Search Results (22)

First-year computer-science students judged their grade on two assessments differing in judgment type: a concurrent self-assessment embedded in an early quiz (Assessment 1, $N=101$) and a postdiction after a mid-course written-and-laboratory exam (Assessment 2, $N=117$), with 94 students linked across both. The concurrent judgment was optimistic (mean signed error $+9.10$ pp; $70.3\%$ overestimators; Spearman $\rho =0.533$). The postdictions reversed the bias: students underestimated the written component by $10.08$ pp and the laboratory by $4.21$ pp, rank-order calibration being markedly better for the laboratory task ($\rho =0.804$). In the linked subsample, the reversal was large (paired $d_z=1.00$) but absolute error did not improve ($p=0.582$): the error changed direction, not magnitude. A tertile split shows broad-based quiz optimism and, on the postdictions, a gradient compatible with regression to the mean. An exploratory post hoc re-banding of the laboratory scores, excluding the administrative 0–1 segment, yields a Dunning–Kruger-compatible between-band difference of $1.80$ raw points (95% CI $[0.94,2.66]$, $p=0.0001$), robust to alternative bandings, though the low-band overestimation is not. The Assessment 1 overestimation replicates in two further cohorts. Calibration therefore differed markedly across two contexts that differ simultaneously in judgment type, timing, format, content and diagnostic cues, which these data cannot disentangle. Full article

An emerging body of research has shown that requiring participants to make concurrent JOLs during learning can reactively influence memory performance, a phenomenon known as the reactivity effect. However, it remains unclear whether making JOLs can also reactively alter metacognitive control. The present study addressed this question across three types of materials: unrelated word pairs, general knowledge questions, and related word pairs. After studying each item, participants decided whether they would choose to restudy it. In JOL conditions, participants made an item-by-item JOL during learning, while in no-JOL conditions, no such judgment was required. Across all materials, making JOLs reliably increased the proportion of items selected for restudy, indicating a robust reactivity effect on metacognitive control. These findings suggest that JOL reactivity should be considered not only in relation to memory performance but also in relation to metacognitive control. Full article

Understanding how users perceive and attend to the anthropomorphic appearance and empathic responses of artificial intelligence medical conversational agents (AIMCAs) can help reveal the key judgment cues underlying trust formation and use decisions, while also informing interface and dialog design. To this end, this study employs a 3 (appearance anthropomorphism: high, medium, low) × 2 (empathic response: present, absent) within-subject eye-tracking experiment, combined with subjective scales and brief post-task open-ended feedback. During a static prototype viewing task based on hypothetical consultation scenarios, we concurrently recorded trust, behavioral intention, and visual measures for key areas of interest (AOIs; appearance area, conversational content area, and overall interface area). Eye-tracking measures were normalized by AOI coverage proportion to improve cross-AOI comparability. The results show that both anthropomorphic appearance and empathic response significantly increased users’ trust in AIMCAs and their behavioral intention. An interaction between these two types of social cues was also observed, suggesting that when visual embodiment and linguistic style are aligned at the social level, users are more likely to form favorable overall judgments. At the level of visual processing, however, no interaction effect was found, and the eye-tracking measures showed only partial main effects, indicating that subjective synergy does not necessarily correspond to synergistic changes in attentional allocation. Overall, anthropomorphic appearance and empathic response exerted consistent facilitating effects on outcome variables, but displayed different patterns of attentional allocation and information prioritization at the visual level. Accordingly, AIMCA design should emphasize consistency between appearance cues and conversational strategies, optimize users’ initial judgments and interface comprehension, and use intention through verifiable information organization and clear boundary cues. Full article

Comprehensive soil surveys necessitate the integration of multidimensional pedological information, ranging from the morphological delineation of horizons and the taxonomic identification of soil groups to the quantitative assessment of soil organic matter (SOM). These attributes collectively constitute the basis for interpreting pedogenesis and guiding sustainable soil management. However, conventional methods are limited by the subjectivity of expert judgment for horizon and soil group identification, and the time-consuming nature of laboratory analyses for SOM quantification. We developed a novel multimodal deep learning framework based on an improved Mask R-CNN architecture that integrates soil profile images with auxiliary soil property data to concurrently delineate soil horizons, classify soil groups, and quantify SOM. The model was trained on high-resolution soil profile images from 451 soil survey sampling sites spanning ten soil groups across Anhui Province, China. Data augmentation and transfer learning with pre-training on large general image datasets were employed to address the dataset size limitations and improve model generalization. In addition to accurately delineating master horizons, we evaluated three schemes for classifying transitional horizons, which are often ambiguously determined by expert assessments: (i) assigning the transitional horizon to one adjacent master horizon; (ii) assigning it to both neighboring master horizons as an overlapping section; and (iii) treating the transitional horizon as an independent layer. Scheme (iii) achieved the best overall performance, e.g., horizon delineation with accuracy = 0.925, recall = 0.933, F1-score = 0.929, and segmentation mean average precision (seg-mAP) = 0.918, soil group classification accuracy = 0.717 and prediction of SOM with R² = 0.565. These results demonstrate that treating transitional horizons as independent layers yields superior segmentation. Consequently, this integrated framework provides a robust, automated solution for high-throughput soil resource assessment. Full article

Internet of Things (IoT) applications often require only minimal necessary information—such as threshold judgments, binning, or prefixes—yet they must control privacy leakage arising from multi-round and cross-entity access without exposing raw values. Existing solutions, however, frequently rely on ciphertext structures and server-side states, making it difficult to define a leakage upper bound for restricted answers in the sense of Differential Privacy (DP), or they lack unified information budgeting and k-use control. To address these challenges, this paper proposes a verifiable differential privacy partial disclosure scheme for IoT. We employ DP accounting to uniformly constrain the leakage of three types of operators: threshold, binning, and prefix. Furthermore, we design stateless k-use tokens based on Verifiable Random Functions (VRFs) and chained receipts to generate publicly verifiable compliance evidence for each response. We implemented an end-edge-cloud prototype system and evaluated its performance on two use cases: smart meter threshold alarms and industrial sensor out-of-bound detection. Experimental results demonstrate that compared with a baseline relying on server-state counting for k-use control, our stateless k-use mechanism improves throughput by approximately 25–37% under concurrency scales of 1, 8, and 16, and reduces p95 latency by an average of 15%. Meanwhile, in multi-party splicing attack experiments, the re-identification accuracy remains stable in the 0.50–0.52 range, approximating random guessing. These results validate that the proposed scheme possesses low-energy engineering feasibility and audit-friendliness while effectively suppressing splicing risks. Full article

Large Language Models (LLMs) have demonstrated exceptional performance in knowledge-based dialogue generation and text evaluation. Synthetic data serves as a cost-effective alternative for generating high-quality datasets. However, it often plagued by hallucinations, inconsistencies, and self-anthropomorphized responses. Concurrently, manual construction of knowledge-based dialogue datasets remains bottlenecked by prohibitive costs and inherent human subjectivity. To address these multifaceted challenges, we propose ACE (Automatic Construction of Knowledge-Grounded and Engaging Human–AI Conversation Dataset), a hybrid method using hierarchical prompt engineering. This approach mitigates hallucinations and self-personalization while maintaining response consistency. Furthermore, existing human and automated evaluation methods struggle to assess critical factors like factual accuracy and coherence. To overcome this, we introduce the Truthful Answer Score (TAS), a novel metric specifically designed for knowledge-based dialogue evaluation. Our experimental results demonstrate that the ACE dataset achieves higher quality than existing benchmarks, such as Wizard of Wikipedia (WoW) and FaithDial. Additionally, TAS aligns more closely with human judgment, offering a more reliable and scalable evaluation framework. Our findings demonstrate that leveraging LLMs through systematic prompting can substantially reduce reliance on human annotation while simultaneously elevating the quality and reliability of synthetic datasets. Full article

Music has been shown to increase arousal and attention and even facilitate processing during non-musical tasks, including those related to speech and language functions. Mind wandering has been studied in many sustained attention tasks. Here, we investigated the intersection of these two phenomena: the role of mind wandering while listening to familiar/unfamiliar musical excerpts, and its effects on concurrent linguistic processing. We hypothesized that familiar music would be less distracting than unfamiliar music, causing less mind wandering, and consequently benefit concurrent speech perception. Participants (N = 96 young adults) performed a lexical-semantic congruity task where they judged the relatedness of visually presented word pairs while listening to non-vocal classical music (familiar or unfamiliar orchestral pieces), or a non-music environmental sound clip (control) played in the background. Mind wandering episodes were probed intermittently during the task by explicitly asking listeners if their mind was wandering in that moment. The primary outcome was accuracy and reactions times measured during the lexical-semantic judgment task across the three background music conditions (familiar, unfamiliar, and control). We found that listening to familiar music, relative to unfamiliar music or environmental noise, was associated with faster lexical-semantic decisions and a lower incidence of mind wandering. Mind wandering frequency was similar when performing the task when listening to familiar music and control environmental sounds. We infer that familiar music increases task enjoyment, reduces mind wandering, and promotes more rapid lexical access during concurrent lexical processing, by modulating task-related attentional resources. The implications of using music as an aid during academic study and cognitive tasks are discussed. Full article

This study had two purposes: (1) to develop a forced-choice personality inventory to assess student personality characteristics based on the five-factor (FFM) personality model and (2) to examine its factor structure via the Thurstonian Item Response Theory (TIRT) approach based on Thurstone’s law of comparative judgment. A total of 200 items were generated to represent the five dimensions, and through Principal Axis Factoring and the composite reliability index, a final pool of 75 items was selected. These items were then organized into 25 blocks, each containing three statements (triplets) designed to balance social desirability across the blocks. The study involved two samples: the first sample of 1484 students was used to refine the item pool, and the second sample of 823 university students was used to examine the factorial structure of the forced-choice inventory. After re-coding the responses into a binary format, the data were analyzed within a standard structural equation modeling (SEM) framework. Then, the TIRT model was applied to evaluate the factorial structure of the forced-choice inventory, with the results indicating an adequate fit. Further suggestions for future research with additional studies are provided to justify the scale’s reliability (e.g., test–retest) and validity (e.g., concurrent, convergent, and divergent). Full article

The dynamics underpinning the urban landscape change are primarily driven by social, economic, and environmental issues. Owing to the population’s fluctuating needs, a new and dual perspective of urban space emerges. The Artificial Intelligence (AI) of a territory, or the system of technical diligence associated with the anthropocentric world, makes sense in the context of this temporal mismatch between territorial processes and utilitarian apparatus. This creates cerebral connections between several concurrent decision-making systems, leading to numerous perspectives of the same urban environment, often filtered by the people whose interests direct the information flow till the transformability. In contrast to the conventional methodologies of decision analysis, which are employed to facilitate convenient judgments between alternative options, innovative Artificial Intelligence tools are gaining traction as a means of more effectively evaluating and selecting fast-track solutions. The study’s goal is to investigate the cross-functional relationships between Artificial Intelligence (AI) and current decision-making support systems, which are increasingly being used to interpret urban growth and development from a multi-dimensional perspective, such as a multi-criteria one. Individuals in charge of administering and governing a territory will gain from artificial intelligence techniques because they will be able to test resilience and responsibility in decision-making circumstances while also responding fast and spontaneously to community requirements. The study evaluates current grading techniques and recommends areas for future upgrades via the lens of the potentials afforded by AI technology to the establishment of digitization pathways for technological advancements in the urban valuation. Full article

The Czochralski method is the primary technique for single-crystal silicon production. However, anomalous states such as crystal loss, twisting, swinging, and squareness frequently occur during crystal growth, adversely affecting product quality and production efficiency. To address this challenge, we propose an enhanced multimodal fusion classification model for detecting and categorizing these four anomalous states. Our model initially transforms one-dimensional signals (diameter, temperature, and pulling speed) into time–frequency domain images via continuous wavelet transform. These images are then processed using a Dense-ECA-SwinTransformer network for feature extraction. Concurrently, meniscus images and inter-frame difference images are obtained from the growth system’s meniscus video feed. These visual inputs are fused at the channel level and subsequently processed through a ConvNeXt network for feature extraction. Finally, the time–frequency domain features are combined with the meniscus image features and fed into fully connected layers for multi-class classification. The experimental results show that the method can effectively detect various abnormal states, help the staff to make a more accurate judgment, and formulate a personalized treatment plan for the abnormal state, which can improve the production efficiency, save production resources, and protect the extraction equipment. Full article

Prison sentences that exceed the natural lifespan present a puzzle because they have no more power to deter or incapacitate than a single life sentence. In three survey experiments, we tested the extent to which participants support these longer-than-life sentences under different decision contexts. In Experiment 1, 130 undergraduates made hypothetical prison sentence-length recommendations for a serious criminal offender, warranting two sentences to be served either concurrently or consecutively. Using a nationally representative sample (N = 182) and an undergraduate pilot sample (N = 260), participants in Experiments 2 and 3 voted on a hypothetical ballot measure to either allow or prohibit the use of consecutive life sentences. Results from all experiments revealed that, compared to concurrent life sentences participants supported the use of consecutive life sentences for serious offenders. In addition, they adjusted these posthumous years in response to mitigating factors in a manner that was indistinguishable from ordinary sentences (Experiment 1), and their support for consecutive life sentencing policies persisted, regardless of the default choice and whether the policy was costly to implement (Experiments 2 and 3). These judgment patterns were most consistent with retributive punishment heuristics and have implications for sentencing policy and for theories of punishment behavior. Full article

Fear and frustration are two emotions thought to frequently contribute to problem behaviour, often leading to relinquishment. Inferring these emotions is challenging as they may present with some similar general signs, but they potentially require different treatment approaches to efficiently address the behaviour of concern. Although behavioural assessment frameworks have been proposed, it is largely unknown how clinical animal behaviourists (CABs) assimilate information about the emotional state of an animal to inform their behavioural assessment. In other fields (such as both in human and veterinary medicine), the use of intuition and gut feelings, without the concurrent use of an assessment framework, can lead to higher rates of error and misdiagnosis. Therefore, this study used semi-structured interviews of ten CABs and qualitative methods to explore the ways they conceptualise, recognise and differentiate fear and frustration in dogs. Although interviewees perceived fear and frustration as negative affective states that lead to changes in an animal’s behaviour, there was little consensus on the definition or identification or differentiation of these emotions. The use of a scientific approach (i.e., hypothesis-driven and based on falsification of competing hypotheses) for behavioural assessment was highly variable, with individual assessment processes often characterised by tautology, intuition, circular reasoning and confirmation bias. Assessment was typically based on professional judgment, amalgamating information on interpretation of communicative signals, motivation, learning history, breed, genetics and temperament. Given the lack of consensus in the definition of these states, it is clearly important that authors and clinicians define their interpretation of key concepts, such as fear and frustration, when trying to communicate with others. Full article

Palm oil plays a crucial role in the food industry, industrial applications, and bioenergy, accounting for over one-third of global vegetable oil production. The production area has quadrupled, and the volume is about seven times higher today than in the early 1990s. This significant increase is attributed to several factors, including the oil palm’s notably higher yield per hectare compared to other oilseeds, cost-effectiveness, versatility, and excellent manufacturing characteristics. Despite its economic benefits, industrial palm oil production raises substantial ecological and social concerns, such as deforestation, habitat loss, and labor issues. This study presents a comprehensive sustainability assessment that concurrently considers economic, environmental, and social aspects. Through qualitative expert interviews, various stakeholders in the supply chain evaluated the sustainability criteria of palm oil production and application using the Analytical Hierarchy Process (AHP), a decision support tool helping to analyze, structure, and solve complex decision problems. The results reveal that, on average, the experts consider environmental criteria to be of the highest importance, followed by social sustainability, while economic criteria are of lower significance. However, the approximations regarding the weighting of the criteria showed considerable variations among experts. The AHP priority index for RSPO-certified palm oil is nearly as high as the reference product “EU canola oil”; this observation is consistent with all expert judgments. This study provides an adequate approach to assessing the sustainability of agricultural supply chains, offering practical recommendations for the food industry and policymakers. Full article

The objective of this study was to design and validate the Compassion Fatigue Scale (EFat-Com) in Peruvian nurses. Methods: A 13-item scale was designed using qualitative procedures and expert judgment. This version was administered to 201 nursing professionals using an electronic form along with two other measures: the Patient Health Questionnarie-2 and the Satisfaction with Life Scale. Results: Exploratory factor analysis supported the existence of two factors with factor loadings > 0.54. Confirmatory factor analysis of the two-factor model yielded satisfactory fit indices after the elimination of two items. Regarding concurrent validity, a positive relationship was obtained between the EFat-Com and the measure of depression; however, no correlation was found with the measure of life satisfaction. The internal consistency was 0.807 for the total scale, 0.79 for Factor 1, and 0.83 for Factor 2. Conclusions: The EFat-Com showed adequate psychometric properties with respect to content-based validity evidence, internal structure, and reliability. Therefore, the instrument can be used in research and professional settings. However, it is essential to continue studying the validity evidence in other contexts. Full article

(1) Background: This article discusses the first two phases of development and validation of the Three Domains of Judgment Test (3DJT). This computer-based tool, co-constructed with users and capable of being administered remotely, aims to assess the three main domains of judgment (practical, moral, and social) and learn from the psychometric weaknesses of tests currently used in clinical practice. (2) Method: First, we presented the 3DJT to experts in cognition, who evaluated the tool as a whole as well as the content validity, relevance, and acceptability of 72 scenarios. Second, an improved version was administered to 70 subjects without cognitive impairment to select scenarios with the best psychometric properties in order to build a future clinically short version of the test. (3) Results: Fifty-six scenarios were retained following expert evaluation. Results support the idea that the improved version has good internal consistency, and the concurrent validity primer shows that 3DJT is a good measure of judgment. Furthermore, the improved version was found to have a significant number of scenarios with good psychometric properties to prepare a clinical version of the test. (4) Conclusion: The 3DJT is an interesting alternative tool for assessing judgment. However, more studies are needed for its implementation in a clinical context. Full article