure and different disease transitions.

Improving Source Code Pre-training via Type-Specific Masking

The masked language modeling (MLM) task is widely recognized as one of the most effective pre-training tasks and currently derives many variants in the software engineering (SE) field. However, most of these variants mainly focus on code representation without distinguishing between different code token types, while some focus on a specific type, such as code identifiers. Indeed, various code token types exist, and there is no evidence that only identifiers can improve PTMs. Thus, to improve PTMs through different types, we conducted an extensive study to evaluate how different type-specific masking tasks can affect PTMs. First, we extract five code token types, convert them into type-specific masking tasks, and generate their combinations. Second, we pre-train CodeBERT and PLBART using combinations and fine-tuned them on four SE downstream tasks. Experimental results show that type-specific masking tasks can enhance CodeBERT and PLBART on all downstream tasks. Furthermore, we discuss topics related to low-resource datasets, conflicting PTMs that original pre-training tasks conflict with our methods, the cost and performance of our methods, factors that impact the performance of our methods, and applying our methods on state-of-the-art PTMs. These discussions comprehensively analyze the strengths and weaknesses of different type-specific masking tasks.

중등 예비교사의 삼각형의 외심 과제 설계 사례 분석: 정당화 과정을 중심으로

This study aims to examine how pre-service teachers design tasks that justify the circumcenter of a triangle based on given original tasks. Tasks designed by 28 secondary pre-service teachers were analyzed according to justification types derived from prior research. The findings revealed that pre-service teachers, while distinguishing between the justification of the existence and uniqueness of the circumcenter or modifying the order of defining the circumcenter, focused on ensuring that students could recognize and understand the argument for justification. This study suggests specific focal points that need to be emphasized in pre-service teacher education for both empirical and deductive justification of the circumcenter of a triangle.

The Impact of Episodic Memory on Decision-Making in Aging: Scenarios from Everyday Life Situations.

Background: Decision-making is a highly complex process that depends on numerous cognitive functions, such as episodic memory. It is also influenced by aging. However, how changes in episodic memory with age contribute to changes in decision-making is not clear yet. Objective: This work aimed to examine the role of two memory processes, recollection and familiarity, in decision-making in ageing. Method: Thirty young adults and 30 older adults performed two episodic memory tasks: recognition, which allowed for the measurement of recollection and familiarity, and recall, which allowed for the measurement of recollection. In both tasks, they first viewed a series of pictures and then were asked to recognise or recall them respectively. They also performed an original scenario task based on situations inspired by everyday life, evaluating decision-making under conditions of either risk or ambiguity. In this task, participants were presented with short descriptions of situations requiring a decision and had to choose between two given options. Results: Lower performances was observed in recall and recognition tasks in older than in young adults. In the scenarios task, young adults sought significantly more risk and ambiguity than older adults. In both young and older adults, recollection and familiarity processes were involved differently in decision-making. The former is more involved in decision-making under ambiguity, and the latter in decision-making under risk. Conclusions: The results suggest that decision-making changes with age, but that the involvement of the episodic memory, familiarity and recollection processes, does not appear to vary with age.

Difficulties in perceptual–motor coordination of reaching behavior in children with autism spectrum disorder

Increased risk of injury from collisions with objects is an important issue in children with autism spectrum disorder (ASD). The purpose of the present study was to examine whether impaired perceptual–motor coordination may underlie the high frequency of collisions. Specifically, we hypothesized that collisions are likely to occur frequently in children with ASD due to their difficulty with body-related spatial perception and their inadequate motor planning, due to their detail-focused processing style, i.e., a tendency to focus attention on specific details rather than on the overall context. To evaluate impairment of perceptual–motor coordination in children with ASD, an original action selection task was developed to assess (a) body-related spatial perception and (b) predictive attentional properties for planning the subsequent movements based on the measures of hand movement and gaze behavior. Twenty-six children (13 diagnosed as ASD and 13 with typical development [TD]) completed the task, and their parents completed a questionnaire regarding their child's susceptibility to injury. Results showed that children with ASD displayed inaccurate body-related spatial perception and inadequate motor planning under conditions that induced their detail-focused processing style. In addition, analyses of gaze behavior reflected the characteristics of a detail-focused processing in children with ASD. This was correlated with the severity of ASD and the measure of motor planning. The present findings suggest that difficulty with perceptual–motor coordination, resulting in part from the detail-focused processing style, might contribute to susceptibility to injury in children with ASD. We consider that our evaluation of the difficulty with perceptual–motor of individuals with ASD provides a framework for understanding their high rate of collision-related injuries and could inform strategies for preventing these injuries.

Distinct neural correlates of accuracy and bias in the perception of facial emotion expressions

ABSTRACT We investigated neural correlates of Emotion Recognition Accuracy (ERA) using the Assessment of Contextualized Emotions (ACE). ACE infuses context by presenting emotion expressions in a naturalistic group setting and distinguishes between accurately perceiving intended emotions (signal), and bias due to perceiving additional, secondary emotions (noise). This social perception process is argued to induce perspective taking in addition to pattern matching in ERA. Thirty participants were presented with an fMRI-compatible adaptation of the ACE consisting of blocks of neutral and emotional faces in single and group-embedded settings. Participants rated the central character’s expressions categorically or using scalar scales in consequent fMRI scans. Distinct brain activations were associated with the perception of emotional vs. neutral faces in the four conditions. Moreover, accuracy and bias scores from the original ACE task performed on another day were associated with brain activation during the scalar (vs. categorical) condition for emotional (vs. neutral) faces embedded in group. These findings suggest distinct cognitive mechanisms linked to each type of emotional rating and highlight the importance of considering cognitive bias in the assessment of social emotion perception.

Revisiting the moral forecasting error – A preregistered replication and extension of “Are we more moral than we think?”

Predictions are often inaccurate. Still, the direction of prediction errors may vary. Contrary to research on the intention-behavior gap, where people fail to live up to their ambitions, a study on “moral forecasting” found that people behaved more honestly than they predicted. In this registered report, we present two close replication attempts and one conceptual replication attempt of this moral forecasting error across two experiments. In Experiment 1 (N = 1839), we recruited a general population sample from the same country as the original study (Canada) to an online experiment. We successfully replicated the moral forecasting error using a math-based cheating task from the original study: Predicted cheating was much higher in a moral forecasting condition than actual cheating in a moral action condition (d = 0.69). In Experiment 2 (N = 1381) we replicated the forecasting error again, using the same task in a general population sample from the U.S. (d = 0.72). However, we were unable to conceptually replicate the effect using a different dishonesty measure, the “mind game”, in Experiment 1 (φ = 0.03). We also could not reduce the forecasting error through a debiasing intervention in Experiment 2 (d = 0.01). Across both experiments, participants predicted that others would cheat much more than they would themselves. In this registered report, we conclude that the moral forecasting error is robust for the original cheating task. We also show that it can generalize contextually (from a lab to an online setting), but not to a different task. Future research may show exactly when predictions about one's own honesty are pessimistic rather than optimistic.

The Effect of Screen Habits and Alternative Activities on Tactile Exploration Skills in 6- to 36-Month-Old Toddlers.

With the rising ubiquity of digital media and screens in everyday life, toddlers are increasingly exposed to different screens from an early age (i.e., television, computer, tablet, phone). However, few studies have examined the effect of these screens on toddlers' perceptual development. Since tactile exploration skills are necessary for environmental discovery and overall development, the current research investigates the links between screen-use habits and the tactile exploration skills (with visual control) of 6- to 36-month-old toddlers. The study involved observing the interactions of 135 toddlers with various objects and assessing the complexity of their visuo-tactile exploration strategies through two original experimental tasks. Data concerning screen habits and other relevant factors, such as socio-economic level, were collected using a parental questionnaire. Toddlers with greater screen exposure time demonstrated weaker tactile exploration skills and employed less age-appropriate exploration strategies. Socio-economic factors and parental engagement in alternative activities significantly influenced these developmental outcomes. These findings emphasize the importance of reducing screen time and promoting interactive co-viewing and alternative activities to mitigate the negative effects of screen exposure. Further longitudinal research is needed to determine the long-term impacts of early screen exposure on tactile exploration and overall psychological development.

Aтеология и критика религии в философии Мишеля Онфре

The article examines the atheological concept of “the most popular French philosopher” Michel Onfray, as well as his criticism of religion. Onfray borrows the term “atheology” from G. Bataille, which he understands as a current of thought that deconstructs theological truths. The philosopher identifies three original tasks of atheology: the deconstruction of monotheism, the demythologization of Judeo-Christianity and Islam, and the deconstruction of theocracy. He implements all these three tasks in one way or another. The article demonstrates that Onfray’s concept of atheology does not represent something radically new or significantly different from the existing tradition of religion critique. Onfray limits himself to the three monotheisms that have the greatest political influence and resolutely condemns almost all aspects of the three religions. We note that in doing so, Onfray simplifies the image of religion, presenting it exclusively in a negative light. Such negativism brings Onfray closer not only to contemporary new atheists, especially C. Hitchens, who believes that “religion poisons everything”, but also to such representatives of French materialism as Holbach and La Mettrie. Onfray’s approach seems abstract because it does not provide a thorough socio-historical explanation of monotheistic religions. It is shown that his criticism of religion is based on a significant philosophical tradition that goes from Democritus, Lucretius, Epicurus, to Freud, Marx, and Nie­tzsche, and includes French materialists such as Holbach, La Mettrie, Helvetius. His hedonism is more likely to be drawn from the Cyrenaics than from Epicurus. The originality of Onfray’s approach lies in the fact that he incorporates materialism and hedonism into his concept of “philosophy of nature”. Onfray’s positive agenda, although resonant with a number of contemporary issues, is formulated in extremely general terms and is poorly worked out

Improving biomedical entity linking for complex entity mentions with LLM-based text simplification.

Large amounts of important medical information are captured in free-text documents in biomedical research and within healthcare systems, which can be made accessible through natural language processing (NLP). A key component in most biomedical NLP pipelines is entity linking, i.e. grounding textual mentions of named entities to a reference of medical concepts, usually derived from a terminology system, such as the Systematized Nomenclature of Medicine Clinical Terms. However, complex entity mentions, spanning multiple tokens, are notoriously hard to normalize due to the difficulty of finding appropriate candidate concepts. In this work, we propose an approach to preprocess such mentions for candidate generation, building upon recent advances in text simplification with generative large language models. We evaluate the feasibility of our method in the context of the entity linking track of the BioCreative VIII SympTEMIST shared task. We find that instructing the latest Generative Pre-trained Transformer model with a few-shot prompt for text simplification results in mention spans that are easier to normalize. Thus, we can improve recall during candidate generation by 2.9percentage points compared to our baseline system, which achieved the best score in the original shared task evaluation. Furthermore, we show that this improvement in recall can be fully translated into top-1 accuracy through careful initialization of a subsequent reranking model. Our best system achieves an accuracy of 63.6% on the SympTEMIST test set. The proposed approach has been integrated into the open-source xMEN toolkit, which is available online via https://github.com/hpi-dhc/xmen.

Copyright protection framework for federated learning models against collusion attacks

Federated learning (FL) models are constructed by multiple participants who provide their training datasets and collaborate in joint training. However, training and deployment processes have encountered various challenges in terms of intellectual property protection, such as illegal theft and data leakage. Existing FL protection frameworks focus on each client independently and verify the model ownership. When they are under collusion attacks (i.e., when multiple clients negotiate to steal together), they cannot accurately identify the clients that have stolen the model. To address this challenge, a novel watermarking protection scheme against collusion attacks for federated learning is proposed in this work. It employs anti-collusion coding to design unique watermark information for each client, which can effectively detect colluders. Furthermore, it utilizes a specific regularized loss function for watermark information embedding along with the incorporation of skip connections to embed the watermark information within each batch normalization layer. The experimental results demonstrated that embedding different watermark information into each client did not affect the accuracy of the original task. The accuracy was approximately 100% when identifying the colluders. The embedding and extraction times for the original task were only 1.53% and 0.29%, respectively. Further, it exhibited high robustness against various common attacks, including fine-tuning, shearing, and collusion attacks.

ECG signal fusion reconstruction via hash autoencoder and margin semantic reinforcement

The ECG signal is often accompanied by noise, which can affect its shape characteristics, so it is important to perform signal de-noising. However, the commonly used signal noise reduction methods, such as wavelet or filter transformation, often prioritize high-frequency signals over low-frequency ones, leading to the loss of low-frequency band features or difficulties in capturing them. We propose a fusion reconstruction framework that combines hash autoencoder and margin semantic reinforcement to enhance low-frequency band features. Specifically, for labeled samples, margin semantic reinforcement identifies and corrects weight discrepancies among bands with similar waveforms but different labels to amplify the low-frequency signals associated with the label and reduce irrelevant ones. Meanwhile, hash autoencoder utilizes a semantic hash dictionary to reconstruct the original signal and mitigate noise pollution. For unlabeled samples, the hash autoencoder is utilized to generate pseudo-labels, followed by the reproduction of the aforementioned enhanced reconstruction process. The final step involves weighting the two types of signals, enhanced with margin semantics and hash autoencoder reconstruction, to achieve the reconstruction objective of the original signal, facilitating recognition and detection tasks. Experiments conducted on different classical classifiers demonstrate that the reconstructed ECG signals can significantly improve their performance.

Stereoisomers Are Not Machine Learning's Best Friends.

This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisomers is indeed crucial for machine learning applications. Current tools offer various molecular descriptors, including their textual representation as Isomeric SMILES that can distinguish stereoisomers. However, such representation is text-based and does not have a fixed size, so a conversion is needed to make it usable to machine learning approaches. Word embedding techniques can be used to solve this problem. Mol2vec, a word embedding approach for molecules, offers such a conversion. Unfortunately, it cannot distinguish between stereoisomers due to its inability to capture the spatial configuration of molecular structures. This study proposes several approaches that use word embedding techniques to handle molecular discrimination using stereochemical information on molecules or considering Isomeric SMILES notation as a text in Natural Language Processing. Our aim is to generate a distinct vector for each unique molecule, correctly identifying stereoisomer information in cheminformatics. The proposed approaches are then compared to our original machine learning task: predicting the association constant between cyclodextrin and a guest molecule.

Материалистическая философия Мишеля Онфре

The article examines the atheological concept of “the most popular French philosopher” Michel Onfray, as well as his criticism of religion. Onfray borrows the term “atheology” from G. Bataille, which he understands as a current of thought that deconstructs theological truths. The philosopher identifies three original tasks of atheology: the deconstruction of monotheism, the demythologization of Judeo-Christianity and Islam, and the deconstruction of theocracy. He implements all these three tasks in one way or another. The article demonstrates that Onfray’s concept of atheology does not represent something radically new or significantly different from the existing tradition of religion critique. Onfray limits himself to the three monotheisms that have the greatest political influence and resolutely condemns almost all aspects of the three religions. We note that in doing so, Onfray simplifies the image of religion, presenting it exclusively in a negative light. Such negativism brings Onfray closer not only to contemporary new atheists, especially C. Hitchens, who believes that “religion poisons everything”, but also to such representatives of French materialism as Holbach and La Mettrie. Onfray’s approach seems abstract because it does not provide a thorough socio-historical explanation of monotheistic religions. It is shown that his criticism of religion is based on a significant philosophical tradition that goes from Democritus, Lucretius, Epicurus, to Freud, Marx, and Nietzsche, and includes French materialists such as Holbach, La Mettrie, Helvetius. His hedonism is more likely to be drawn from the Cyrenaics than from Epicurus. The originality of Onfray’s approach lies in the fact that he incorporates materialism and hedonism into his concept of “philosophy of nature”. Onfray’s positive agenda, although resonant with a number of contemporary issues, is formulated in extremely general terms and is poorly worked out.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

Point cloud registration via sampling-based evolutionary multitasking

Point cloud registration (PCR) is an important task in both computer vision and pattern recognition. However, since the success of most existing two-stage PCR methods largely depends on the implicit assumption that the first coarse registration stage can provide an approximately optimal initial transformation for the second fine stage, these methods suffer from low success rate when the assumption is not satisfied. Evolutionary multitasking can find better solutions for multiple tasks simultaneously by transferring knowledge among multiple related tasks. Inspired by evolutionary multitasking, this study proposes a sampling-based multitask optimization method for PCR, which solves the problem of low registration success rate by sharing knowledge between two related tasks generated by a dataset. Specifically, one task is for roughly aligning the sampled point cloud, and the other is for the original unsampled point cloud. The main purpose of the first task is to continuously provide useful knowledge to the second task so that it can achieve precise alignment. Then, a new knowledge transfer mechanism guided by historical information is proposed, which can effectively adjust the search behavior of the original unsampled task. In addition, we develop a dynamic task complement strategy to complement the accuracy that may be lost by downsampling. The results on five different datasets show that the proposed method can achieve higher registration accuracy with higher success rate compared to other state-of-the-art PCR methods.

'Ripple effects' of urban environmental characteristics on cognitive performances in Eurasian red squirrels.

Urban areas are expanding exponentially, leading more species of wildlife living in urban environments. Urban environmental characteristics, such as human disturbance, induce stress for many wildlife and have been shown to affect some cognitive traits, such as innovative problem-solving performance. However, because different cognitive traits have common cognitive processes, it is possible that urban environmental characteristics may directly and indirectly affect related cognitive traits (the ripple effect hypothesis). We tested the ripple effect hypothesis in urban Eurasian red squirrels residing in 11 urban areas that had different urban environmental characteristics (direct human disturbance, indirect human disturbance, areas of green coverage and squirrel population size). These squirrels were innovators who had previously repeatedly solved a food extraction task (the original task). Here, we examined whether and how urban environmental characteristics would directly and indirectly influence performance in two related cognitive traits, generalisation and (long-term) memory. The generalisation task required the innovators to apply the learned successful solutions when solving a similar but novel problem. The memory task required them to recall the learned solution of the original task after an extended period of time. Some of the selected urban environmental characteristics directly influenced the task performance, both at the population level (site) and at individual levels. Urban environmental characteristics, such as increased direct and indirect human disturbance, decreased the proportion of success in solving the generalisation task or the memory task at the population (site) level. Increased direct human disturbance and less green coverage increased the solving efficiency at individual levels. We also found an indirect effect in one of the urban environmental characteristics, indirect human disturbance, in the generalisation task, but not the memory task. Such an effect was only seen at the individual level but not at the population level; indirect human disturbance decreased the first original latency, which then decreased the generalisation latency across successes. Our results partially support the ripple effect hypothesis, suggesting that urban environmental characteristics are stressors for squirrels and have a greater impact on shaping cognitive performance than previously shown. Together, these results provide a better understanding of cognitive traits that support wildlife in adapting to urban environments.