Reviewerly/exHarmony huggingface dataset:
Direct Evidence that Inversion of Faces Disrupts Configural Processing

Inversion of a face can be readily shown to interfere with the matching or recognition of that face. Often this disruption is attributed to the inability to employ a configural representation whereby the whole face can be assimilated and judged as an integrated representation. However, direct evidence for this explanation has been lacking. Configural effects are enabled by the overlap of large receptive fields (r.f.s) centered at varied positions throughout the face which serve to magnify the impact of small metric differences distinguishing similar faces (Xu et al., 2014). Subjects judged whether a briefly presented sequence of two similar computer-generated faces, both upright or both inverted, with an intervening mask depicted the same person. When different, the faces could differ in the one of three areas of the face–upper, middle, or lower–with changes such as the height of the eyebrows, the length of the nose, or the width of the mouth. Fixation for a given trial was centered on the eyes, nose, or mouth, as the position of the faces was vertically shifted from trial to trial (but not within trials) (Fig. 1). The different feature could thus be 0 (< 1°), 1 (1–2°), or 2 (3–4°) positions, from fixation. For upright faces, there was little effect of distance from fixation in detecting a differing feature; for inverted faces accuracy fell with distance of that feature (Fig. 2). Accuracy on different trials, for both upright and inverted faces, were highly correlated (rs ~ .95) with the magnitude of the difference in the faces as scaled by the Gabor-jet dissimilarity measure (Fig. 3), with the benefit of dissimilarity greater for the upright faces. The greater cost of distance in inverted faces can be interpreted as a diminished benefit of a configural representation enabled by large r.f.s.
Modeling voxel visual selectivities through convolutional neural network clustering

Visual properties used in cortical perception are subject to ongoing study, and features of intermediate complexity are particularly elusive. Recent works have used layers of convolutional neural networks (CNNs) to predict cortical activity in visual regions of the brain (e.g., Yamins 2014). Understanding the visual properties captured by CNN models can suggest similar structures represented in the brain. We use layers 2 through 5 of AlexNet (Krizhevsky 2012, trained on ImageNet) to identify candidate visual groupings. At each layer, we group image patches from ImageNet (Deng 2009) based on the corresponding pattern of CNN unit responses (Leeds 2017). We study the image patches in resulting clusters for similarity in unit responses and for intuitive visual/semantic consistency, based on labels from five subjects. We additionally assess the ability of clusters to improve the performance in predicting single voxel responses to visual stimuli measured from separate subjects from Kay (2008). For each CNN layer, we use each cluster’s average unit response pattern as a candidate set of weights to predict voxel activity from activity of all CNN units. We correlate cluster-based stimulus responses with voxel responses across ventral temporal cortex. For all four CNN layers studied, cluster-based stimulus responses strongly correlate (r>0.3) with voxels in mid-level visual regions – V4, LO, and IT. Correlations are larger at higher CNN layers. Within each layer, there is significant correlation between cluster density (similarity of CNN responses to patches within the cluster) and voxel correlation magnitude. However, there is consistently less agreement on subject-reported image patch qualities for high-correlation clusters compared to patches from low-correlation clusters. Frequently occurring “properties” include texture, color, and full objects. In intermediate cortical vision, voxels may tune for complex mixtures of shade and texture properties less intuitive to human observers, but still uncovered through trained computer vision models.
Predicting Mental States from Eye Movements During Reading

The pattern of eye movements made during reading is one of the most stereotyped high-level behaviors made by humans. As the readers of a language, we all agree to move our eyes in about the same way. Yet might there be hidden within all this self-similar behavior subtle clues as to how a reader is engaging the material being read? Here we attempt to decode a reader’s eye movements to reveal their level of text comprehension and their mental state, with the states considered being the dimensions of: interested/bored, awake/sleepy, anxious/relaxed, and confused/clear. Eye movements were recorded from participants reading five published SAT passages. After each the corresponding SAT question was asked, followed by a questionnaire about the reader’s mental state while reading the passage. A sequence of fixation location (x,y), fixation duration, and pupil size features were extracted from the reading behavior and input to a deep neural network (CNN/RNN), which was used to predict the reader’s comprehension (e.g., accurate or inaccurate answer) and their questionnaire scores. Specifically, classifiers were trained on labeled reading data (80%) for a given passage, then evaluated in their ability to predict scores from the unlabeled reading eye movements (20%). We also compared our model to an SVM model that used hand-coded features, such as average forward speed and angularity of eye movement, to predict the same comprehension and questionnaire scores. We found that our models successfully predicted most of the scores, in some cases highly above chance (classification accuracy >80%), and that the deep network model generally outperformed the SVM model. By learning and using features that code the seriality and nonlinearity of fixations made during reading, we conclude that a CNN/RNN can decode reading behavior to reveal a reader’s level of comprehension and mental state.
Relaciones interpersonales en el trabajo del equipo de enfermería

Objective:Analyze interpersonal relationships established by the nursing staff of a hospital.Method: Qualitative and descriptive research, performed in a hospital located in Rio Grande do Norte capital.There were sixteen nurses participating in the study, whose data were collected through semistructured interview and then treated by thematic analysis.Results: An interpersonal relationship category emerged in the perspective of the nursing staff that it was possible to see the weakness in interpersonal relationships in the unit of study. Conclusion:This study evidences the relevance in the development of strategies that are enabling the development of interpersonal relationships of nursing
Visibility-Aware Part Coding for Vehicle Viewing Angle Estimation

A number of spatially-localised semantic parts of vehicles sensitive to pose changes are encoded their visible probabilities into a mid-level feature vector. Car pose estimation is then formulated into a regression on concatenated low- and mid- level features to continuously changing viewing angles. Each dimension of our visibility-aware part codes separates all the training samples into two groups according to its visual existence in images, which provides additional part-specific range constraint of viewing angles. Moreover, the proposed codes can alleviate the suffering from sparse and imbalanced data distribution in the light of modelling latent dependency across angle targets. Experimental evaluation for car pose estimation on the EPFL Multi-View Car benchmark demonstrates significant improvement of our method over the state-of-the-art regression methods, especially when only sparse and imbalanced data is available.
English education during the fourth and fifth National Curriculum Period in South Korea: Focusing on the newspaper article analysis.

This study investigates the historical development of English education in South Korea regarding the fourth and fifth National Curriculum Period. By analyzing the newspaper articles in five major newspapers in Korea, we focused on the characteristics of English education in each period. The research questions are: 1) what are the characteristics of the fourth National Curriculum? 2) what are the characteristics of the fifth National Curriculum?, and 3) what is the implication of these previous curricula on the current English education in South Korea? During the fourth National Curriculum Period, after the successful hosting of the 1986 Asian Games and the 1988 Summer Olympic Games in Seoul, the importance of communication skills in English spread throughout the country. Accordingly, the need for early English education emerged. In the fifth curriculum, various social factors, including the liberalization of overseas travel, led positive washback effects on English education. In elementary schools, English instructions were conducted as a special activity. In secondary schools, various changes in the educational environment were involved, including the introduction of listening tests and recruitment of native English teachers, which still affects English education in 2020. This paper concludes with educational implications and future research directions.
Collaboration-Based Location Privacy Protection Method

In the privacy protection method based on user collaboration, all participants and collaborators must share the maximum anonymity value set in the anonymous group. No user can get better quality of service by reducing the anonymity requirement. In this paper, a privacy protection algorithm random-QBE, which divides query information into blocks and exchanges randomly, is proposed. Through this method, personalized anonymity, query diversity and location anonymity in user cooperative privacy protection can be realized. And through multi-hop communication between collaborative users, this method can also satisfy the randomness of anonymous location, so that the location of the applicant is no longer located in the center of the anonymous group, which further increases the ability of privacy protection. Experiments show that the algorithm can complete the processing in a relatively short time and is suitable for deployment in real environment to protect user's location privacy.
Diagnostic Objects Contribute to Late -- But Not Early-- Visual Scene Processing

Humans instantaneously and effortlessly obtain semantic information from visual scenes, yet it remains unclear which scene features drive this identification. This study examines the possibility that a subset of objects are central to scene categorization. Many models of object usage in scenes assume that all objects contribute equally to scene category. However, some objects are more diagnostic of their environments. For example, while “chairs” can be found in many types of scenes, “refrigerators” are evocative of kitchens, and are therefore diagnostic of the scene category. Using a labeled scene database, we defined diagnostic objects as those found nearly exclusively in a single scene category (p(category|object) > 0.9). We obscured all diagnostic objects in each image using localized phase scrambling (diag- condition) and compared these images with both unmodified images, and images with a similar amount of non-diagnostic information obscured (rand- condition). Observers (N=14) viewed 996 images for 250 ms each and performed 2AFC categorization, while 64-channel EEG was recorded. Observers were most accurate (0.91) for the intact images, but were also significantly more accurate for the rand- (0.90) compared with the diag- (0.86, p< 0.001, d=0.87). Observers were also 69 ms slower to classify the diag- images compared with the rand- (p< 0.0001, d=0.37). EEG waveforms were submitted to a linear classifier trained on unmodified images in order to determine the time-resolved decoding accuracy of diag- and rand- conditions. We found decodable information starting around 60 ms post-image onset for both experimental conditions. Decoding accuracy was significantly lower for diag- images starting 143 ms after image onset, suggesting that while the diag- images did not disrupt initial visual processing, diagnostic objects contributed to later semantic processing.
Neuromorphic Processing for Optical Communications

Neuromorphic computing has been recently demonstrated as a lucrative technology for communications systems. Optical neuromorphic technology enables implementation of machine learning algorithms in optical domain. We will discuss the recent progress, as well as, advantages and challenges of such technology for high speed and energy efficient signal processing.
Revealing the behaviorally-relevant dimensions underlying mental representations of objects

Humans can identify and categorize visually-presented objects rapidly and without much effort, yet for our everyday interactions with the world some object dimensions (e.g. shape or function) matter more than others. While these behaviorally-relevant dimensions are believed to form the basis of our mental representations of objects, their characterization typically depends on small-scale experiments with synthetic stimuli, often with pre-defined dimensions, thus leaving open the large-scale structure of the behavioral representations on which we ground object recognition and categorization. To fill this gap, we used large-scale online crowdsourcing of behavioral choices in a triplet odd-one-out similarity task. Based on natural images of 1,854 distinct objects and ~1.5 million behavioral responses, we developed a data-driven computational model (sparse positive embedding) that identifies object dimensions by learning to predict behavior in this task. Despite this dataset representing only 0.15% of all possible trials, cross-validated performance was excellent, correctly predicting 63% of individual human responses and approaching noise ceiling (67%). Further, the similarity structure between objects derived from those dimensions exhibited a close correspondence to a reference similarity matrix of 48 objects (r = 0.90). The model identified 49 interpretable dimensions, representing degrees of taxonomic membership (e.g. food), function (e.g. transportation), and perceptual properties (e.g. shape, texture, color). The dimensions were predictive of external behavior, including human typicality judgments, category membership, and object feature norms, suggesting that the dimensions reflect mental representations of objects that generalize beyond the similarity task. Further, independent participants (n = 20) were able to assign values to the dimensions of 20 separate objects, reproducing their similarity structure with high accuracy (r = 0.84). Together, these results reveal an interpretable representational space that accurately describes human similarity judgments for thousands of objects, thus offering a pathway towards a generative model of visual similarity judgments based on the comparison of behaviorally-relevant object dimensions.
Recommendation Based on Multimodal Information of User-Item Interactions

In order to alleviate the problem of rating sparsity in recommendation systems, a model called RBMI (Recommendation Based on Multimodal Information of User – Item Interactions) is proposed in this paper. Firstly, latent features of items are extracted by convolutional neural network from the description texts of items. Next, for each user, RBMI takes the latent feature vectors and the corresponding ratings of the items that the user have rated previously as the input of a long short-term memory network for learning dynamic latent representation of the user. Finally, the sigmoid function is employed to predict the interaction probability between users and items. Extensive experiments on two real-world datasets have been done and the results show that the proposed model offers better performance and gains significant improvement comparing with the existing state-of- art approaches.
Impact of Fused Visible-Infrared Video Streams on Visual Tracking

Currently state-of-the-art trackers rely on fully convolutional neural network (FCNN) for extracting salient features in order to create an appearance representation of the target. Ordinarily, most of them intend to work with input streams from the visible spectrum, yet how does an input stream from the infrared spectrum and a fused visible-infrared stream affect their performances and how does it benefit or detriment them? Towards this end, we compare the performance of various reference trackers utilizing FCNN for feature extraction, on visible, infrared and fused spectrums. By utilizing a carefully processed publicly available data set for the evaluation, containing visible-infrared paired sequences, we ensure to find synchronized and same attributes at the same locations, effectively studying only the impact of a spectral change. Thus, by analyzing quantitative results, we identify visual attributes which benefit or detriment from a fused approach on typical visual tracking scenarios.
Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks for Healthy Bone Marrow Baseline Image Generation

The 2019 Irish Machine Vision and Image Processing (IMVIP 2019), Technological University Dublin, Irealnd, 28-30 August 2019
Better statistical regularity with aging? Age-related difference in the neural processing of idioms

To process information efficiently, people extract regular/consistent patterns from the outside world to better predict upcoming events. Indeed, infant studies suggest that prior experience is necessary for such statistical regularity to be used. Yet, little work has been done to investigate if older adults—with their longer life experiences—would lead to greater influence of statistical regularity compared to their younger counterparts. We examined older and younger adults’ processing of Chinese idioms—four-word phrases with semantic regularity acquired through experience. Thirty younger and 27 older neurologically-intact right-handed adults underwent an idiom recognition fMRI experiment. Participants viewed sequentially presented quartets of Chinese characters and, upon onset of the fourth characters, judged whether the quartets constituted idioms or not. Quartet sequences comprised frequent idioms, infrequent idioms, scrambled characters, and non-characters. Compared to younger adults, older adults had higher recognition hits for frequent idioms and higher false alarms for scrambled characters, with no difference in d’, but smaller beta (more likely to make idioms judgment). fMRI results showed that older adults engaged higher neural responses to idioms relative to scrambled characters in left frontal, left parietal, and bilateral temporal areas, and to scrambled words relative to non-characters in left superior frontal gyrus. Critically, greater distinctive fronto-temporal engagement between idioms (either frequent or infrequent) and scrambled characters correlated with higher d’ only in older but not younger adults. Our findings indicate that older adults were more prone to judging four-word phrases as idioms, with those who were more selective (greater d’) for idiom judgment engaging more selective neural processing, possibly reflecting interaction of aging and experience. These findings suggest that different neural mechanisms are recruited for making the same idiom judgment in younger and older adults, reflecting the differential influence of statistical regularity extracted from the environment.
MR-AntMiner: A Novel MapReduce Classification Rule Discovery with Ant Colony Intelligence

Ant colony optimization (ACO) algorithms have been successfully applied to data classification problems that aim at discovering a list of classification rules. However, on the one hand, the ACO algorithm has defects including long search times and convergence issues with non-optimal solutions. On the other hand, given bottlenecks such as memory restrictions, time complexity, or data complexity, it is too hard to solve a problem when its scale becomes too large. One solution for this issue is to design a highly parallelized learning algorithm. The MapReduce programming model has quickly emerged as the most common model for executing simple algorithmic tasks over huge volumes of data, since it is simple, highly abstract, and efficient. Therefore, MapReduce-based ACO has been researched extensively. However, due to its unidirectional communication model and the inherent lack of support for iterative execution, ACO algorithms cannot easily be implemented on MapReduce. In this paper, a novel classification rule discovery algorithm is proposed, namely MR-AntMiner, which can capitalize on the benefits of the MapReduce model. In order to construct quality rules with fewer iterations as well as less communication between different nodes to share the parameters used by each ant, our algorithm splits the training data into some subsets that are randomly mapped to different mappers; then the traditional ACO algorithm is run on each mapper to gain the local best rule set, and the global best rule list is produced in the reducer phase according to a voting mechanism. The performance of our algorithm was studied experimentally on 14 publicly available data sets and further compared to several state-of-the-art classification approaches in terms of accuracy. The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets. Furthermore, experimental studies show the feasibility and the good performance of the proposed parallelized MR-AntMiner algorithm.
Visual Search goes real: The challenges of going from the lab to (virtual) reality

Searching for your keys can be easy if you know where you put them. But when your daughter loves playing with keys and has the bad habit of just randomly placing them in the fridge or in a pot, your routine search might become a nuisance. What makes search in the real world usually so easy and sometimes so utterly hard? There has been a trend to study visual perception in increasingly more naturalistic settings due to the legit concern that evidence gathered from simple, artificial laboratory experiments does not translate to the real world. For instance, how can one even attempt to measure set size effects in real world search? Does memory play a larger role when having to move your body towards the search target? Do target features even matter when we have scene context to guide our way? In this talk, I will review some of my labs’ latest efforts to study visual search in increasingly realistic environments, the great possibilities of virtual environments, and the new challenges that arise when moving away from highly controlled laboratory settings for the sake of getting real.
Discussion on Abnormal Values in Static Penetration Data

Abstract As the mainstream method of in-situ testing technology, static penetration testing is widely used. At present, the implementation of a large number of projects must be based on its test results. It also makes us have to pay attention to the reliability of its test results. In this paper, based on the previous experience and combined with the static penetration data in Hefei area, the reliability of test data is studied. The abnormal data in the static penetration data is eliminated and the influence of the abnormal data on the calculation results of correlation distance is expounded.