Reviewerly/exHarmony huggingface dataset:
Overestimation of Variability in Ensembles of Size and Color Despite Focusing Attention on Relevant Features

The visual system efficiently and accurately extracts summary statistics (e.g., the mean) from sets of similar objects through ensemble perception. Evidence for this phenomenon has been demonstrated in various features such as orientation, size, and faces. However, prior work suggests the visual system is biased to overestimate variability in ensembles, particularly when objects in an ensemble are more similar and have less variability. This has been referred to as the variability overestimation effect and has been found when perceiving low-level features (e.g., line orientation, color) and mid-level features (e.g., size). To further explore this bias, we examined the role of attention when perceiving variability within a set of objects. Using the method of adjustment, participants judged either the variability of size or color value in a set of nine sequentially presented circles that varied in both size and color value. For half of the trials, participants saw a pre-cue indicating to focus on either the set's size or color. For the remaining trials, participants focused on both size and color until the response phase, which presented a post-cue indicating which feature variability they were to estimate. Participants vastly overestimated variability in size (172%) and color value (139%) when objects were most similar. For color value, the pre-cue reduced this bias (115%) compared to attending to both features (168%). Similarly, participants overestimated size variability to a greater extent when focusing attention on both features (188%) than on one feature (158%). Although the pre-cue reduced the variability overestimation effect, participants still greatly overestimated the variability of both features when objects were most similar. Focusing attention on a specific feature did not eliminate the bias, further suggesting the visual system is fundamentally biased to overestimate variability. These findings have implications for real-world tasks requiring judgments about sets of similar objects (e.g., information visualization).
Penerapan Model Pembelajaran Kooperatif Tipe Numbered Head Together (Nht) Berbantuan Media Interaktif Berbasis Animasi terhadap Motivasi dan Hasil Belajar Siswa UpTD SMP Negeri 1 Kokop

This study aims to reviw student learning motivation and student learning achievement tests using Numbered Head Together (NHT) learning assisted by interactive media based on animation the research method used is the Lilierfrors method assisted by Microsoft Excel. In terms of the teacher’s ability, and can increase the ability of student in learning data processing while the purpose of this research is to find out student motivation questionnaires and student learning achievement tests, this reseachis in the from of quantitative research the subject of this research is class VII-A students at UPTD SMP NEGERI 1 KOKOP the data collection instrument used on learning motivation and student response test sheets which were very positive, where 98% of students expresses their enjoyment of learning using the Numbered Heads Together (NHT) cooperative learning model through interactive media based on animation achievement tests. Positive and understand 99% of this study it is stromgly application of the Numbered Heads Together (NHT) cooperative learning model can increase student learning VII-A UPTD SMP NEGERI 1 KOKOP in data processing material.
Smart Glove Berbasis IoT dengan Output Teks dan Suara

Stroke in Indonesia was the second leading cause of death and third cause of disability in the world. In Indonesia, the proportion of disability adjusted life-years (DALYs) contribution from stroke is around 9.7% and is the number one cause of disability. People with disabilities will have difficulty doing daily activities, especially communication. Therefore it is necessary to develop communication aids that can help stroke sufferers communicate daily by utilizing palm movements. In this study, two flex sensors were placed on the fingers. Communication aids for stroke sufferers function to detect four basic movements, namely: "I want to eat", "I want to drink", "I want to urinate/poop",and “rest”. The four sentences are also displayed on a display and also on the Smartphone. The detection of these four movements is obtained by bending the two fingers which are detected using a flex sensor placed on a glove. This tool is expected to be the development of communication aids for stroke sufferers.
Petisi Online dan Keterampilan Partisipatoris Warga Negara

This study aimed to examine the efficacy of online petitions in enhancing citizen participation skills. Employing a quantitative approach with descriptive methods, the research focused on the population of active students enrolled in the Teaching and Education Faculty at Sultan Ageng Tirtayasa University. Simple random sampling was utilized as the sampling technique. The validity and reliability of the data were assessed using the SPSS application version 26, while effectiveness was measured through the Likert Summarizing Rating (LSR) method. The findings indicated a significant level of effectiveness in developing citizen participatory skills through online petitions, as evidenced by the assessment score of 20.139 &gt; Q2. The average ideal score demonstrated an overall research effectiveness percentage of 73%. These outcomes highlight the potential of online petitions to foster citizen engagement, as they encourage individuals to exhibit intellectually supported and active participation as citizens. Online petitions serve as a facilitator for citizens to raise and address pertinent issues, thereby aiding in the cultivation of civic skills. This research contributes to the understanding of the role and impact of online petitions in nurturing citizen participatory skills, emphasizing the importance of online platforms in promoting active citizenship and fostering democratic engagement.
Use of immersive and interactive systems to objectively and subjectively characterize user experience in work places

This demo’s objective is to display how simulated environments can be used in the evaluation of work-related indoor environments compared to physical environments. In fact, in indoor environment evaluations, Virtual Reality (VR) offers new possibilities for experimental design as well as a functional rapprochement between the laboratory and real life. With VR, environmental parameters (e.g light, color, furniture...) can be easily manipulated at reasonable costs, allowing to control and guide the user’s sensorial experience. One main challenge is to acknowledge to which extent simulated environments are ecologically valid and which functions would be more solicited in different environmental-simulation display formats. User-centric evaluation and sensory analysis in the building sector is in its beginning; this new tool could be of benefit for the building sector, on one hand for methodological facilitation purposes and on the other for cost reductions. In order to achieve the objectives of this project, a first step is to develop and validate the indoor simulations. In environmental simulations, one of the most used formats, for its visual realism and ease of use are 360° panoramic photos and videos, which permits capturing physical-world images. In an objective of validation of the format, 360° photos of workplaces were taken in the building of Halle 6 Ouest of Nantes University and an immersive and interactive test based on physiological indicators to subjectively and objectively assess comfort and performances in work offices was developed. The demo will comprise a head-mounted display with integrated eye-tracking and the measure of electrodermal activity, heart rate and galvanic skin response.
Perencanaan Keuangan (RAPBS) Rencana Anggaran Pendapatan dan Belanja Sekolah dalam Pengembangan Sekolah di Smk Al Washliyah 9 Perbaungan

The draft School Revenue and Expenditure Budget (RAPBS) is carried out through a process of managing school funds, of course involving various parties such as the school principal who regulates financial nets and their management is carried out by treasurers and other components that assist the process of implementing school finances. The purpose of this study was to analyze the implementation of financial planning (RAPBS) school income and expenditure budget plans in school development. This research was carried out with a qualitative approach using descriptive methods. Data collection techniques are observation, interviews and documentation as well as literature studies and group discussions. The results of this research are that the acceptance component of SMK Al Washliyah 9 Perbaungan is optimized by using educational funding sources, namely the existence of regular BOS funds sourced from APBN revenues. Reporting on the accountability of the RAPBS is carried out only to the extent of the school principal. And will be conveyed at the meeting with the parties involved in it. The principal submits a report, especially regarding school financial receipts and expenditures. Evaluation is carried out every quarter or per semester. The conclusion from this study is that good use of the RAPBS has made school development better, including: character education, human resource development and the use of technology.&#x0D; Keywords: RAPBS; Accountability; Development
Robust Pose Estimation of Pedestrians with a Deep Neural Networks

In this paper, we provide a method for robust estimation of pedestrian pose that is especially useful for autonomous vehicles traveling toward pedestrians far away. Pedestrians in the far distance appear relatively small when seen by a camera, making it difficult to estimate the pedestrian's pose. We use fused deep neural networks (DNNs) to resolve the problems presented by pedestrians in the far distance. First, DNNs are used to detect pedestrians and enlarge the observed image. Next, the DNN method of pose estimation is applied. The proposed method uses a single camera to estimate the posture of a pedestrian in the far distance. Far-off pedestrians observed by cameras in moving cars appear as low-resolution images of non-rigid bodies. Detection and orientation estimation are difficult with conventional image processing methods. We used a series of DNNs to detect pedestrians, improve data availability, and estimate challenging postures to address these limitations. In this paper, we propose a method based on the multi-stage fusion of DNNs to solve a difficult problem for a single DNN. The experimental results established the superiority of the proposed method when applied to data challenging for conventional pose estimation methods. Applications of the proposed method include observing small objects and objects in the far distance. The method may be especially useful in surveillance systems, sports broadcasting, and other applications requiring human posture estimation.
ENSEMBLING DEEP CONVOLUTIONAL NEURAL NEWORKS FOR BALINESE HANDWRITTEN CHARACTER RECOGNITION

While deep learning has proven its performance in various problems and applications, it also opens opportunities in a new way to promote the heterogeneity of cultures and heritages. Balinese script is a cultural heritage in Bali, where it is used to write on palm-leaf manuscripts and contains essential information. Most manuscripts were damaged due to age and lack of maintenance, so a digitalization technique should be developed. In this study, we propose an ensemble of deep convolutional neural networks to recognize the handwritten characters in the Balinese script. We extensively compared various deep convolutional neural network architectures, and the results showed that our ensemble methods achieved the state of the art.
A model comprising independent control and conjugacy explains miniature fixation eye movements

Despite the abundance of time we spend holding gaze on visible objects, mechanisms that generate miniature eye movements of fixation are largely unknown. A general principle of ocular control is Hering’s Law which states that equal innervation drives both eyes during vergence and conjugate eye movements. However, during fixation, while microsaccades are largely conjugate, ocular drift in the two eyes is less correlated. We investigated ocular fixation with traditional foveal fixation stimuli (a single .2 deg dot), or rings with either 3 or 6 deg radii. Observers were instructed to fixate the center of each stimulus. Results showed decorrelated drift, and a large degree of horizontal “microvergence” in which the eyes drifted with different velocities. However, vertical drift showed microvergence for a similar duration as did horizontal drift. In contrast, microsaccades were always conjugate. We previously introduced a model to describe midline smooth pursuit vergence that incorporates independent eye control signals along with a unitary conjugate command (Heinen et al., VSS 2022). Here we demonstrate that this model generates conjugate microsaccades. Merely adding independent band-limited white noise to individual eye control modules caused the model to generate slow drift eye movements that predominantly exhibited “microvergence.” The results suggest that while microsaccades are generated by a unitary conjugate mechanism, slow drift is generated by independent control of the two eyes, and independent noise sources contribute to slow drift expression.
Scene and object false memory in a photo-realistic paradigm

Encoding of false visual memories can have severe negative consequences (e.g. incorrectly identifying foraged berries; mistaken eyewitness testimony). This phenomenon remains unclear, as most research focuses on verbal episodic stimuli, or nonrepresentational drawings when studied in the visual domain. Here, we complement this prior work by probing for whether false memories could be induced for unusual object-scene pairings in realistic photographs. Participants (N = 60) studied photographs of household scenes (SCEGRAM database: Öhlschläger & Võ, 2017). Later, we added unusual objects (e.g. ketchup in shower) to some scenes and removed them from others, and participants indicated whether each scene was an exact match with a studied scene. We investigated whether participants would report false memories for scenes where the unusual object was added and whether the frequency of these endorsements would differ from other false alarms (e.g., to brand-new, object-removed scenes). The hit rate for exact-match scenes was high, indicating strong memory formation. Interestingly, the false memory rate for object-added scenes was greater than for brand-new scenes. However, participants were also highly likely to false alarm to object-removed scenes, indicating a reliance on gist-based memory that overlooks incongruous objects. We also investigated the downstream fate of memories for the changed objects themselves. We found a boost to memory for objects that, via their addition or removal, violated predictions from prior memories. In contrast to previous work, we showed that false memories can be generated even for out-of-place objects, where the error cannot be attributed to scene schemas. Though participants endorsed schema violations, they were also likely to overlook these violations when the object was removed at test, highlighting the malleability of visual memory. Current follow-ups identify periods of off-task thought in real-time to trigger trials that gauge the association between susceptibility to false memory and ongoing attentional states.
A study of humans and convolutional neural networks on how to recognize blurry objects at the threshold of visibility

Humans can recognize objects under conditions of severe blur despite a vast loss of high spatial frequency content. Our previous work has suggested that the pervasive occurrence of blur in everyday vision may contribute to robust recognition behavior, as evidenced by the fact that convolutional neural networks (CNNs) optimized to recognize blurry objects can better predict neural responses to objects across diverse viewing conditions in both macaques and humans (Jang & Tong, VSS, 2022). Such findings led us to the following questions. Can humans further improve in their ability to recognize blurry objects with supervised training? Also, do CNNs trained on blurry objects learn robust properties that better approximate human performance, or do they learn alternative shortcut strategies? We devised a novel training protocol in which an initially blurred object gradually became less blurry over time, allowing participants to decide when they were ready to make a classification response about object identity. After the classification response, participants provided a feature saliency map of the diagnostic regions in the blurred image that contributed to their decision using a mouse pointer to demarcate the diagnostic regions. We found that participants showed significant improvement in their blur recognition thresholds after only 4 sessions of perceptual training. Moreover, CNNs optimized to recognize blurry objects provided a better match to participants’ blur recognition thresholds across individual object images. The feature saliency maps of blur-trained CNNs also appeared to lead to somewhat more holistic processing of visual objects, thereby providing a better correspondence with the spatially diagnostic regions of the object images annotated by human observers. Taken together, our study shows that human robustness to blur is highly adaptable, improves with modest training experience, and likely benefits from the frequent encounters that people have with blur in everyday life.
Eye movements during active vision are not driven by saliency, meaning, or aesthetics

Due to the technical challenges of real-world eye tracking, most of our knowledge about how we direct our gaze in scenes comes from laboratory studies requiring observers to remain still while viewing static images for a prolonged period of time. How do we direct our gaze when walking through a familiar environment? What is the relationship between gaze and the spatial distribution of low and high-level visual structure under these conditions? Eleven participants recorded first-person videos with binocular eye tracking (Pupil Labs) while repeatedly walking around a college campus pond over the course of a year. Each of the 97 walks commenced with a 16-point calibration procedure and was followed by a validation procedure to estimate gaze error. We identified four geographic regions of interest within the pond environment, and extracted two-second clips from each region in each walk. For each of the first-person video frames, we computed two popular models of gaze behavior: saliency (Itti & Koch, 2001) and meaning maps (Henderson & Hayes, 2017). Further, maps of scene aesthetics were created by asking 100 observers on Prolific to click on the ten most beautiful regions of each image. We quantified the spatial spread of each of these three maps using entropy. As expected, all maps were broadly distributed across each frame (saliency: 21.2 bits; beauty: 20.2 bits; meaning: 21.5 bits). By contrast, we found that gaze behavior within the frames was very focal, often confined to a narrow region on the path in front of the participant. The entropy of the gaze was markedly lower than the predictors: 14.8 bits. The current results suggest that although saliency and meaning form important predictors of gaze for stationary observers, they do not contribute strongly to active vision, which may rely more on navigationally-relevant features.
Maintaining eye fixations facilitates resolution of spatial cognitive conflicts

Cognitive control can be defined as a set of cognitive functions, which enables humans to solve conflicts between goals and external stimuli and thus to act in accordance with their goals. These conflicts can for example arise between the location of a stimulus and its symbolic meaning. Such a conflict can be moderated, i.e. ameliorated or exacerbated, by spatial attention, the mechanisms responsible for the prioritization of certain locations for visual processing. It is suggested, that holding an eye fixation also involves spatial attention. However, it is still unclear, how maintaining an eye fixation affects the resolution of spatial cognitive conflicts. In two experiments, participants were asked to perform a spatial cognitive control task, the Spatial Stroop Task. Either they were in addition instructed to keep their gaze on a fixation stimulus in the center of the screen and to suppress eye movements to the target stimulus, or no instruction was given regarding the eye movements, allowing the eyes to move freely. In the second experiment, an additional urgency manipulation (i.e. time pressure) was applied. The data from the first experiment implied an interaction between the congruency condition and the fixation condition. The general performance was not affected by the fixation condition, but holding a fixation and thus the binding of attentional resources seemed to facilitate the resolution of the spatial cognitive conflict. In the second experiment, adverse effects of urgency on the resolution of spatial cognitive conflict were evident in both fixation conditions. However, the adverse effects were reduced when holding a fixation. In conclusion, in both experiments maintaining an eye fixation supported cognitive control for spatial conflicts by facilitating the resolution of spatial cognitive conflicts.
Open world learning: research, innovation and the challenges of high-quality education

Click to increase image sizeClick to decrease image size Acknowledgments“We would like to convey our gratitude to Indonesia Endowment Fund for Education (LPDP) Scholarship for granting our studies and reinforcing this publication”.Additional informationFundingThe work was supported by the Lembaga Pengelola Dana Pendidikan.
A Deep Learning Framework for Estimating Global and Diffuse Solar Irradiance Using All-Sky Images

Nowadays, all-sky imagers (ASI) provide valuable information regarding the sky’s state, and they have been extensively used in cloud detection, segmentation, and solar forecasting studies. In this study, global and diffuse horizontal irradiances (GHI and DHI) are modeled using a Convolutional Neural Network (CNN) and Red–Green–Blue (RGB) information retrieved through ASI images. The predicted GHI and DHI underestimated observations with systematic biases of −1.8 W m−2 and −0.5 W m−2, while the dispersion errors were 82.7 W m−2 and 39.8 W m−2, respectively. The correlation coefficient was high, approaching 0.95 and 0.85 for GHI and DHI.