Reviewerly/exHarmony huggingface dataset:
Building a Digital Learning Hub: Moodle-Based E-Learning for Sekolah Penggerak in Kolaka Utara

Moodle serves as a valuable platform for creating online courses, training, and internet-based education while supporting essential e-Learning content distribution standards, specifically referring to SCORM. This research pursues three primary objectives: (1) the development of Moodle-based e-Learning in SMPN 4 Kolaka Utara, (2) the development of a MOODLE-based e-Learning content package adhering to the SCORM standard, and (3) the comprehensive evaluation of the Moodle-based e-Learning development process. Adopting the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) approach, the outcome of this research is the establishment of an innovative E-learning platform, named mesikolah.com, accessible online via https://mesikolah.com/. These courses meet the stringent SCORM standardization criteria, including Accessibility, Adaptability, Affordability, Durability, Interoperability, and Reusability. Formative evaluation results demonstrate a strong alignment with the chosen research approach, ADDIE, while the summative evaluation reveals positive user feedback, with an impressive 80% satisfaction rate on the e-Learning platform. Although this research focuses on the e-Learning development stage and content package preparation, further investigations are warranted, such as classroom action research. Notably, the current content package predominantly comprises text and flash presentations, necessitating future development of diverse media types, such as video on demand or video streaming.
The Effect of Self-efficacy and Course Design Quality on Students’ Satisfaction with Online Courses: A Structural Equation Modeling Approach

Students’ satisfaction with online courses is considered as one of the most critical components in the continued use, as well as, adoption of e-learning applications. The study aimed at determining and analyzing the constructs that affect students’ satisfaction. It examined the effect of students’ self-efficacy and the quality of course design on students’ satisfaction, mediated by their attitudes toward online courses. The study was conducted at University of Ha’il. Responses of 202 students were used for the data analysis. The collected data was analyzed using two steps in AMOS: The proposed measurement model was developed using confirmatory factor analysis (CFA), and the relationships were examined using structural equation modeling (SEM). The results revealed that both students’ self-efficacy and the quality of course design had a significant positive effect on students’ satisfaction, mediated by their attitudes towards online courses. The outcomes of this study can help decision-makers and policymakers in higher education take essential steps to enhance students’ satisfaction with online courses and ensure that they continue to be used.
Where’s Waldo? Exploring Gaze Strategy in a Visual Search Task Online and In-Person

In this study, we employed a ‘Where’s Waldo’ visual search task to compare the eye movement behaviours of participants in-person and online. Participants were presented with a “Where’s Waldo” scene and their task was to find the ‘Waldo’ target in the scene. They pressed the spacebar to indicate when they had located the target then used the mouse to click on the target’s location. Eye-tracking data was recorded using the online Gazer (n = 28) and the in-person Eyelink 1000 (n = 9). An initial comparison of the results indicates the online participants were less accurate at identifying the target’s location (online: 79% accuracy v. in-lab: 90%). Online participants were also significantly slower to press the space bar to indicate target detection (online: 9555 ms v. in-lab: 7208 ms). However, analysis of eye-tracking data showed an opposite pattern of eye movement behaviours where the online participant’s first fixations on the target location occurred 800ms sooner than the first fixations of the in-lab participants (online: 5604 ms v. in-lab: 6406 ms). Therefore, online participants were fixating on the target more quickly, but were slower to indicate detection. Region of Interest (ROI) analysis indicated that in-lab participants averaged 1.06 fixations on the area with the Waldo target whereas online participants averaged 4.05 fixations. In other words, the in-person participants appeared to respond after a single fixation on the target, but the online participants made several saccades to and away from the target before responding. Online participants may be demonstrating a more conservative strategy in the visual search task by only providing the detection response after confirming target presence in multiple passes. Overall, we recorded compelling eye-tracking and behavioural data both in-person and online and provide evidence that remote participants may use an altered gaze strategy in a visual search task.
Toddler and preschooler attention to naturalistic scene features

Effective learning from visual exploration of our world depends on selective attention to informationally rich regions. In adults, attention is well-predicted by the distribution of semantic content, or local meaning, in a scene (Henderson & Hayes, 2017, 2018). Here, we address if meaning also predicts children’s attention in scene viewing. To investigate where 12- to 48-month-old children (N = 64, 30 girls) naturally look within scenes, we recorded children’s gaze using an SMI-Red eye tracker (at 120 Hz) toward digitized photographs of 24 real-world scenes for 6000 ms. The scenes were taken from Henderson and Hayes (2017), and adult ratings provided relative informativeness of scene patches about the meaning of scene regions, resulting in a meaning map for each scene. For example, a patch of blue sky would be judged less meaningful than a patch containing a recognizable part of a car tire. We compared toddler and preschoolers’ patterns of fixation with feature maps for meaning, physical saliency, and distance to the center of the screen. Children were more likely to fixate scene regions as meaning increased (ß = .90, z = 8.81, p < .001), saliency increased (ß = .52, z = 5.105, p < .001), and at more central regions of the scene (ß = .41, z = 5.105, p < .001). The effect of meaning was nearly double that of image saliency and center proximity. As the effect of meaning on attention increased, the effect of saliency decreased (ß = -0.14, z = -2.096, p = .036). Interestingly, we did not find that older children looked more to high meaning regions (p = .89). In conclusion, by 12 months, toddlers have refined their attention toward informative, adult-defined meaningful regions. As children explore novel objects in their visual world, this mechanism could lead to rapid accumulation of knowledge.
Visual landmark information is multiplexed with target information in the visual responses of prefrontal gaze centres.

How does the visual system extract useful information from a rich environment for action? For example, while reaching for a coffee cup, the brain may use several allocentric visual cues (nearby book or computer) to effectively grasp it. These landmarks influence spatial cognition and goal-directed behavior, but how landmark-related visual coding subserves action is poorly understood. To this goal, with gaze system as a model, we recorded 101/312 frontal (FEF) and 43/256 supplementary (SEF) eye field visual responses (in two head-unrestrained monkeys) to the presentation of a target (T, 100 ms), in presence of a visual landmark (L, intersecting lines; presented in one of four diagonal directions/configurations from T). First, using a response field model fitting approach, we confirmed our previous findings (Bharmauria et al. 2020, 2021) that Te (Target relative to initial 3D eye orientation) was the best model for FEF and SEF visual responses at the population level, but some neurons (30% in FEF and 20% in SEF) preferentially coded for landmark. We then specifically tested two mathematical continua (of ten equal steps) to quantify the influence of L on visual response: 1) Target to landmark in eye coordinates (Te-Le) that directly tested the influence of L and Target-in-eye to Target-relative-to landmark (Te-TL) to test the multiplexed influence of T and L. Along both continua, we found a significant influence of the landmark relative to the shuffled control data in most neurons (suggesting both landmark coding and influence of landmark on target coding). Further, the same analysis on separate T-L configurations resulted in a significant shift toward landmark-centered target coding at the population level in FEF, suggesting an influence of landmark coding. These results show that visual landmark influences visual responses in the gaze system, potentially stabilizing future gaze in the presence of noisy 3D eye position signals.
Designing an “Other Race Effect” test for forensic facial identification experts using the performance of deep networks and untrained humans.

Forensic face examiners outperform untrained participants in face identity matching (Phillips et al., 2018), though it is unclear whether this superiority generalizes to other-race faces. We developed a challenging test that can be performed with the limited time available to professional examiners. To select the most difficult image pairs from a set of Black (n= 3,102) and White (n= 122,728) faces (self-identified race when images were collected), we employed a deep convolutional neural network (DCNN) (Deng et al., 2019) and an experiment with untrained participants. Image pairs (n= 36 per race) were assembled using a DCNN “perceptual” similarity measure. Same-identity (different-identity) image pairs with the lowest (highest) similarity scores were selected from all possible pairs. Untrained participants (White: n= 26, Black: n= 11) judged whether the images showed the same identity or different identities. Ranking by perceptual difficulty, we created a set of 10 Black and 10 White face pairs (half same-identity pairs). “Difficulty” was measured by tallying the number of participants who incorrectly indicated same-identity pair as different identities, and vice versa. Next, we benchmarked the test by computing participants’ accuracy (area under the ROC curve) on the subset of pairs. The test proved challenging for untrained participants [Black participants: (faces: Black= 0.66, White= 0.53); White participants: (faces: Black= 0.56, White= 0.49)]. Participant race, face race, and the interaction did not affect accuracy (p > 0.05). Notably, additional DCNNs performed more accurately on the White face pairs than Black face pairs (Szegedy et al., 2017: Black= 0.72 , White= 1.0; Ranjan et al., 2017: Black= 0.5, White= 0.92). Given that the pattern of performance across race differed for humans and the DCNNs, we conclude that untrained human benchmarks are critical in building a challenging and balanced cross-race test for experts.
Language Models of Visual Cortex: Where do they work? And why do they work so well where they do?

It’s often taken for granted that the best models of visual cortex are vision models. Recent research into models that learn from various combinations of vision and language, however, has reinvigorated longstanding debates over just how visual our models of visual cortex really need be. In this work, we characterize where and to what extent unimodal language models or multimodal vision-language models best predict evoked visual activity in the human ventral stream. We do this with a series of controlled modeling experiments on brain responses in 4 subjects responding to 1000 images from the Natural Scenes Dataset (NSD), with both classical and voxel-reweighted RSA (veRSA). Using a series of models which consist of pure SimCLR-style visual self-supervision, pure CLIP-style language-alignment, or a combination of the two, we first demonstrate that language-aligned models -- when controlling for dataset -- are in fact no better than unimodal vision models at predicting activity in the ventral stream. We next use captions associated with the NSD images to the test the brain predictivity of language embeddings from across the processing hierarchy of (N=24) unimodal language models (e.g. SentenceBERT, GPT2), demonstrating that while these kinds of embeddings systematically fail to predict activity in early visual cortex, they perform on par with unimodal vision models (N=19) in occipitemporal cortex (with classical and veRSA scores of up to 43% and 67%, respectively). Finally, in a series of text manipulation experiments (e.g. word scrambling, nouns only), we show that the predictive power of these models seems predicated almost entirely on simple nouns in no syntactic order (with veRSA scores of up to 61%). These results qualify recent excitement about language-alignment in the ventral stream, and suggest language models are only successful models of high-level vision to the extent they capture information about the objects present in an image.
Benchmarking Human Mid-Level Scene Understanding

People can perceive 3D structure in arbitrary scenes, inferring geometry, semantic information and more. Large-scale vision benchmarks have been instrumental in driving the development of computer vision models. Previous benchmarking work targeting 3D scene understanding collected sparse annotations of segmentation, depth, and surface normals for a limited set of domains. However, these datasets lack sufficient measurement resolution to ensure statistical reliability, limiting the interpretability of model-to-human comparisons. Additionally, these efforts focused on natural images without ground-truth scene geometry, making it difficult to assess human accuracy. Here, we collected a large benchmark dataset measuring humans on several mid-level scene understanding tasks – including segmentation (n=342), relative depth (n=342), and surface normals (n=335) – across a variety of synthetic and natural images. Images were sourced from synthetic indoor-scene datasets, a novel Gestalt-inspired dataset we developed, and the Natural Scenes Dataset (Allen, 2021). In the segmentation and depth tasks, two dots were placed on an image and participants judged if the dots were on the same/different objects, or which dot was closer to the camera. In the surface normal task, participants oriented an arrow with a circular base to match the orientation of a visible surface in the image. In contrast to previous work, which focused on natural scene images, the addition of several synthetic datasets permits precise comparison of human responses to ground truth. We also collect responses from multiple participants per-point in each image, allowing us to obtain precise estimates of inter-annotator reliability. We found that people are both accurate and reliable when making segmentation judgements, and are reliable, though less accurate, when making judgments about relative depth or surface orientation. We envision this dataset as a resource to drive further developments in computational models of visual scene understanding.
Evaluation of Graph-Based Visual Saliency model using infant fixation data

Infant researchers are increasingly relying on computational models of bottom-up visual saliency, such as Graph-Based Visual Saliency (GBVS; Harel et al., 2006), to better understand the development of visual attention. GBVS predicts where an observer will look by decoding low-level features (color, intensity, orientation) of images and computing an overall map based on a weighting of these feature properties. The resulting maps are thought to reflect the distribution of the physical saliency of an image. However, GBVS was designed to predict adult fixations, and therefore, it is unclear the extent to which this model can reliably predict infant fixations or approximate infant visual saliency processing. We recorded eye gaze from 4- (N = 19), 6- (N = 21), and 10-month-old infants (N = 23) and adults (N = 24) as they viewed up to 48 naturalistic scenes from the MIT Saliency Benchmark Project (Judd et al., 2012). Correlations between each participant’s fixation density maps for each scene and GBVS saliency map were higher for adults compared to infants, indicating poorer GBVS performance for infant fixation data. Maps constructed for each of the individual channels (color, intensity, orientation) revealed that eye gaze was best predicted by orientation. Although GBVS performance did not increase over infancy, comparison of the GBVS-fixation density correlations to the noise ceiling (i.e., leave-one-out fixation density correlations) revealed that at 4 months, physical salience as measured by GBVS–and orientation in particular–accounted for nearly all explainable variation in eye gaze. However, the proportion of explainable variance accounted for by physical salience dramatically decreased across infancy. We suggest that young infants’ limited visual acuity and cortical development may result in qualitatively different processing of physical salience in naturalistic scene-viewing tasks compared to adults. Future work will explore how differences in scene properties (e.g., entropy/clutter) may relate to GBVS performance.
Partial Leader Optimizer

A new swarm intelligence-based metaheuristic optimizer, namely Partial Leader Optimizer (PLO), is presented. PLO contains several autonomous agents that represent the solution. The best solution represents collective intelligence, i.e., the leader. PLO has distinct mechanics in finding the acceptable solution during the given iteration. Every agent moves to a specified target in every iteration. Two options can be chosen to determine the target. First, the target is calculated by pushing the virtual best solution away from the corresponding agent. Second, the target is randomly chosen within the solution space. This target selection is conducted stochastically based on the threshold that is set manually before the iteration. Then, several candidates are generated between the target and the agent's current location. The distance between adjacent candidates is the same. The agent moves to the best candidate and updates the best solution. Simulation is implemented to observe and analyze the PLO’s performance. The well-known 23 benchmark functions are used as the optimization problems. In this simulation, PLO is benchmarked with marine predator algorithm (MPA), particle swarm optimization (PSO), average subtraction-based optimizer (ASBO), slime mold algorithm (SMA), and pelican optimization algorithm (POA). The result shows that PLO is competitive compared to these algorithms, especially in solving fixed-dimension multimodal functions. PLO is better than PSO, MPA, SMA, ASBO, and POA in optimizing 22, 19, 18, 9, and 20 functions out of 23, respectively.
Using deep neural networks to test possible origins of human face perception

Human face recognition is highly accurate, and exhibits a number of distinctive and well documented behavioral and neural "signatures" such as the face-inversion effect, the other-race effect and neural specialization for faces. How does the remarkable human ability of face recognition arise in development? Is experience with faces required, and if so, what kind of experience? We cannot straightforwardly manipulate visual experience during development in humans, but we can ask what is possible in machines. Here, I will present our work testing whether convolutional neural networks (CNNs) optimized on different tasks with varying visual experience capture key aspects of human face perception. We find that only face-trained – not object-trained or untrained – CNNs achieved human-level performance on face recognition and exhibited behavioral signatures of human face perception. Moreover, these signatures emerged only in CNNs trained for face identification, not in CNNs that were matched in the amount of face experience but trained on a face detection task. Critically, similar to human visual cortex, CNNs trained on both face and object recognition spontaneously segregated themselves into distinct subsystems for each. These results indicate that humanlike face perception abilities and neural characteristics emerge in machines and could in principle arise in humans (through development or evolution or both) after extensive training on real-world face recognition without face-specific predispositions, but that experience with objects alone is not sufficient. I will conclude by discussing how this computational approach offers novel ways to illuminate how and why visual recognition works the way it does.
Psychophysics of variable fonts: Gaze measures of reading efficiency

Reading is a demanding task, and while many studies have investigated the visual factors associated with reading, the recent development of variable fonts opens new avenues for this research. Variable fonts can be customized along a set of continuous parametric axes within a single font file (e.g., thin stroke, slant, etc.), lending themselves readily to psychophysical techniques. To understand how these settings can influence individual reading performance and which settings may improve reading efficiency, we recorded participants’ eye movements as they read short passages. For this study, we varied five font parameters within Roboto Flex: thick stroke, thin stroke, slant, weight, and width at five levels each. Participants read one passage per setting, displayed across four screens, and we measured saccade amplitude normalized to letter width as well as the number and duration of fixations. Our results demonstrate that increasing width and weight decrease reading efficiency since saccade amplitude decreased as letters became wider and visually heavier. Increasing thick stroke had the largest effect on reading efficiency, while thin stroke and slant had the smallest. We also found considerable individual variability in the degree to which these axes impacted individuals’ reading efficiency and the number of fixations they made. Our results suggest that the customizability of variable fonts and the sensitivity of our gaze measures may make it possible to quickly find the settings that are best for each reader and enable a new range of psychophysical investigations of the impact of font on reading.
Toward A Computational Model of Directional Visual Relations

Deep convolutional networks (DCNs) have been shown to match human visual ability in various tasks including object classification and segmentation. Nevertheless, DCNs still struggle to match our ability for abstract visual reasoning. Visual relations can broadly be divided into categorical and directional relations. Previous work has investigated the ability of DCNs to solve categorical visual reasoning (CVR) problems. For instance, it has been found that DCNs can solve spatial reasoning tasks such as determining whether objects are vertically or horizontally arranged much more efficiently than same-difference tasks such as determining whether two objects are same or different. Here, we explore another class of visual reasoning problems known as directional visual relations (DVR), where the order of each object in a relation matters. For instance, a visual scene of “a baby on a blanket” would be different from a scene of “a blanket on a baby.” We hypothesized that attention and working memory are needed to solve these tasks and because these functions are lacking in DCNs, DCNs would be limited in their ability to learn to solve these tasks. First, we studied how DCNs learn to solve DVR tasks, judging whether a target object is to the left vs. right and to the bottom vs. top of a reference object. DCNs struggle to learn directional visual relations when stimulus variability makes rote memorization difficult. Extending a DCN architecture to incorporate attention and memory yield a model that solves the task on par with human judgments. Altogether, our findings suggest that feedforward processing alone is insufficient to solve DVR and that attention and working memory are crucial for modeling how the brain solves DVR.