Reviewerly/exHarmony huggingface dataset:
Memory augmentation with adaptive cognitive interfaces

Memory is better for memorable images and during attentive states. Previous work shows that memorability and attentional state each explain significant variance in memory. In this study, we investigate whether we can precisely tailor what images are shown during which attentional states. Can we leverage image memorability to rescue memory during an attentional lapse? Or conversely, can an attentive state rescue memory for an otherwise forgettable image? In this study, our goal was to leverage both of these factors in real time to modulate human long-term memory. First, we selected scene images from the SUN database that reliably differed in memorability, i.e., corrected recognition or CR (CRhigh=83.42; CRlow=46.51; p<0.001) and ensured that they were balanced for lower-level visual features (RGB and spatial frequency, ps>0.5). Then, we used a continuous performance task in which participants categorized a series of rapidly presented scene images (500 scenes, 1 s stimulus presentation) as either indoor or outdoor. We built upon prior work that monitored attention dynamics in real time via behavioral fluctuations of response time (RT) and triggered specific images in high (slow RT) or low (fast RT) attentional states. Afterwards, participants completed a surprise recognition memory test. Participants successfully performed both the attention and memory phases (A′s>.5, ps<0.001). Furthermore, we successfully detected attentive and inattentive moments in real time, which were individually tailored based on each participant’s RTs. We then manipulated which specific images were paired with which attentional states. For half of the participants, extremely memorable information was presented during extremely attentive moments, and vice versa for the other half of participants. In sum, we created an adaptive cognitive interface in which we jointly controlled stimulus presentation contingent to attention dynamics, to create personalized encoding experiences.
Measuring visual attention to online videos with a mouse cursor window paradigm: considerations for large scale data collection

Previous research has allowed for large scale datasets to be created from mouse-based measures of attention to images (e.g., SALICON). However, there has not been a similar, easy-to-use solution for large scale data collection of attention to video stimuli. Here, we demonstrate our novel mouse-based measure of attention that can be used with videos in online experiments. Our results show similar performance between this paradigm and eye tracking in terms of the attended regions of interest in video. This paradigm tracks the user’s mouse location as they use their mouse to move a window of high resolution around an otherwise blurred screen. To view video content in more detail, the user moves their mouse window to that location. This results in a robust measure of visual attention that can be used to identify regions of video content participants find most salient or informative. Our research has compared eye movements from the DIEM dataset to mouse movements collected from online participants watching videos with a mouse-contingent bi-resolution display. To test the settings of the mouse-based method, participants experienced large, medium, and small window sizes and blur levels, in a 3x3 within-subjects factorial design. New results show that the motions made between these methodologies differ, but they result in visits to similar regions of interest in video. This suggests that mouse-based methods may not replicate effects that require the speed and ease of eye movements, but can replicate effects regarding attention to salient, informative, and preferred regions of content. These findings further support the validity of this method in large scale data collection on users’ attention to video stimuli–valuable to both experimental research, and the training and testing of video saliency models. With this presentation, we will discuss plans to make our methodology available for use with online experiment software.
Evaluation Of YouTube Information Quality About Pes Planus

The aim of this study is to measure the quality of information about 'flatfoot' and 'pes planus' presented online on the social media site YouTube and to determine the trends of viewers to medical information on YouTube.'flatfoot and pes planus' was typed into the YouTube search module. From the search results, videos with 50000 views or more, longer than 45 seconds, containing information about flatfoot and pes planus disease were selected. DISCERN, JAMA scoring, daily average views, number of likes, number of comments were collected from 53 videos that met the criteria. The profession of the sharer was evaluated in terms of the information quality of the sharing and the orientation of the audience.The mean number of views per day of the examined videos was 2047. Mean video presentation time was 8 minutes 50 seconds. The mean JAMA score was 2/4 and the mean DISCERN score was 38.16/75. According to the DISCERN score according to the professions, the video quality was moderate in doctors (41.44±12.99), moderate in physiotherapists (41.91±9.04), poor in coaches (32.78±7.87), poor in patients (34.50±5.32) and weak in others (34.89±14.00). According to the Spearman correlation between DISCERN score and mean daily viewing significant relationships were found in the doctors group p:0.0102 and the others group p:0.0033, however, no significant relationships were observed in the physiotherapists group p:0.1073, the flatfoot patients group p:0.5363 and the coaches group p:0.9111. There were significant relationships between like and comment counts in all groups (doctors p:0.0088, coaches p:0.0069, physiotherapists p:0.0007, others p:0.0018, patients p:0.0066).Looking at previous studies, it was observed that the quality of online health information was historically inadequate. Likewise, in our study on YouTube, we found that the quality of flatfoot and pes planus information was poor to moderate.
Phase-Dependent Asymmetry of Pattern Masking in Natural Images Explained by Intrinsic Position Uncertainty

A fundamental everyday task is visually searching for specific targets in the natural environments that surround us. In recent years, there have been a number of efforts directed at measuring and modeling detection of targets at specific locations in natural backgrounds, a key subtask of visual search. We have found that a productive approach is to bin natural background patches into joint histograms, with bins along background dimensions known from the classic vision literature to have a big impact on target detectability. By measuring psychometric functions in a sparse subset of these bins, it is possible to estimate how the included dimensions jointly affect detectability over the whole space of natural backgrounds. In previous studies, we found that human thresholds for sine and plaid targets are proportional to the separable product of the background luminance, contrast, similarity, and inverse partial-masking factor (a new dimension). Furthermore, this multidimensional Weber’s law was predicted by a simple template-matching observer with divisive normalization along each of the dimensions. The measure of similarity in these studies was the cosine similarity of the amplitude spectra of the target and background (SA)—a phase invariant measure of similarity. Here, we investigated the effect of the cosine similarity of target and background images (SI)—a phase dependent measure of similarity. We found that image similarity also has a substantial effect on threshold, and that threshold decreases monotonically with SI, in agreement with a recent study of visual search (Rideaux et al. JOV, 2022). Interestingly, the template-matching observer makes a completely different prediction: threshold is a U-shaped function of SI reaching a minimum when the target and background are orthogonal (SI = 0). Nevertheless, when the template-matching observer includes a small amount of intrinsic position uncertainty (which we measured in a separate experiment) the pattern of thresholds is predicted.
Object-based attention during scene perception elicits boundary contraction in memory

Two types of boundary transformation, contraction and extension, are equally likely to occur in memory. In extension, viewers will extrapolate information beyond the edges of the image, whereas in contraction, viewers will forget information near the edges of the image. Recent work suggests that the direction of transformation is dependent on image composition. However cognitive factors, such as object-based attention, may also influence how scenes are encoded into memory. Here, participants (N=36) searched for target objects in 15 scenes, while a separate group (N=36) were asked just to memorize the images. Both groups drew the scenes from memory after a delay. Search participants foveated significantly less of the scene (4.48% vs. 6.19%), but spent more time fixating the target. Across the 518 scenes drawn from memory, regression-analyses found that participants engaged in search were more likely to draw targets, and for both groups, object size was highly predictive of recall. However, only search drawings had a significant tendency to show boundary contraction, with 64.66% of drawings showing contraction and 28.6% showing extension, compared to 41.81% of memorize drawings showing contraction and 47.8% showing extension. These results are especially dramatic given that participants studied the same images in both conditions for roughly the same amount of time. Contraction took the form of a “zoom-in” effect in the search drawings – while targets were drawn in their accurate spatial locations, point of views were zoomed in on the targets so that they were drawn significantly bigger than they originally appeared, and the farther an object was from a target, the less likely it was to be remembered. These results support the recently proposed dynamic-tension model (Park et al., 2021), suggesting that both cognitive factors, like attention, and static properties, like image composition, may influence whether a scene contracts or extends in memory.
Images viewed for longer durations are better remembered during naturalistic encoding

Although it is yet unknown what determines which image we come across in our daily lives will be remembered, physical properties of visual stimuli are typically not considered to substantially contribute to memory. It was recently found that during naturalistic encoding image size influences memory such that bigger images are better remembered. Here we hypothesized that during naturalistic encoding images viewed for longer duration would be better remembered. After replicating the image-size on memory effect in an online experiment (n=189), we ran additional online experiments to test the effect of presentation-duration on memory with new naïve participants (n=90). In this experiment participants freely viewed 160 images presented for 250, 500, 1000, or 2000 ms (image onsets always 2500ms apart) without being aware of any memory related aspect of the experiment. They were later given a surprise old-new recognition test (320 images, 50% old). A main effect of presentation-duration on memory was evident by longer presentations leading to better memory (250ms<500ms<1000ms, post-hoc p’s<0.05). As in the previous study, we found a main effect of visual category on memory with faces best remembered and outdoors the least. Since enlarging image size and extending image presentation duration involve different neural mechanisms, we assumed that the contribution of image size and presentation-duration would be independent. To test that we ran a new study (n=93) with small/large images presented for short/long durations during the exposure phase, while at the surprise recognition memory test all images were presented at the same size and for the same duration. As expected from the earlier studies we found effects of size (p=5.8*10^-7), and presentation-duration (p=9.29*10^-6). Importantly, there was no interaction between size and presentation-duration (p=0.906). These results suggest that during naturalistic encoding, both image size and its presentation duration influence memory and their influences may be independent.
Interactions of body representations in rubber hand illusion and tool-use paradigms

In neuropsychological literature, numerous case studies suggest two separate body representations in the brain; one for perception, called the body image, and one for action, called the body schema. Rubber hand illusion and tool-use paradigms have been used frequently in the last twenty-five years to investigate these body representations, respectively, with minimal overlap between the fields. However, interactions between these paradigms are probable, considering the common sensory modalities targeted by the techniques used for measuring their effects. Here, we combined rubber hand illusion and tool-use paradigms in a novel behavioral experimental setup (N=72) and comparatively examined the resulting changes in body representations through measures of forearm bisection (for body schema), proprioceptive drift, and subjective experience questionnaire (for body image). Specifically, after a tool-use task where subjects actively used a grabber tool with their right hand to move cubes close to or away from their body, we observed a change in the metric representation of the right forearm length depending on the length of the tool used (using the long tool resulted in an increase in perceived forearm length). Subsequent to the tool-use task, the “tool-holding” rubber hand illusion, where the experimenter stroked the tip of the tool held by the rubber hand either synchronously or asynchronously to that of the tool held by the subjects, also resulted in perceived forearm elongation if the subject observed a longer tool held by the rubber hand. Follow-up experiments showed that the forearm elongation effect that occurred during rubber hand illusion depended on prior active use of the tool, embodiment of the observed hand and tool, and a length disparity between the held and observed tools, which together revealed that the representation of forearm length, a component of body schema, can be modified through changes in body image.
Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

The DL (Deep Learning) method is the standard for forecasting tourist arrivals. This method provides very good forecasting results but needs improvement if the data is small. Statistical data from the BPS (Central Bureau of Statistics) needs to be corrected, resulting in forecasts that tend to be invalid. This study uses statistical data and GT (Google Trends) as a solution so that the data is sufficient. GT data has a lot of noise because there is a shift between web searches and departures. This difference will produce noise that needs to be cleaned. We use monthly data from January 2008 to December 2021 from BPS sources combined with GT. Hilbert-Huang Transform (HHT) is proposed to clean data from various disturbances. The DL used in this study is long short-time memory (LSTM) and was evaluated using the root mean squared error RMSE and mean absolute percentage error (MAPE). The evaluation results show that the HHT-LSTM results are better than without data cleaning.
Determining The Location of Regional Landfill in Kediri District Using Spatial Approach with Geographic Information System Applications

Existing landfills in Kediri Regency and City are experiencing overcapacity conditions which are influenced by the increasing volume of waste along with the increasing population and the rapid development of urban areas. The Regional Landfill in Kediri Regency which is a national project based on Presidential Regulation of the Republic of Indonesia Number 80 of 2019 concerning the Acceleration of Economic Development in the Gresik - Bangkalan - Mojokerto - Surabaya - Sidoarjo - Lamongan, Bromo - Tengger - Semeru, and Selingkar Wilis - Lintas Selatan areas and is part of the Kediri Regency Regional Spatial Plan needs to be analyzed for location feasibility. The feasibility of the location of the Regional Landfill is the goal of this research with a spatial approach using Geographic Information Systems by considering the parameters in SNI 03-3241-1994 concerning Procedures for Selecting the Location of Waste Landfills. The selected variables are constraining factors consisting of regional criteria, namely geological conditions, hydrogeological conditions, slope, protected areas, distance to airfields, flood susceptibility and distance to water bodies. The feasible locations for regional landfill based on spatial analysis using GIS are in Ngadiluwih, Kras, Purwoasri, and Mojo sub-districts.
Linking brain activity during viewing and recall of movie events through gaze behavior

Viewing and recall engage overlapping neural systems in the human brain. They are further linked through gaze reinstatement, the recapitulation of encoding-related gaze patterns during recall. Here, we characterize the relationship between these phenomena for continuous narratives. Specifically, we investigate whether eye movements reflect the event structure of a movie, and whether the observed neural overlap is grounded in shared gaze patterns. We tested this by combining eye tracking and model-based gaze predictions with fMRI data acquired while participants watched and recalled an episode of the BBC show Sherlock. First, we found that gaze patterns during movie viewing were indeed event specific. Further, these patterns were consistent across participants and in-scanner and out-of-scanner eye tracking, predicted by frame-wise saliency, and predictive of how memorable each event was. Second, we reconstructed the movie event structure during recall using a hidden Markov model trained on the MR-signal of the eyeballs during viewing, suggesting that encoding related gaze patterns were partially reinstated during recall. Finally, we related the eyeball multi-voxel pattern to brain activity and found substantial overlap in gaze-dependent activity between viewing and recall. Collectively, our results suggest that gaze patterns are inherently linked with how we remember events, and that behavioral and cortical reinstatement jointly support recall of episodic memories.
Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website

The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.
Manajemen Risiko Teknologi Informasi Aplikasi E-Office ASN Menggunakan ISO 31000:2018

In carrying out the task of government affairs at Diskominfosanditik Sumedang Regency, an E-Government system is implemented, namely the ASN E-Office application which is a website-based and mobile-based application with a minimum specification of Android 4 which is used by all ASN (State Civil Apparatus) consisting of PNS (Civil Servants) and PPPK (Government Employees with Work Agreements) in Sumedang Regency. With this application, there may be threats and attacks including vulnerabilities that pose a risk, which disrupt the process of using the application. Therefore, the researcher decided to conduct information technology risk management research as a handling and protection of the application by applying the risk management process from ISO 31000:2018 where any information needed in this research was obtained from internal sources at Diskominfosanditik Sumedang Regency. This research was conducted in the informatics section and resulted in 14 possible risks consisting of 3 risks with high levels, 2 risks with medium levels, and 9 risks with low levels. From the GAP measurement, there is a difference that the need for risk measurement as the first step for risk management.&#x0D;
Nija-Onto: An ontology of The Nigerian languages version 1

Ontologies are becoming increasingly important as a knowledge-based resource for man and machines. Ontology is a veritable tool in computing for representing and thus preserving, domain knowledge for both human and machine processing. This paper builds a foundational ontology for Nigerian languages. Each language is annotated with its location information showing geo-political zone, states, and places where the Language originates. The ontology of Nigerian languages was built using the Methontology approach and specifically created using the Protégé 5.5.0 OWL editor. The Ontology was built on and evaluated using queries and a set of competency questions. The Nija-Onto was found to strongly align with its purpose of representing domain knowledge and thus provides visualized and highly tractable information on Nigerian languages.
Assessment of smartphone usage among students for effective learning during Covid-19 pandemic lockdown in senior secondary schools in Ogun State, Nigeria

This paper assessed the use of smartphone for effective learning during Covid-19 pandemic lockdown among senior secondary schools in Ogun State. It compared the difference that exists between the gender, age and location of the students during Covid-19 lockdown. The study was conducted using a descriptive research method. Three (3) research questions and hypotheses were formulated and one hundred and fifty (150) respondents were sampled using simple random technique. The instrument for data collection was researcher’s designed questionnaire. Five (5) experts in the field of Educational Technology validated the0 instrument to ensure content and face validity of the instrument. Cronbach’s Alpha was used to find out the value of the reliability of the instrument and it yielded 0.80. Data collected were analyzed using mean, standard deviation and ANOVA to test the hypotheses formulated at 0.05 significance. The study concluded that smartphone can be effectively use for learning regardless of the gender, age and locations. It was therefore recommended that curriculum planners should incorporate the use of smartphone into the school curriculum and that the government should provide necessary facilities and infrastructures for the use of smartphones in schools.