Reviewerly/exHarmony huggingface dataset:
Complexity &amp; Memorability have a Nonlinear Relationship when Remembering Scenes

Recent evidence suggests that the level of detail present in a scene image is inherently related to the memorability of that scene. Encoding of scene images is hence dependent upon a two-level memory process (Evans & Baddeley, 2019), in which the first level extracts the “gist trace”, and the second level extracts idiosyncratic details. It is these details which both aid later recognition of the scene, and allows remembered scenes to be distinguished from similar category distractors. Level of detail in scenes can be operationalized as scene complexity enabling comparisons between the complexity and memorability of a given scene. Finding a positive linear relationship between complexity and memorability suggests that scene complexity captures the “detail trace” of human memory (Kyle-Davidson et al, 2022). We hypothesise that detail and memorability have a nonlinear relationship given that when one explores the extremes on the image complexity continuum there may be reduced or enhanced memory for very simple and complex images. Linearity appears because the memorability of scene images used to investigate this relationship lie along a local tangent of the true nonlinear relationship; appearing linear because they capture a small part of the actual relationship. To explore this, we investigated the relationship between complexity (i.e, detail) and memorability modulating memorability in a repeat-recognition memory experiment by applying four different load levels during the encoding phase with scene images. We compared observer recognition performance after encoding with no cognitive load, low (digit repetition), medium (1-back counting) and high (3-back counting) load conditions for scene images with different ground-truth complexity values. We find the emergence of a nonlinear relationship between memorability and complexity across different load conditions; initially suggestive that low-complexity and high-complexity scenes display better performance under load even if not unique exemplars in a set, whereas medium-complexity scenes are more easily forgotten.
Modeling the dynamics of spreading attention in objects: Do transformers behave like humans?

Transformers have recently achieved state-of-the-art performance in many domains, including in solving the problems of object detection and grouping in images. However, despite their success, transformers are controversial as models of the human brain. Importantly, these models have not been shown to capture the way humans group and perceive objects. Here we explore the potential for the attention mechanism in transformers to map onto the dynamics of human object-based attention and grouping. We probe the mechanisms of object-based attention using a two-dot paradigm, where two markers are placed on an image and the task is to indicate whether they are on the same or different objects. Previous related work found that human reaction time in this task varies with the difficulty of object grouping and the spread of attention within an object. Our model first processes images through a convolutional neural network and then through a transformer network to find the self-attention weights between different pieces of the image, each represented by a token. The model then “spreads” attention through self-attention weights by starting from the first marker location and accessing all the tokens having strong self-attention weights to the starting-marker token, thus supporting the unrestricted spread of attention in the selection of the other tokens. The token closest to the second marker is then selected from among all the strongly connected tokens, corresponding to the hypothesized active spreading of attention in the two-dot task. This process repeats until attention spreads to the second token. We show that the model predicts subjects’ reaction time as estimated by the number of steps taken in the image-dependent attention spread. Our work shows that the dynamically formed self-attention connections in transformers have a role similar to that of feedback and lateral connections in the spread of object-based attention in human vision.
Qualitative Inconsistency Detection as a novel method for identifying the information used in locomotor interception

Research into the visual control of interception has generally assessed overall characteristics of ensemble averages of participant behavior. In contrast, the present contribution applied the novel Qualitative Inconsistency Detection (QuID) method on individual trials of locomotor interception under different target motion conditions. Starting from the assumption that visual control of locomotor interception is based in the nulling of (time derivatives of) pertinent optical angles, this method compares actual changes in locomotor direction with the direction signaled a visuomotor delay earlier by such nulling, on a trial-to-trial basis. In doing so, QuID hunts for inconsistencies as a method to disqualify consistent reliance on candidate informational variables. We considered the target-heading angle THA and the target’s bearing angle BA in two studies of locomotor interception of targets moving along circular trajectories in the agent’s plane of motion. We detected sign changes in participant heading direction (7346 instances in the two data sets) and evaluated if nulling zeroth-, first- or second-order time derivative changes in THA and BA could be responsible for each individual direction change. Two inconsistencies could surface. First, nulling a specific informational variable could lead to a change in the direction opposite to that observed. Second, the nulling of a specific informational variable should lead to a change in heading in a specific direction but no such change was observed. The analyses of the 7346 events ruled out the use of THA and its time derivatives, and implied the use of the first time derivative of BA early (up to 1 s) into the trial and a combination of the first and second time derivatives of BA later on. An alternative for combining variables would be the use of a time derivative of fractional order.
Penetration Testing for the Cloud-Based Web Application

This paper discusses methods, tools, approaches, and techniques used for the penetration testing on the cloud-based web application on Amazon AWS platform. The findings of a penetration test could be used to fix weaknesses and vulnerabilities, and significantly improve security. The testing is implemented by undertaking a malicious attack aiming to breach system networks and thereby confirm the presence of cloud infrastructure. The research focuses on cloud-based web applications' high-risk vulnerabilities such as unrestricted file upload, command injection, and cross-site scripting. The outcomes expose and approved some vulnerabilities, flaws, and mistakes in the utilised cloud based web application. It is concluded that some vulnerabilities haveto be considered before architecting the cloud system. Recommendations are proposing solutions to testing results.
Travel distance estimation from biological motion and optic flow

Humans use visual motion to estimate how far they walked. In static environments, this visual motion is the optic flow. In populated scenes, the biological motion of other people destroys the one-to-on correspondence between optic flow and travel distance. We investigated how observers estimate travel distance when walking in a crowded environment. In three conditions, participants saw self-motion through a crowd of standing, approaching, or leading point-light walkers. For a standing crowd, optic flow is a veridical signal for distance perception. For an approaching crowd, the visual motion is the sum of the self-motion-induced optic flow and the optic flow produced by the approaching walkers. In contrast, when the self-motion follows a leading crowd, in which walkers keep their distance from the observer, no optic flow is produced. All conditions also contained a ground plane consisting of stripes oriented parallel to the self-motion that contained no optic flow cues but perspective cues to static distance. After each trial, participants (n = 25) reported their traveled distance by adjusting a target on the ground plane to match the distance of their travel. We found that, overall, distance estimation was quite similar in all conditions showing that biological motion information can be used (a) to compensate for excessive optic flow in the approaching condition and (b) to generate distance information in the leading condition. All conditions were well fit by the leaky path integration model that describes a disproportional underestimation of long distances. Analysis of the fit parameters revealed slight but significant differences that illustrate how biological motion and optic flow are combined for travel distance estimation.
Exploring the neural correlates of naturalistic hybrid search tasks

Since their introduction in the 60s, we’ve learned a lot about hybrid search (HS) tasks, which require observers to search for any item of a memory set. One of the main signatures of HS is a robust linear relationship between RT and visual set size (as in other visual search -VS- tasks) and a logarithmic relationship between RT and memory set size. This has been investigated under several scenarios, including word searches and categorical targets, and its behavioral correlates have been used to inform theories of VS. However, little is known about the underlying neural mechanisms in HS (and in general about overt attention VS tasks). One reason is that eye movements produce artifacts in M-EEG signals, which are much larger than the signals of interest. Here, we aim to start uncovering the neurophysiological mechanisms underlying HS. We first ran an online behavioral experiment using a new mapping naturalistic search task, where the memory set changes in each trial. We found that the main signatures of HS (linear increase in RT with visual set size and logarithmic increase with memory set size), remain present in target-present trials even when contextual information is present. In a second experiment, we combined EEG and eye tracking recordings while participants performed the same task. By using a deconvolution analysis approach, we found differences in the fixation-related potentials (brain potentials aligned to fixation onset) depending on the memory set size. In a third experiment, we extended our approach to combine MEG and eye movement recordings. After identifying and characterizing robust markers of neural and saccadic spike artifacts in the signal, we found significant task effects in fixation-related fields and low-frequency oscillations. Altogether, our approach provides a way to inquire about the role of specific neurophysiological signals in eye movements and behavior.
In Silico Approach for Understanding the Associations Between Vision and Emotions Underlying the Uncanny Valley Effect

[Introduction] People typically feel uneasy when observing robots and computer graphic characters that resemble humans but are not perfectly human-like, an effect known as the “uncanny valley.” Several empirical studies examining affective responses to morphed images between human and non-human categories suggested that visual cues from two different categories elicit conflicting inferences about the entity, leading to feelings of eeriness. However, the detailed relationship between visual representations and emotional responses remains unclear. Artificial neural networks (ANNs), which can predict the relevant text description given an image, are promising models for providing insight into the processes underlying human cognition by exploring vast instances of human affective responses to visual concepts. In this study, we investigated how an ANN evaluates the matching of the morphed images to affective words used to describe uncanny valley effects in previous studies. [Methods] We created stimulus images by morphing between human faces and non-human objects at five morph levels and assessed the score of the images’ matching to words using CLIP (Contrastive Language–Image Pre-training), a state-of-the-art ANN that estimates semantic matching between an image and a caption. Ho and MacDorman proposed the indices of humanness, eeriness, and attractiveness using a semantic differential scale for evaluating the affective responses of human observers in studies of the uncanny valley. We calculated CLIP scores for the adjectives comprising the three indices and examined how these indices changed across morph levels. [Results and Conclusions] The eeriness index was highest at the midpoint of the morph continuum, where visual cue conflicts were maximal. This result indicates that CLIP associates visual cue conflicts in images with eerie impressions through training on an enormous amount of data covering our daily visual experiences. The current study explored how visual representations are related to human observers’ sentiment using ANN.
Evaluating Pyramid-Based Image Statistics Using Contrastive Learning

Texture models based on the steerable pyramid (Heeger & Bergen, 1995) have been long used both for texture synthesis (Portilla & Simoncelli, 1999), as well as for modeling human visual processing in areas V1/V2 (Freeman & Simoncelli, 2011), and have been adapted to model human peripheral vision (Balas et al., 2009). Yet, more work remains to determine which statistics are necessary and sufficient to fully represent textures computationally, as well as what statistics correspond to those used by the human visual system. To determine which statistics are most critical, we train a single-layer, fully-connected network to learn a representation that brings samples from the same texture together in latent space, and pushes apart samples from different textures. The network’s input is a vector of statistics from a recent peripheral vision model (Brown et al., 2021), and is trained with a contrastive learning loss. We find that this network successfully clusters both samples from the same texture, as well as families of similar textures. In analyzing the learned weight matrix, we identify the combinations of statistics useful for clustering like-textures. We perform this analysis for networks with varying numbers of output nodes, both above and below the number of input statistics. We then add a sparsity constraint that limits the representation to a single input statistic per output node, rather than a weighted combination of input statistics. We find that this network can still successfully cluster texture families, and again identify the most and least important statistics for this task. This work combines popular non-parametric statistics with learned representations, providing a simple platform to study texture representation, and giving further insight into models of mid-level vision by identifying the visual statistics most and least important for downstream visual processing.
Uncovering the hidden computations of deep neural networks by tracing the trajectory manifold from images to feature activations

Vision researchers have long sought to understand a ‘neural code’ that facilitates object recognition. The typical framework supposes visual processing transforms a pixel representation of an image into a more pragmatic feature space, directions in which encode high level visual concepts. In this framework, populations of neurons represent images in a ‘complex’ way, as points on a high-dimensional manifold, while single neurons (features) encode images in a ‘simple’ way, as scalars along a single axis. The scalar encoding of single features is often intuitively puzzling; suppose we find a feature highly selective for faces, is it then sufficient to say an individual image simply has ‘2’ faciness, or ‘30’, or ‘-10’? This account obfuscates the multiplicity of reasons a given image might excite/inhibit the face feature. In the present work, we present a novel method for elucidating these reasons behind deep neural network features, opening single features to analyses typically only suitable for populations. Leveraging access to the gradients from a given feature, we can construct a feature-wise ‘image trajectory manifold’. Images will be close to each other on this manifold if they drive the feature for the same reason, i.e., it’s roughly the same sequence of computations that leads to the feature’s activation. Images will be farther apart if they take distinctive hierarchical routes to the feature, even if they invoke a similar level of activation. For example, we find many features in an Imagenet trained model have a distinct route for dog faces, adjacent to the route for human faces, and far from the route for text. We provide a plotting tool for exploring where different images land on a feature’s trajectory manifold, as well as a feature visualization tool that leverages image trajectories to accentuate the aspects of an image that drive an individual feature’s response.
Exploration of Power Consumption/Execution time models for Video Applications

Accomplishing high efficiency solutions in accelerating the power consumption/execution time for implementing the video applications of the Fall Detection System on processors or on FPGA or heterogeneous computing platform, Zynq- 7000 all programmable system-on-chip. In general, the aim of power/execution time estimation methodology mentions about the speed and accuracy. In our work, we target accuracy based modeling style and analysis information collected from measurement on real board to obtain sufficiently accurate power estimation for the Fall Detection System on heterogeneous platform. Therefore, we experiment and verify the model’s accuracy on Zynq-7000 AP SoC platform, to show the applicability of our model.
AN ANALYSIS ON STUDENTS’ WRITING MOTIVATION AT SMKN 1 BANGKINANG

This study aims to describe the problems in writing narrative text for class XI students of SMAN 1 Koto Kampar Hulu, describing the problems faced by students in write narrative texts, and provide solutions or ways to overcome problems faced by students. This study uses a qualitative description method. Technique data collection was obtained by using interview techniques and documentation, and the data obtained were analyzed using Triangulation data sources and method triangulation. The results showed that in writing narrative text, in class XI students of SMAN 1 Koto Kampar Hulu carried out based on 2013 curriculum, class XI English learning syllabus, and carried out according to the English lesson plan for narrative text teaching materials. The problems faced in writing narrative texts are: teacher teaching to students is not optimal, students' motivation is low in writing narrative text, How to overcome these difficulties is that teachers are given training and guidance regarding learning and students are given attention, motivation to be active in learning
MENGATASI MASALAH BELAJAR DENGAN STRATEGI PEMBELAJARAN BERBASIS MULTIPLE INTELEGENSI

The instructional process must be appropriate with the talents, interests and physical and psychology developments of student (The Government Regulation, number 32 point 19 year 2013). When there is an incompatibility, so it would be trouble learning, such as: students are difficult to take a change of their behavior permanently and trouble to develop their potential itself. The instructional problems may come from student itself, such as: the diversity of learning styles, and physical and psychology development. But it can be occurred because of external factor. One way to overcome these problems is using a multiple intelligence-based instructional strategy, so that the learning is suitable with the talent, interest and physical and psychology development of student. Because of the diversity of individual differences in a class, it may be difficult to give special services to each student. Implication of this diversity, the teacher needs to pay attention to multiple intelligences resonance in the process of instructional.
Brain settings across free-viewing tasks: from Exploration to Visual Search and Hybrid Search

When we look at a scene in the real world, we typically do it with a goal in mind, for instance searching for our keys or looking for one of many cups to prepare our breakfast. These goals can shape how we go over the scene. Here, we aimed to study how ecologically-relevant variables such as the particular task performed, the progression of the task, and the memory load required by the task influence brain activity. We performed two experiments with co-registered EEG and eye-tracking to investigate task-related effects on fixation-related potentials (FRPs). In the first experiment, participants were asked to observe (EX) some images or actively search for a target among an array of faces and objects (VS) in other. In the second experiment, participants memorized potential targets (of a memory set size -MSS-) and searched for any of them (hybrid search, HS). We applied a deconvolution analysis approach to estimate the contribution of the different elements embedded in the task. In both cases, we obtained robust FRPs for specific fixation content such as object category, consistent with classical fixed-gaze experiments and a handful of free-viewing studies. We also report a robust task effect, both comparing EX vs VS, and MSS=1 vs MSS>1 in the HS task. Moreover, when further investigating the dynamics along the trial, we found significant effects of trial progression and its interaction with the task effect. These effects occur early after fixation onset (100-200ms). These results shed light on top-down influences associated with different goals, not only in the pattern of eye movements but also in the brain response after each fixation. Overall, these studies show how combining experimental and analytical approaches allow us to discern among multiple overlapping neural processes, preserving key attributes of real-world tasks.
Remembering where, but not what: how spatial and object visual memory change across delays in recall

Although it has been established that memory worsens the further we get from the original event (Ebbinghaus, 1885), it is largely unknown what memory components degrade over this passage of time (e.g., object or spatial memory). Previous recognition studies suggest there may be different rates of decline in memory, with object detail declining more quickly than spatial memory of these objects (Talamini & Gorree, 2012). Memory may also become more semantic over time, leading to false objects inserted into our memories (Lampinen et al., 2001). However, these recognition methods—which test memory for images as a whole—lack the capability to fully capture the resolution of memory: what about images are actually remembered? Here, we conducted a series of experiments utilizing drawing to test recall, capturing how the details within memory—such as which objects are remembered and their spatial accuracy—change across increasing delays. In the primary experiment, participants were presented with a scene image and either immediately after, or following a delay (5 minutes, 1 hour, 24 hours, 48 hours, or one week later), drew what they remembered from this scene. These drawings were then scored by separate online participants, quantifying the number of objects recalled, their spatial accuracy, and the presence of false objects. We found participants significantly remembered scenes in less detail over time, recalling fewer objects from the scene when recalling further from encoding. However, participants also had surprisingly accurate memory a week later, with few false objects and high spatial precision. Although spatially precise, we found interesting asymmetries in vertical and horizontal spatial memory of objects. Therefore, loss of object detail in memory appears to be the primary memory component that worsens over time, supporting different rates of decline in memory and a dissociation between spatial and object memory.