OpenRaiser/Intern-Atlas huggingface dataset:
The Invariant Ground Truth of Affect
Venue: arXiv cs.AI 2022
Year: 2022

Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is predominately attributed to the affect labels which inadvertently include biases inherent to the subjective nature of emotion and its labeling. The response to such limitations is usually augmenting the dataset with more annotations per data point; however, this is not possible when we are interested in self-reports via first-person annotation. Moreover, outlier detection methods based on inter-annotator agreement only consider the annotations themselves and ignore the context and the corresponding affect manifestation. This paper reframes the ways one may obtain a reliable ground truth of affect by transferring aspects of causation theory to affective computing. In particular, we assume that the ground truth of affect can be found in the causal relationships between elicitation, manifestation and annotation that remain \emph{invariant} across tasks and participants. To test our assumption we employ causation inspired methods for detecting outliers in affective corpora and building affect models that are robust across participants and tasks. We validate our methodology within the domain of digital games, with experimental results showing that it can successfully detect outliers and boost the accuracy of affect models. To the best of our knowledge, this study presents the first attempt to integrate causation tools in affective computing, making a crucial and decisive step towards general affect modeling.
Training speech emotion classifier without categorical annotations
Venue: arXiv cs.SD 2022
Year: 2022

There are two paradigms of emotion representation, categorical labeling and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional category using a mapping algorithm. We investigated the performances of a combination of three feature extractors, three neural network architectures, and three mapping algorithms on two different corpora. Our study shows the advantages and limitations of the classification via regression approach.
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
Venue: arXiv cs.LG 2022
Year: 2022

Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend to all neighbors of the central node when aggregating the features. In this paper, we show that a large portion of the neighbors are irrelevant to the central nodes in many real-world graphs, and can be excluded from neighbor aggregation. Taking the cue, we present Selective Attention (SA) and a series of novel attention mechanisms for graph neural networks (GNNs). SA leverages diverse forms of learnable node-node dissimilarity to acquire the scope of attention for each node, from which irrelevant neighbors are excluded. We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms. Lastly, theoretical analysis on the expressive power of the proposed SATs and a comprehensive empirical study of the SATs on challenging real-world datasets against state-of-the-art GNNs are presented to demonstrate the effectiveness of SATs.
A Study and Analysis of Manuscript Publications in the Open Access Journals
Venue: arXiv cs.DL 2022
Year: 2022

The purpose of this study is to analyze the research article publishing with special reference to preparing to publish and peer reviewing. Peer reviewing is the process required for standardizing any publications. Manuscript writing is an art. Though it appears to be simple there is a lot of effort required. Peer reviewing is the process that eliminates articles that do not meet the standard of the journals and the scope of the journals. The study investigated authors views on manuscript submissions to the publishing process. There are 375 samples selected for this study who have experienced publishing journals listed in refereed journals. For the selection of the sample 50 ScimagoJR Library and Information Science open access journals between 2019 to 2021 are verified by the authors.
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
Venue: arXiv cs.CL 2022
Year: 2022

Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control for story preference utilize prompt engineering which is labor intensive and often inconsistent. They may also use logit-manipulation methods which require annotated datasets to exist for the desired attributes. To address these issues, we first train a contrastive bi-encoder model to align stories with corresponding human critiques, named CARP, building a general purpose preference model. This is subsequently used as a reward function to fine-tune a generative language model via reinforcement learning. However, simply fine-tuning a generative language model with a contrastive reward model does not always reliably result in a story generation system capable of generating stories that meet user preferences. To increase story generation robustness we further fine-tune the contrastive reward model using a prompt-learning technique. A human participant study is then conducted comparing generations from our full system, ablations, and two baselines. We show that the full fine-tuning pipeline results in a story generator preferred over a LLM 20x as large as well as logit-based methods. This motivates the use of contrastive learning for general purpose human preference modeling.
Nobody Wants to Work Anymore: An Analysis of r/antiwork and the Interplay between Social and Mainstream Media during the Great Resignation
Venue: arXiv cs.CY 2022
Year: 2022

r/antiwork is a Reddit community that focuses on the discussion of worker exploitation, labour rights and related left-wing political ideas (e.g. universal basic income). In late 2021, r/antiwork became the fastest growing community on Reddit, coinciding with what the mainstream media began referring to as the Great Resignation. This same media coverage was attributed with popularising the subreddit and, therefore, accelerating its growth. In this article, we explore how the r/antiwork community was affected by the exponential increase in subscribers and the media coverage that chronicled its rise. We investigate how subreddit activity changed over time, the behaviour of heavy and light users, and how the topical nature of the discourse evolved with the influx of new subscribers. We report that, despite the continuing rise of subscribers well into 2022, activity on the subreddit collapsed after January 25th 2022, when a moderator's Fox news interview was widely criticised. While many users never commented again, longer running trends of users' posting and commenting behaviour did not change. Finally, while many users expressed their discontent at the changing nature of the subreddit as it became more popular, we found no evidence of major shifts in the topical content of discussion over the period studied, with the exception of the introduction of topics related to seasonal events (e.g. holidays, such as Thanksgiving) and ongoing developments in the news (e.g. working from home and the curtailing of reproductive rights in the United States).
Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions
Venue: arXiv cs.CV 2022
Year: 2022

In this work, we compare five deep learning solutions to automatically segment the resection cavity in postoperative MRI. The proposed methods are based on the same 3D U-Net architecture. We use a dataset of postoperative MRI volumes, each including four MRI sequences and the ground truth of the corresponding resection cavity. Four solutions are trained with a different MRI sequence. Besides, a method designed with all the available sequences is also presented. Our experiments show that the method trained only with the T1 weighted contrast-enhanced MRI sequence achieves the best results, with a median DICE index of 0.81.
Surface abnormality detection in medical and inspection systems using energy variations in co-occurrence matrixes
Venue: arXiv cs.CV 2022
Year: 2022

Detection of surface defects is one of the most important issues in the field of image processing and machine vision. In this article, a method for detecting surface defects based on energy changes in co-occurrence matrices is presented. The presented method consists of two stages of training and testing. In the training phase, the co-occurrence matrix operator is first applied on healthy images and then the amount of output energy is calculated. In the following, according to the changes in the amount of energy, a suitable feature vector is defined, and with the help of it, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective parts are distinguished from the healthy ones. In the results section, the mentioned method has been applied on stone and ceramic images and its detection accuracy has been calculated and compared with some previous methods. Among the advantages of the presented method, we can mention high accuracy, low calculations and compatibility with all types of levels due to the use of the training stage. The proposed approach can be used in medical applications to detect abnormalities such as diseases. So, the performance is evaluated on 2d-hela dataset to classify cell phenotypes. The proposed approach provides about 89.56 percent accuracy on 2d-hela.
Awareness and usage of SWAYAM course among library and information science students a survey
Venue: arXiv cs.DL 2022
Year: 2022

The Government of India initiated SWAYAM (Study Webs of Active-learning for Young Aspiring Minds), where the objective of the programme is to take the best teaching and learning resources to all with no costs. To find out the awareness and usage of SWAYAM courses among Library and Information science students, survey method of research used. To collect data from selective Universities students a structured questionnaire was prepared. The results show that most of the responses received from Annamalai University, majority respondents are aware of SWAYAM course. Respondents are aware of these courses through their teachers. 74.73 percent of respondents replied that their University providing orientation programme on SWAYAM. Half of the respondents prefer Word file format for submitting their assignment. More than 82 percent of respondents are spending 1 to 3 hours for SWAYAM course. Half of the respondents are agreed SWAYAM courses help to gain new knowledge and supports life-long learning. Further, the result reveals that the numbers of students continue their enrolled courses and only less number were discontinued due to some reasons
ENTS: An Edge-native Task Scheduling System for Collaborative Edge Computing
Venue: arXiv cs.NI 2022
Year: 2022

Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge nodes. Recently, there has been a trend to orchestrate and schedule containerized application workloads in CEC, while Kubernetes has become the de-facto standard broadly adopted by the industry and academia. However, Kubernetes is not preferable for CEC because its design is not dedicated to edge computing and neglects the unique features of edge nativeness. More specifically, Kubernetes primarily ensures resource provision of workloads while neglecting the performance requirements of edge-native applications, such as throughput and latency. Furthermore, Kubernetes neglects the inner dependencies of edge-native applications and fails to consider data locality and networking resources, leading to inferior performance. In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status. We showcase the superior efficacy of ENTS with a case study on data streaming applications. We mathematically formulate a joint task allocation and flow scheduling problem that maximizes the job throughput. We design two novel online scheduling algorithms to optimally decide the task allocation, bandwidth allocation, and flow routing policies. The extensive experiments on a real-world edge video analytics application show that ENTS achieves 43\%-220\% higher average job throughput compared with the state-of-the-art.
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing
Venue: arXiv cs.CV 2022
Year: 2022

We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing (e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss how CNNs use convolution operations to extract informative features (e.g., geometric patterns and textures) from the such representations to predict emergent properties and phenomena and/or to identify anomalies. We also discuss how CNNs can exploit color as a key source of information, which enables the use of modern computer vision hardware (e.g., infrared, thermal, and hyperspectral cameras). We illustrate the concepts using diverse case studies arising in spectral analysis, molecule design, sensor design, image-based control, and multivariate process monitoring.
Learning to Autonomously Reach Objects with NICO and Grow-When-Required Networks
Venue: arXiv cs.RO 2022
Year: 2022

The act of reaching for an object is a fundamental yet complex skill for a robotic agent, requiring a high degree of visuomotor control and coordination. In consideration of dynamic environments, a robot capable of autonomously adapting to novel situations is desired. In this paper, a developmental robotics approach is used to autonomously learn visuomotor coordination on the NICO (Neuro-Inspired COmpanion) platform, for the task of object reaching. The robot interacts with its environment and learns associations between motor commands and temporally correlated sensory perceptions based on Hebbian learning. Multiple Grow-When-Required (GWR) networks are used to learn increasingly more complex motoric behaviors, by first learning how to direct the gaze towards a visual stimulus, followed by learning motor control of the arm, and finally learning how to reach for an object using eye-hand coordination. We demonstrate that the model is able to deal with an unforeseen mechanical change in the NICO's body, showing the adaptability of the proposed approach. In evaluations of our approach, we show that the humanoid robot NICO is able to reach objects with a 76% success rate.
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent
Venue: arXiv cs.AI 2022
Year: 2022

Legged locomotion is widespread in nature and has inspired the design of current robots. The controller of these legged robots is often realized as one centralized instance. However, in nature, control of movement happens in a hierarchical and decentralized fashion. Introducing these biological design principles into robotic control systems has motivated this work. We tackle the question whether decentralized and hierarchical control is beneficial for legged robots and present a novel decentral, hierarchical architecture to control a simulated legged agent. Three different tasks varying in complexity are designed to benchmark five architectures (centralized, decentralized, hierarchical and two different combinations of hierarchical decentralized architectures). The results demonstrate that decentralizing the different levels of the hierarchical architectures facilitates learning of the agent, ensures more energy efficient movements as well as robustness towards new unseen environments. Furthermore, this comparison sheds light on the importance of modularity in hierarchical architectures to solve complex goal-directed tasks. We provide an open-source code implementation of our architecture (https://github.com/wzaielamri/hddrl).
A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning
Venue: arXiv cs.RO 2022
Year: 2022

We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. To perform the tasks successfully and effectively, the robots have to adopt intelligent collaboration strategies, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot as well as information about whether two robots can perform a handover action by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging GTAMP domains and show that it can generate high-quality task-and-motion plans with respect to the planning time, the resulting plan length and the number of objects moved compared to two state-of-the-art baselines.
Meta Transferring for Deblurring
Venue: arXiv cs.CV 2022
Year: 2022

Most previous deblurring methods were built with a generic model trained on blurred images and their sharp counterparts. However, these approaches might have sub-optimal deblurring results due to the domain gap between the training and test sets. This paper proposes a reblur-deblur meta-transferring scheme to realize test-time adaptation without using ground truth for dynamic scene deblurring. Since the ground truth is usually unavailable at inference time in a real-world scenario, we leverage the blurred input video to find and use relatively sharp patches as the pseudo ground truth. Furthermore, we propose a reblurring model to extract the homogenous blur from the blurred input and transfer it to the pseudo-sharps to obtain the corresponding pseudo-blurred patches for meta-learning and test-time adaptation with only a few gradient updates. Extensive experimental results show that our reblur-deblur meta-learning scheme can improve state-of-the-art deblurring models on the DVD, REDS, and RealBlur benchmark datasets.
Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network
Venue: arXiv cs.SI 2022
Year: 2022

Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics of a multi-commodity flow network comprehensively, and design resilience metrics that measure the node-level and network-level dependence of single-sourcing, distant, or non-adjacent suppliers/customers in food supply chains. We conduct a case study of the US state-level agricultural multi-commodity flow network with hierarchical commodity types. The results indicate that, by leveraging GeoKG, our method supports measuring both node-level and network-level resilience across space and over time and also helps discover concentration patterns of agricultural resources in the spatial network at different geographic scales.
Multi-trainer Interactive Reinforcement Learning System
Venue: arXiv cs.LG 2022
Year: 2022

Interactive reinforcement learning can effectively facilitate the agent training via human feedback. However, such methods often require the human teacher to know what is the correct action that the agent should take. In other words, if the human teacher is not always reliable, then it will not be consistently able to guide the agent through its training. In this paper, we propose a more effective interactive reinforcement learning system by introducing multiple trainers, namely Multi-Trainer Interactive Reinforcement Learning (MTIRL), which could aggregate the binary feedback from multiple non-perfect trainers into a more reliable reward for an agent training in a reward-sparse environment. In particular, our trainer feedback aggregation experiments show that our aggregation method has the best accuracy when compared with the majority voting, the weighted voting, and the Bayesian method. Finally, we conduct a grid-world experiment to show that the policy trained by the MTIRL with the review model is closer to the optimal policy than that without a review model.
On the Computation of Distributed Knowledge as the Greatest Lower Bound of Knowledge
Venue: arXiv cs.MA 2022
Year: 2022

Let $L$ be a finite lattice and $\mathcal{E}(L)$ be the set of join endomorphisms of $L$. We consider the problem of given $L$ and $f,g \in \mathcal{E}(L)$, finding the greatest lower bound $f \sqcap_{{\scriptsize \mathcal{E}(L)}} g$ in the lattice $\mathcal{E}(L)$. (1) We show that if $L$ is distributive, the problem can be solved in time $O(n)$ where $n=| L |$. The previous upper bound was $O(n^2)$. (2) We provide new algorithms for arbitrary lattices and give experimental evidence that they are significantly faster than the existing algorithm. (3) We characterize the standard notion of distributed knowledge of a group as the greatest lower bound of the join-endomorphisms representing the knowledge of each member of the group. (4) We show that deciding whether an agent has the distributed knowledge of two other agents can be computed in time $O(n^2)$ where $n$ is the size of the underlying set of states. (5) For the special case of $S5$ knowledge, we show that it can be decided in time $O(nα_{n})$ where $α_{n}$ is the inverse of the Ackermann function.