Reviewerly/exHarmony huggingface dataset:
ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector  Graphic Primitives

Selection functionality is as fundamental to vector graphics as it is for raster data. But vector selection is quite different: instead of pixel-level labeling, we make a binary decision to include or exclude each vector primitive. In the absence of intelligible metadata, this becomes a perceptual grouping problem. These have previously relied on heuristics derived from empirical principles like Gestalt Theory, but since these are ill-defined and subjective, they often result in ambiguity. Here we take a data-centric approach to the problem. By exploiting the recursive nature of perceptual grouping, we interpret the task as constructing a hierarchy over the primitives of a vector graphic, which is amenable to learning with recursive neural networks with few human annotations. We verify this by building a dataset of these hierarchies on which we train a hierarchical grouping network. We then demonstrate how this can underpin a prototype selection tool.
Detection method of roof bearing capacity of highway bridge tunnel based on self balance method

In order to improve the detection accuracy of tunnel roof bearing capacity and shorten the detection time, a detection method of highway bridge tunnel roof bearing capacity based on self balance method is proposed. Firstly, the principle of self balance method is analyzed, and the self balance method is used to collect the bearing capacity data of highway tunnel roof. Secondly, based on the above data, the power of external force and internal dissipation of tunnel roof is calculated, and the normal and tangential strain rates of failure points are calculated combined with the angle between sliding surface and plastic flow direction. Based on the calculation results, the bearing capacity detection equation is constructed, and the equation is solved by calculating parameters to obtain the bearing capacity detection results. Finally, a comparative verification experiment is carried out. The experimental results show that compared with the traditional detection methods, the detection accuracy and efficiency of text detection methods are significantly improved.
The Neural Correlates of Image Texture in the Human Vision Using  Magnetoencephalography

Undoubtedly, textural property of an image is one of the most important features in object recognition task in both human and computer vision applications. Here, we investigated the neural signatures of four well-known statistical texture features including contrast, homogeneity, energy, and correlation computed from the gray level co-occurrence matrix (GLCM) of the images viewed by the participants in the process of magnetoencephalography (MEG) data collection. To trace these features in the human visual system, we used multivariate pattern analysis (MVPA) and trained a linear support vector machine (SVM) classifier on every timepoint of MEG data representing the brain activity and compared it with the textural descriptors of images using the Spearman correlation. The result of this study demonstrates that hierarchical structure in the processing of these four texture descriptors in the human brain with the order of contrast, homogeneity, energy, and correlation. Additionally, we found that energy, which carries broad texture property of the images, shows a more sustained statistically meaningful correlation with the brain activity in the course of time.
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI

Data scarcity and noise are important issues in industrial applications of machine learning. However, it is often challenging to devise a scalable and generalized approach to address the fundamental distributional and semantic properties of dataset with black box models. For this reason, data-centric approaches are crucial for the automation of machine learning operation pipeline. In order to serve as the basis for this automation, we suggest a domain-agnostic pipeline for refining the quality of data in image classification problems. This pipeline contains data valuation, cleansing, and augmentation. With an appropriate combination of these methods, we could achieve 84.711% test accuracy (ranked #6, Honorable Mention in the Most Innovative) in the Data-Centric AI competition only with the provided dataset.
Correction to “A Step Towards the Optimal Estimation of Image Orientation”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the name of the college was missing from the affiliation of the first two authors.
A Real-Time Solution for Social Distance Detection in COVID-19 Pandemic

Given the fact that the COVID-19 outbreak recently showed impact on lakhs of people around the world, the number of people infected is on the rise. Various nations are taking unprecedented precautionary measures to deal with the worldwide pandemic scenario and restrict the spread of the outbreak. For avoiding the virus from spreading, social distance is one of the significant practices. The goal of this work is to provide a social distance detecting system using deep learning technology. Here, object detection algorithms are used for people detection in the given pictures, which provides a bounding box for each human being with a centroid. After that, object tracking techniques are used to track people in the frame by assigning a unique ID to each of them. Following detection, distance measuring methods are used to determine the pairwise distance among people in the picture. An exact value is defined to determine that people are violating social distance measures or not. If the computed distance between detected human beings is greater than the predetermined value, then they are classified as non-violating individuals; else, they come under the category of violating individuals.
Codes from symmetric polynomials

We define and study a class of Reed-Muller type error-correcting codes obtained from elementary symmetric functions in finitely many variables. We determine the code parameters and higher weight spectra in the simplest cases.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation

Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training and testing models using unbiased datasets, which does not align well with real-world applications. Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting. To this end, we first design and construct a large-scale benchmark dataset for cross-dataset transfer learning on the height estimation task. This benchmark dataset includes a newly proposed large-scale synthetic dataset, a newly collected real-world dataset, and four existing datasets from different cities. Next, a new experimental protocol, few-shot cross-dataset transfer, is designed. Furthermore, in this paper, we propose a scale-deformable convolution module to enhance the window-based Transformer for handling the scale-variation problem in the height estimation task. Experimental results have demonstrated the effectiveness of the proposed methods in the traditional and cross-dataset transfer settings. The datasets and codes are publicly available at https://mediatum.ub.tum.de/1662763 and https://thebenchmarkh.github.io/.
Zero-Shot Certified Defense against Adversarial Patches with Vision  Transformers

Adversarial patch attack aims to fool a machine learning model by arbitrarily modifying pixels within a restricted region of an input image. Such attacks are a major threat to models deployed in the physical world, as they can be easily realized by presenting a customized object in the camera view. Defending against such attacks is challenging due to the arbitrariness of patches, and existing provable defenses suffer from poor certified accuracy. In this paper, we propose PatchVeto, a zero-shot certified defense against adversarial patches based on Vision Transformer (ViT) models. Rather than training a robust model to resist adversarial patches which may inevitably sacrifice accuracy, PatchVeto reuses a pretrained ViT model without any additional training, which can achieve high accuracy on clean inputs while detecting adversarial patched inputs by simply manipulating the attention map of ViT. Specifically, each input is tested by voting over multiple inferences with different attention masks, where at least one inference is guaranteed to exclude the adversarial patch. The prediction is certifiably robust if all masked inferences reach consensus, which ensures that any adversarial patch would be detected with no false negative. Extensive experiments have shown that PatchVeto is able to achieve high certified accuracy (e.g. 67.1% on ImageNet for 2%-pixel adversarial patches), significantly outperforming state-of-the-art methods. The clean accuracy is the same as vanilla ViT models (81.8% on ImageNet) since the model parameters are directly reused. Meanwhile, our method can flexibly handle different adversarial patch sizes by simply changing the masking strategy.
Are evolutionary algorithms safe optimizers?

We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life. Such problems are referred to as safe optimization problems (SafeOPs). While SafeOPs have received attention in the machine learning community in recent years, there was little interest in the evolutionary computation (EC) community despite some early attempts between 2009 and 2011. Moreover, there is a lack of acceptable guidelines on how to benchmark different algorithms for SafeOPs, an area where the EC community has significant experience in. Driven by the need for more eficient algorithms and benchmark guidelines for SafeOPs, the objective of this paper is to reignite the interest of the EC community in this problem class. To achieve this we (i) provide a formal definition of SafeOPs and contrast it to other types of optimization problems that the EC community is familiar with, (ii) investigate the impact of key SafeOP parameters on the performance of selected safe optimization algorithms, (iii) benchmark EC against state-of-the-art safe optimization algorithms from the machine learning community, and (iv) provide an open-source Python framework to replicate and extend our work.
ParaNames: A Massively Multilingual Entity Name Corpus

We introduce ParaNames, a multilingual parallel name resource consisting of 118 million names spanning across 400 languages. Names are provided for 13.6 million entities which are mapped to standardized entity types (PER/LOC/ORG). Using Wikidata as a source, we create the largest resource of this type to-date. We describe our approach to filtering and standardizing the data to provide the best quality possible. ParaNames is useful for multilingual language processing, both in defining tasks for name translation/transliteration and as supplementary data for tasks such as named entity recognition and linking. We demonstrate an application of ParaNames by training a multilingual model for canonical name translation to and from English. Our resource is released under a Creative Commons license (CC BY 4.0) at https://github.com/bltlab/paranames.
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning

Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return. While flexible, it faces difficulties arising from the inefficient exploration due to its single step nature. In this work, we present Generative Planning method (GPM), which can generate actions not only for the current step, but also for a number of future steps (thus termed as generative planning). This brings several benefits to GPM. Firstly, since GPM is trained by maximizing value, the plans generated from it can be regarded as intentional action sequences for reaching high value regions. GPM can therefore leverage its generated multi-step plans for temporally coordinated exploration towards high value regions, which is potentially more effective than a sequence of actions generated by perturbing each action at single step level, whose consistent movement decays exponentially with the number of exploration steps. Secondly, starting from a crude initial plan generator, GPM can refine it to be adaptive to the task, which, in return, benefits future explorations. This is potentially more effective than commonly used action-repeat strategy, which is non-adaptive in its form of plans. Additionally, since the multi-step plan can be interpreted as the intent of the agent from now to a span of time period into the future, it offers a more informative and intuitive signal for interpretation. Experiments are conducted on several benchmark environments and the results demonstrated its effectiveness compared with several baseline methods.
A system for verifying non-standard personal identity documents using deep learning

Personal identity documents (IDs) are used for identity verification by many services in the real world. Recent research includes identity document verification using only an image of the document obtained from a mobile phone camera allowing verification procedures to be automatic and remote. Literature shows promising results for verification of standard IDs although the problem of verifying non-standard IDs such as student identity cards remains less explored. Identity verification pipeline often requires information matching , a non-trivial problem in case of non-standard identity cards, lacking in other literature. In this study, we describe a complete system utilizing deep learning methods to verify student identity cards from images captured using a mobile phone camera. Other contributions of this study include a method to correct skew in images using a convolutional neural network, use of state-of-the-art models for text extraction, comparison between algorithms applicable to various stages of the system and a customized fuzzy string-matching algorithm for information verification. This system evaluates an ID card in 2.31s and achieves an F1 score of 0.90054, G-Mean of 0.89442, and average precision of 0.95 in detecting improper ID cards. The system achieves superior performance in verifying student IDs, a type of non-standard IDs. The study also highlights several enhancements and open problems for improving verification.
The Theory of Categorial Conversion and the Problem-Solution Dualities in Knowing and Knowledge

The entry point to this chapter is a discussion on the meanings of the concepts and phenomena of category and categorial conversion and their uses in the understanding of knowing, knowledge and the principles of diversity and unity that they may entail. The concepts are introduced to deal with transformations and changes within the space of quantitative–qualitative dualities as they relate to info-statics and info-dynamics of varieties in all areas of knowing as intellectual investment flows and information-knowledge accumulation as intellectual capital stocks. Alongside the theory of categorial conversion is an introduction of the theory of Philosophical Consciencism and the discussions on its relations to neuro-decision-choice processes, education, preference formations, varieties of human progress, shifting of generational preferences and increasing individual and social abilities in the space of variety creation-destruction dualities as seen in terms of individual and collective curiosities over the space of imagination-reality dualities and continual variety transformations. The theories of categorial conversion and philosophical consciencism are mutually interdependent, inter-supportive and inseparable, just like the theories of info-statics and info-dynamics are mutually interdependent, inter-supportive and analytically inseparable. Similarly, the theories of categorial conversion and info-statics are mutually creating, while the theories of Philosophical Consciencism and info-dynamics are also mutually interdependent, creating and inseparable. The epistemic frameworks of both the theories of categorial conversion and info-statics are are shown to be linked to the epistemic frameworks of possibility and necessity, while the epistemic frameworks of the theories of philosophical consciencism and info-dynamics are shown to be linked to the frameworks of probability and freedom over the spaces of neuro-decision-choice activities and the action space as they relate to the conditions established over the spaces of identification-transformation and imagination-reality dualities within which the concepts and the phenomena of phantom, ill-posed, unresolved, unintended and other problems are defined and discussed in relation to paradigms of thought and cognitive capacity limitations in knowing. The knowing and information-knowledge processes are shown to be cognitive journeys between variety ignorance to variety knowledge in the space of ignorance-knowledge dualities as a sub-space of the space of actual-potential dualities, where the journeys are constrained by informogy, categorial conversion is an epistemic ation-knowledge inequality conditions with uncertainty and risks.
A Random Point Initialization Approach to Image Segmentation with Variational Level-sets

Image segmentation is an essential component in many image processing and computer vision tasks. The primary goal of image segmentation is to simplify an image for easier analysis, and there are two broad approaches for achieving this: edge based methods, which extract the boundaries of specific known objects, and region based methods, which partition the image into regions that are statistically homogeneous. One of the more prominent edge finding methods, known as the level set method, evolves a zero-level contour in the image plane with gradient descent until the contour has converged to the object boundaries. While the classical level set method and its variants have proved successful in segmenting real images, they are susceptible to becoming stuck in noisy regions of the image plane without a priori knowledge of the image and they are unable to provide details beyond object outer boundary locations. We propose a modification to the variational level set image segmentation method that can quickly detect object boundaries by making use of random point initialization. We demonstrate the efficacy of our approach by comparing the performance of our method on real images to that of the prominent Canny Method.
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of  Gaussian Processes

Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous. The present paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning, where a Gaussian process (GP) prior is presumed on the sought value function, and instantaneous rewards are probabilistically generated based on value function evaluations at two consecutive states. Capitalizing on a random feature-based approximant of the GP prior, an online scalable (OS) approach, termed {OS-GPTD}, is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To benchmark the performance of OS-GPTD even in an adversarial setting, where the modeling assumptions are violated, complementary worst-case analyses are performed by upper-bounding the cumulative Bellman error as well as the long-term reward prediction error, relative to their counterparts from a fixed value function estimator with the entire state-reward trajectory in hindsight. Moreover, to alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme, termed OS-EGPTD, that can jointly infer the value function, and select interactively the EGP kernel on-the-fly. Finally, performances of the novel OS-(E)GPTD schemes are evaluated on two benchmark problems.
Dynamic Latent Dirichlet Allocation Tracks Evolution of Online Hate Topics

Not only can online hate content spread easily between social media platforms, but its focus can also evolve over time. Machine learning and other artificial intelligence (AI) tools could play a key role in helping human moderators understand how such hate topics are evolving online. Latent Dirichlet Allocation (LDA) has been shown to be able to identify hate topics from a corpus of text associated with online communities that promote hate. However, applying LDA to each day’s data is impractical since the inferred topic list from the optimization can change abruptly from day to day, even though the underlying text and hence topics do not typically change this quickly. Hence, LDA is not well suited to capture the way in which hate topics evolve and morph. Here we solve this problem by showing that a dynamic version of LDA can help capture this evolution of topics surrounding online hate. Specifically, we show how standard and dynamical LDA models can be used in conjunction to analyze the topics over time emerging from extremist communities across multiple moderated and unmoderated social media platforms. Our dataset comprises material that we have gathered from hate-related communities on Facebook, Telegram, and Gab during the time period January-April 2021. We demonstrate the ability of dynamic LDA to shed light on how hate groups use different platforms in order to propagate their cause and interests across the online multiverse of social media platforms.
FABLE WRITING SKILLS IN THE INDONESIAN LANGUAGE THROUGH THE USE OF SHORT FILM LEARNING MEDIA IN CLASS VII STUDENTS OF SMPN 2 SEMAAPURA

This study was aimed at analyzing (1) the students' ability in writing Indonesian fable stories before and after utilizing short film as a learning media; (2) the effectiveness of using short film in enhancing the ability to write Indonesian fable in the seventh-grade students of SMP Negeri 2 Semarapura.This research was descriptive qualitative research.In line with that, the data collection techniques were tests, questionnaires, and observation.The test was passed twice, namely for pre-test before being given treatment and post-test after being given treatment.Data collection was continued by spreading questionnaires via Google Form link after the post-test, and observation was conducted while applying the treatment in class.The result revealed that students' initial ability in writing Indonesian fable was not yet in the 'good' category based on the pre-test.This is proven by the validity that there were only one out of fifty students achieved above 70 in the 'good' category while 80% of the other students achieved less than 70.Students' writing ability was then improved after being given treatment in utilizing short film as learning media which was proven by none of the students who achieved the 'very poor' or 'less' scores categories.The low consequence of the students' pre-test was due to the deficiency of information they had about Indonesian fable stories written.In addition, the application of short films as learning media in the learning process was considered to have a consequential effect on improving the ability to write Indonesian fables.Based on the results of the questionnaire that there were 29 (58%) students considered the usage of the short film in the 'very good' category, there were 16 (32%) students who determined the 'good' category, while 5 ( 10%) other students assumed that the usage of the short film is categorized as 'enough'.In addition, the outcomes of observation also confirmed that students were more active and convinced in the learning process when the treatment was applied, so it impacted the improvement of better writing ability in Indonesian fable stories.