Reviewerly/exHarmony huggingface dataset:
Hierarchical ConViT with Attention-Based Relational Reasoner for Visual Analogical Reasoning

Raven’s Progressive Matrices (RPMs) have been widely used to evaluate the visual reasoning ability of humans. To tackle the challenges of visual perception and logic reasoning on RPMs, we propose a Hierarchical ConViT with Attention-based Relational Reasoner (HCV-ARR). Traditional solution methods often apply relatively shallow convolution networks to visually perceive shape patterns in RPM images, which may not fully model the long-range dependencies of complex pattern combinations in RPMs. The proposed ConViT consists of a convolutional block to capture the low-level attributes of visual patterns, and a transformer block to capture the high-level image semantics such as pattern formations. Furthermore, the proposed hierarchical ConViT captures visual features from multiple receptive fields, where the shallow layers focus on the image fine details while the deeper layers focus on the image semantics. To better model the underlying reasoning rules embedded in RPM images, an Attention-based Relational Reasoner (ARR) is proposed to establish the underlying relations among images. The proposed ARR well exploits the hidden relations among question images through the developed element-wise attentive reasoner. Experimental results on three RPM datasets demonstrate that the proposed HCV-ARR achieves a significant performance gain compared with the state-of-the-art models. The source code is available at: https://github.com/wentaoheunnc/HCV-ARR.
Augmented reality for industrial quality inspection: An experiment assessing task performance and human factors

Augmented reality (AR) technologies promise to increase the flexibility and productivity of the workforce by providing real-time information to workers right where it is needed. Following Cognitive Load Theory and Attention Theory, AR assistance can reduce the burden on workers’ mental capacity while performing a task, thereby improving task performance. The benefits of AR on industrial activities like assembly or maintenance have been investigated extensively, but the literature on AR-assisted quality control, specifically, quality inspection, is scarce. This stands in contrast to the importance of industrial quality inspection and highlights the need for research on the topic. In this work, we develop an AR-based system for head-mounted displays (HMDs) that visualizes product defects directly on physical products. We investigate its effect on task performance and human factors through an experiment. Participants performed multiple quality inspections using real manufacturing products and equipment with the help of an AR HMD, a screen, or no additional help. We find increased task performance using the AR HMD system. In comparison to screen-based assistance, a moderating effect of task difficulty was observed. Specifically, AR was more beneficial to task completion times given a difficult task. Furthermore, the AR HMD system reduced mental workload and received a positive rating regarding user experience. Our work contributes to the research in industrial AR usage through a novel AR HMD system for quality inspection and provides so far missing empirical and theoretically grounded evidence on its benefits with regard to task performance and human factors.
HiEve: A Large-Scale Benchmark for Human-Centric Video Analysis in Complex Events

Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A complex event relates to dense crowds, anomalous individuals, or collective behaviors. However, limited by the scale and coverage of existing video datasets, few human analysis approaches have reported their performances on such complex events. To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd & complex events. It contains a record number of poses (>1M), the largest number of action instances (>56k) under complex events, as well as one of the largest numbers of trajectories lasting for longer time (with an average trajectory length of >480 frames). Based on its diverse annotation, we present two simple baselines for action recognition and pose estimation, respectively. They leverage cross-label information during training to enhance the feature learning in corresponding visual tasks. Experiments show that they could boost the performance of existing action recognition and pose estimation pipelines. More importantly, they prove the widely ranged annotations in HiEve can improve various video tasks. Furthermore, we conduct extensive experiments to benchmark recent video analysis approaches together with our baseline methods, demonstrating HiEve is a challenging dataset for human-centric video analysis. We expect that the dataset will advance the development of cutting-edge techniques in human-centric analysis and the understanding of complex events. The dataset is available at http://humaninevents.org
Using Terms and Conditions to apply Fundamental Rights to Content Moderation

Abstract Large online platforms provide an unprecedented means for exercising freedom of expression online and wield enormous power over public participation in the online democratic space. However, it is increasingly clear that their systems, where (automated) content moderation decisions are taken based on a platformʼs terms and conditions (T&amp;Cs), are fundamentally broken. Content moderation systems have been said to undermine freedom of expression, especially where important public interest speech ends up suppressed, such as speech by minority and marginalized groups. Indeed, these content moderation systems have been criticized for their overly vague rules of operation, inconsistent enforcement, and an overdependence on automation. Therefore, in order to better protect freedom of expression online, international human rights bodies and civil society organizations have argued that platforms “should incorporate directly” principles of fundamental rights law into their T&amp;Cs. Under EU law, and apart from a rule in the Terrorist Content Regulation, platforms had until recently no explicit obligation to incorporate fundamental rights into their T&amp;Cs. However, an important provision in the Digital Services Act (DSA) will change this. Crucially, Article 14 DSA lays down new rules on how platforms can enforce their T&amp;Cs, including that platforms must have “due regard” to the “fundamental rights” of users under the EU Charter of Fundamental Rights. In this article, we critically examine the topic of enforceability of fundamental rights via T&amp;Cs through the prism of Article 14 DSA. We ask whether this provision requires platforms to apply EU fundamental rights law and to what extent this may curb the power of Big Tech over online speech. We conclude that Article 14 will make it possible, in principle, to establish the indirect horizontal effect of fundamental rights in the relationship between online platforms and their users. But in order for the application and enforcement of T&amp;Cs to take due regard of fundamental rights, Article 14 must be operationalized within the framework of the international and European fundamental rights standards. If this is possible Article 14 may fulfil its revolutionary potential.
Efficient Large Scale Language Modeling with Mixtures of Experts

Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giridharan Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeffrey Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Veselin Stoyanov. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering

Answering open-domain questions requires world knowledge about in-context entities.As pre-trained Language Models (LMs) lack the power to store all required knowledge, external knowledge sources, such as knowledge graphs, are often used to augment LMs.In this work, we propose knOwledge REasOning empowered Language Model (OREOLM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively.In this way, LM guides KG to walk towards the desired answer, while the retrieved knowledge improves LM.By adopting OREOLM to RoBERTa and T5, we show significant performance gain, achieving state-of-art results in the Closed-Book setting.The performance enhancement is mainly from the KG reasoning's capacity to infer missing relational facts.In addition, OREOLM provides reasoning paths as rationales to interpret the model's decision.
Named Entity and Relation Extraction with Multi-Modal Retrieval

Multi-modal named entity recognition (NER) and relation extraction (RE) aim to leverage relevant image information to improve the performance of NER and RE.Most existing efforts largely focused on directly extracting potentially useful information from images (such as pixel-level features, identified objects, and associated captions).However, such extraction processes may not be knowledge aware, resulting in information that may not be highly relevant.In this paper, we propose a novel Multi-modal Retrieval based framework (MoRe).MoRe contains a text retrieval module and an imagebased retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively.Next, the retrieval results are sent to the textual and visual models respectively for predictions.Finally, a Mixture of Experts (MoE) module combines the predictions from the two models to make the final decision.Our experiments show that both our textual model and visual model can achieve state-of-the-art performance on four multi-modal NER datasets and one multimodal RE dataset.With MoE, the model performance can be further improved and our analysis demonstrates the benefits of integrating both textual and visual cues for such tasks.
Towards Artificial Intelligence Applications in Next Generation Cytopathology

Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with the computer assisted smear test using early generation models. Today, advanced machine learning, working on large image data sets, has been shown to perform classification, detection, and segmentation with remarkable accuracy and generalization in several domains. Deep learning algorithms, as a branch of machine learning, are thus attracting attention in digital pathology and cytopathology, providing feasible solutions for accurate and efficient cytological diagnoses, ranging from efficient cell counts to automatic classification of anomalous cells and queries over large clinical databases. The integration of machine learning with related next-generation technologies powered by AI, such as augmented/virtual reality, metaverse, and computational linguistic models are a focus of interest in health care digitalization, to support education, diagnosis, and therapy. In this work we will consider how all these innovations can help cytopathology to go beyond the microscope and to undergo a hyper-digitalized transformation. We also discuss specific challenges to their applications in the field, notably, the requirement for large-scale cytopathology datasets, the necessity of new protocols for sharing information, and the need for further technological training for pathologists.
Chaotic sparrow search algorithm with manta ray spiral foraging for engineering optimization

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Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra

To improve the accuracy of color image completion with missing entries, we present a recovery method based on generalized higher-order scalars. We extend the traditional second-order matrix model to a more comprehensive higher-order matrix equivalent, called the “t-matrix” model, which incorporates a pixel neighborhood expansion strategy to characterize the local pixel constraints. This “t-matrix” model is then used to extend some commonly used matrix and tensor completion algorithms to their higher-order versions. We perform extensive experiments on various algorithms using simulated data and publicly available images. The results show that our generalized matrix completion model and the corresponding algorithm compare favorably with their lower-order tensor and conventional matrix counterparts.
Task-adaptive physical reservoir computing

Abstract Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu 2 OSeO 3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co 8.5 Zn 8.5 Mn 3 (and FeGe).
Real-Time Simulation of Herds Moving Over Terrain

We present a method for animating herds of animals to follow terrain surfaces in real time. This method involves making modifications to Reynold&amp;rsquo;s boids algorithm. The modifications use local properties of the terrain and thus have low complexity. They produce naturally behaving herds that follow the terrain, swerving around hills and attempting to reduce energy expenditure. The terrain-following rule has a parameter that can be adjusted to produce different behaviors. Empirical analysis shows how this parameter affects energy expenditure of the animals while traveling over the terrain. See demo: http://consystlab.unl.edu/our_work/herds.html
The Search for Counterexamples in Human Reasoning

A major point of contention about human reasoning is whether or not individuals search for counterexamples to conclusions. This theoretical account rests on the semantic principle of validity: a conclusion is valid if and only if it allows for no counterexamples, i.e., possibilities in which the premises are true but the conclusion is false. Hence, by constructing a counterexample, reasoners are able to know that an inference is invalid. In the case of syllogisms, Polk and Newell have defended an account in terms of models, but argued that their explanation of individual differences gains little, if anything, by postulating a search for counterexamples. Bucciarelli and Johnson-Laird have shown that people are able to search for counterexamples if prompted to do so. To search for a search for counterexamples, authors carried out an experiment in which the participants had to evaluate eight inferences based on non-standard quantifiers.
Fast and Accurate Predictions of IDA*'s Performance

Korf, Reid and Edelkamp initiated a line of research for developing methods (KRE and later CDP) that predict the number of nodes expanded by IDA* for a given start state and cost bound. Independent of that, Chen developed a method (SS) that can also be used to predict the number of nodes expanded by IDA*. In this paper we advance both of these prediction methods. First, we develop a variant of CDP that can be orders of magnitude faster than CDP while producing exactly the same predictions. Second, we show how ideas developed in the KRE line of research can be used to substantially improve the predictions produced by SS. Third, we make an empirical comparison between our new enhanced versions of CDP and SS. Our experimental results point out that CDP is suitable for applications that require less accurate but very fast predictions, while SS is suitable for applications that require more accurate predictions but allow more computation time.
Generalized Poisson-Poisson Mixture Model for Misreported Counts with an Application to Smoking Data

The assumption that is usually made when modeling count data is that the response variable, which is the count, is correctly reported.Some counts might be over-or under-reported.We derive the Generalized Poisson-Poisson mixture regression (GPPMR) model that can handle accurate, underreported and overreported counts.The parameters in the model will be estimated via the maximum likelihood method.We apply the GPPMR model to a real-life data set.
Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community

Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors' use of gender stereotypes is associated with the community's ratings. We find that male over-representation and traditional gender stereotypes (e.g., dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.
Context Tree Maximizing

Recent developments in reinforcement learning for non-Markovianproblems witness a surge in history-based methods, among which weare particularly interested in two frameworks, PhiMDP and MC-AIXI-CTW. PhiMDP attempts to reduce the general RL problem, where the environment's states and dynamics are both unknown, toan MDP, while MC-AIXI-CTW incrementally learns a mixture of contexttrees as its environment model. The main idea of PhiMDP is toconnect generic reinforcement learning with classical reinforcementlearning. The first implementation of PhiMDP relies on astochastic search procedure for finding a tree that minimizes acertain cost function. This does not guarantee finding theminimizing tree, or even a good one, given limited search time. As aconsequence it appears that the approach has difficulties with largedomains. MC-AIXI-CTW is attractive in that it can incrementally andanalytically compute the internal model through interactions withthe environment. Unfortunately, it is computationally demanding dueto requiring heavy planning simulations at every single time step.We devise a novel approach called CTMRL, which analytically andefficiently finds the cost-minimizing tree. Instead of thecontext-tree weighting method that MC-AIXI-CTW is based on, we usethe closely related context-tree maximizing algorithm that selectsjust one single tree. This approach falls under the PhiMDPframework, which allows the replacement of the costly planningcomponent of MC-AIXI-CTW with simple Q-Learning. Our empiricalinvestigation show that CTMRL finds policies of quality as good as MC-AIXI-CTW's on sixdomains including a challenging Pacman domain, but in an order ofmagnitude less time.
Position Paper: Incremental Search Algorithms Considered Poorly Understood

Incremental search algorithms, such as D* Lite, reuse information from previous searches to speed up the current search and can thus solve sequences of similar search problems faster than Repeated A*, which performs repeated A* searches. In this position paper, we study goal-directed navigation in initially unknown terrain and point out that it is currently not well understood when D* Lite runs faster than Repeated A*. In general, it appears that Repeated A* runs faster than D* Lite for easy navigation problems (where the agent reaches the goal with only a small number of searches), which means that it runs faster than D* Lite quite often in practice. We draw two conclusions, namely that incremental search algorithms need to be evaluated in more diverse testbeds to improve our understanding of their properties and that they can be improved to be more competitive for easy navigation problems.
Prediction of Hypothyroidism Disease by Data Mining Technique

Background: Hypothyroidism is one of the endocrine diseases found in human being, it is not a immediate fatal disease, but progress in chronic status that lead to other diseases.Several machine learning techniques were applied to hypothyroidism for the prediction of hypothyroid medical diseases.Methods: The dataset used for UCI repository which has 3163 observations with 151 belongs to category hypothyroid disease.Considered with two missing data method, four imbalance data method and two kinds of classification models by using serval evaluation indexes to find a better model.Results: After comparison of these models, query_on_thyroxine and lithium were ignored and TBG was reconsidered in the model.A new RF imputation method was used.Finally, all variables model have 100% accuracy but cost time and money, only use TSH, FTI, TBG, TT4 and age variables model also can keep 0.9988 AUC, which will more useful in real case.Conclusion: Both all variables mode and five variables model have very high accuracy than previous studies, a nonparametric ensemble model is suggest in this case.But there also have some limitations like out-of-date data, hypothyroidisms type were not considered.
Bayesian Wavelet Regression for Spatial Estimation

We consider the problem of estimating the properties of an oil reservoir, like porosity and sand thickness, in an exploration scenario where only a few wells have been drilled.We use gamma ray records measured directly from the wells as well as seismic traces recorded around the wells.To model the association between the soil properties and the signals, we fit a linear regression model.Additionally we account for the spatial correlation structure of the observations using a correlation function that depends on the distance between two points.We transform the predictor variable using discrete wavelets and then perform a Bayesian variable selection using a Metropolis search.We obtain predictions of the properties over the whole reservoir providing a probabilistic quantification of their uncertainties, thanks to the Bayesian nature of our method.The cross-validated results show that a very high accuracy can be achieved even with a very small number of wavelet coefficients.