Reviewerly/exHarmony huggingface dataset:
VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract)

Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved. In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.
Goal Recognition as Reinforcement Learning

Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key issues. First, encoding these dynamics requires careful design by a domain expert, which is often not robust to noise at recognition time. Second, existing approaches often need costly real-time computations to reason about the likelihood of each potential goal. In this paper, we develop a framework that combines model-free reinforcement learning and goal recognition to alleviate the need for careful, manual domain design, and the need for costly online executions. This framework consists of two main stages: Offline learning of policies or utility functions for each potential goal, and online inference. We provide a first instance of this framework using tabular Q-learning for the learning stage, as well as three measures that can be used to perform the inference stage. The resulting instantiation achieves state-of-the-art performance against goal recognizers on standard evaluation domains and superior performance in noisy environments.
Reasoning about Causal Models with Infinitely Many Variables

Generalized structural equations models (GSEMs) (Peters and Halpern 2021), are, as the name suggests, a generalization of structural equations models (SEMs). They can deal with (among other things) infinitely many variables with infinite ranges, which is critical for capturing dynamical systems. We provide a sound and complete axiomatization of causal reasoning in GSEMs that is an extension of the sound and complete axiomatization provided by Halpern (2000) for SEMs. Considering GSEMs helps clarify what properties Halpern's axioms capture.
Adaptive Hypergraph Neural Network for Multi-Person Pose Estimation

This paper proposes a novel two-stage hypergraph-based framework, dubbed ADaptive Hypergraph Neural Network (AD-HNN) to estimate multiple human poses from a single image, with a keypoint localization network and an Adaptive-Pose Hypergraph Neural Network (AP-HNN) added onto the former network. For providing better guided representations of AP-HNN, we employ a Semantic Interaction Convolution (SIC) module within the initial localization network to acquire more explicit predictions. Build upon this, we design a novel adaptive hypergraph to represent a human body for capturing high-order semantic relations among different joints. Notably, it can adaptively adjust the relations between joints and seek the most reasonable structure for the variable poses to benefit the keypoint localization. These two stages are combined to be trained in an end-to-end fashion. Unlike traditional Graph Convolutional Networks (GCNs) that are based on a fixed tree structure, AP-HNN can deal with ambiguity in human pose estimation. Experimental results demonstrate that the AD-HNN achieves state-of-the-art performance both on the MS-COCO, MPII and CrowdPose datasets.
A geometric framework for outlier detection in high-dimensional data

Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework that exploits the metric structure of a data set. Our approach rests on the manifold assumption, i.e., that the observed, nominally high-dimensional data lie on a much lower dimensional manifold and that this intrinsic structure can be inferred with manifold learning methods. We show that exploiting this structure significantly improves the detection of outlying observations in high-dimensional data. We also suggest a novel, mathematically precise, and widely applicable distinction between distributional and structural outliers based on the geometry and topology of the data manifold that clarifies conceptual ambiguities prevalent throughout the literature. Our experiments focus on functional data as one class of structured high-dimensional data, but the framework we propose is completely general and we include image and graph data applications. Our results show that the outlier structure of high-dimensional and non-tabular data can be detected and visualized using manifold learning methods and quantified using standard outlier scoring methods applied to the manifold embedding vectors.
Estimation of Crystal Orientation of Grains on Polycrystalline Silicon Substrate by Recurrent Neural Network

Abstract To analyze crystal defects in polycrystalline silicon substrates, it is necessary to measure the crystal orientation at high speed. In this paper, we propose a method for simultaneous and fast estimation of the crystal orientation of an entire substrate by measuring the reflected light from a single rotation of a light source. The effectiveness of the proposed method is verified by the orientation estimation experiments. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Learning Logic Programs Though Divide, Constrain, and Conquer

We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.
End-to-End Line Drawing Vectorization

Vector graphics is broadly used in a variety of forms, such as illustrations, logos, posters, billboards, and printed ads. Despite its broad use, many artists still prefer to draw with pen and paper, which leads to a high demand of converting raster designs into the vector form. In particular, line drawing is a primary art and attracts many research efforts in automatically converting raster line drawings to vector form. However, the existing methods generally adopt a two-step approach, stroke segmentation and vectorization. Without vector guidance, the raster-based stroke segmentation frequently obtains unsatisfying segmentation results, such as over-grouped strokes and broken strokes. In this paper, we make an attempt in proposing an end-to-end vectorization method which directly generates vectorized stroke primitives from raster line drawing in one step. We propose a Transformer-based framework to perform stroke tracing like human does in an automatic stroke-by-stroke way with a novel stroke feature representation and multi-modal supervision to achieve vectorization with high quality and fidelity. Qualitative and quantitative evaluations show that our method achieves state of the art performance.
Backprop-Free Reinforcement Learning with Active Neural Generative Coding

In humans, perceptual awareness facilitates the fast recognition and extraction of information from sensory input. This awareness largely depends on how the human agent interacts with the environment. In this work, we propose active neural generative coding, a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. Specifically, we develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. We demonstrate on several simple control problems that our framework performs competitively with deep Q-learning. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
Improved Language Models for ASR using Written Language Text

The performance of an Automatic Speech Recognition (ASR) engine primarily depends on ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> ) the acoustic model (AM), (b) the language model (LM) and (c) the lexicon (Lx), While the contribution of each block to the overall performance of an ASR cannot be measured separately, a good LM helps in performance improvement in case of a domain specific ASR at a smaller cost. Generally, LM is greener compared to building AM and is much easier to build, for a domain specific ASR because it requires only domain specific text corpora. Traditionally, because of its ready availability, written language text (WLT) corpora has been used to build LM though there is an agreement that there a significant difference between WLT and spoken language text (SLT). In this paper, we explore methods and techniques that can be used to convert WLT into a form that realizes a better LM to support ASR performance.
Increasing Accuracy of Random Forest Algorithm by Decreasing Variance

This study aims to add a level of randomization to the process of building a tree within a random forest. This extra randomization step is achieved by adopting a sectioning technique of a feature’s set of values to search for the optimal threshold at each tree node split. According to the proposed section-based random forest algorithm (SBRF), on each node split of the decision tree, the following steps are performed: first, sorting the chosen feature’s values, then dividing them into equal sections; next, randomly pick a candidate threshold from each section, evaluate each candidate threshold against a predetermined criterion, and finally, choose the best candidate threshold among them. As a result, SBRF produces models of less variance and not higher bias than the models created by random forest, consequently decreasing the generalization error.
Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg

Despite the advances in multi-person pose estimation, state-of-the-art techniques only deliver the human pose structure.Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat map. For instance segmentation, PosePlusSeg generates a mask offset where keypoint is defined as a centroid for the pixels in the embedding space, enabling instance-level segmentation for the human class. Finally, we propose a new pose and instance segmentation algorithm that enables PosePlusSeg to determine the joint structure of the human pose and instance segmentation. Experiments using the COCO challenging dataset demonstrate that PosePlusSeg copes better with challenging scenarios, like occlusions, en-tangled limbs, and overlapped people. PosePlusSeg outperforms state-of-the-art detection-based approaches achieving a 0.728 mAP for human pose estimation and a 0.445 mAP for instance segmentation. Code has been made available at: https://github.com/RaiseLab/PosePlusSeg.
Information Technology Governance Audit at XYZ College Using COBIT Framework 2019

Reliable and up-to-date information services are one of the parameters for the achievement of an organization's performance, including for a university, which is currently required to be able to adapt to technological advances to produce graduates who can master information technology to support the competence of their respective fields. The curriculum changes launched by the current government are also proof that the application of information technology in higher education is a must. Seeing this, universities have a difficult task, especially at the implementation stage of implementing information technology, because they must prepare not only infrastructure but also human resources who can develop good information system governance. Therefore it is necessary to carry out an IT governance audit. From the results of observations made, XYZ College has utilized information technology in academic services and other information services. However, it was found that information technology governance does not yet have a standard so that it is considered not to be able to meet the desired goals, and instead will cause various problems including uncontrolled operational costs and other problems. The purpose of this study is to measure the performance of information technology governance at XYZ College using the COBIT 2019 framework. Based on the results of an audit conducted from 7 EGIT components, namely processes, organizational structure, information flow and items, people skills and competencies, policies and procedures, culture, ethical behavior, and service infrastructure and application are on average at level 3, it can be concluded that the application of information technology has been running, the infrastructure is adequate but does not have good governance procedures or standards. It is recommended that XYZ Higher Education make standards for information technology governance as well as periodic evaluations of the use of all components of information technology, both software, hardware, and brainware.
Constrained Prescriptive Trees via Column Generation

With the abundance of available data, many enterprises seek to implement data-driven prescriptive analytics to help them make informed decisions. These prescriptive policies need to satisfy operational constraints, and proactively eliminate rule conflicts, both of which are ubiquitous in practice. It is also desirable for them to be simple and interpretable, so they can be easily verified and implemented. Existing approaches from the literature center around constructing variants of prescriptive decision trees to generate interpretable policies. However, none of the existing methods is able to handle constraints. In this paper, we propose a scalable method that solves the constrained prescriptive policy generation problem. We introduce a novel path-based mixed-integer program (MIP) formulation which identifies a (near) optimal policy efficiently via column generation. The policy generated can be represented as a multiway-split tree which is more interpretable and informative than binary-split trees due to its shorter rules. We demonstrate the efficacy of our method with extensive computational experiments on both synthetic and real datasets.
Deconfounding Physical Dynamics with Global Causal Relation and Confounder Transmission for Counterfactual Prediction

Discovering the underneath causal relations is the fundamental ability for reasoning about the surrounding environment and predicting the future states in the physical world. Counterfactual prediction from visual input, which requires simulating future states based on unrealized situations in the past, is a vital component in causal relation tasks. In this paper, we work on the confounders that have effect on the physical dynamics, including masses, friction coefficients, etc., to bridge relations between the intervened variable and the affected variable whose future state may be altered. We propose a neural network framework combining Global Causal Relation Attention (GCRA) and Confounder Transmission Structure (CTS). The GCRA looks for the latent causal relations between different variables and estimates the confounders by capturing both spatial and temporal information. The CTS integrates and transmits the learnt confounders in a residual way, so that the estimated confounders can be encoded into the network as a constraint for object positions when performing counterfactual prediction. Without any access to ground truth information about confounders, our model outperforms the state-of-the-art method on various benchmarks by fully utilizing the constraints of confounders. Extensive experiments demonstrate that our model can generalize to unseen environments and maintain good performance.
Anisotropic Fourier Features for Neural Image-Based Rendering and Relighting

Recent neural rendering techniques have greatly benefited image-based modeling and relighting tasks. They provide a continuous, compact, and parallelable representation by modeling the plenoptic function as multilayer perceptrons (MLPs). However, vanilla MLPs suffer from spectral biases on multidimensional datasets. Recent rescues based on isotropic Fourier features mapping mitigate the problem but still fall short of handling heterogeneity across different dimensions, causing imbalanced regression and visual artifacts such as excessive blurs. We present an anisotropic random Fourier features (RFF) mapping scheme to tackle spectral biases. We first analyze the influence of bandwidth from a different perspective: we show that the optimal bandwidth exhibits strong correlations with the frequency spectrum of the training data across various dimensions. We then introduce an anisotropic feature mapping scheme with multiple bandwidths to model the multidimensional signal characteristics. We further propose an efficient bandwidth searching scheme through iterative golden-section search that can significantly reduce the training overload from polynomial time to logarithm. Our anisotropic scheme directly applies to neural surface light-field rendering and image-based relighting. Comprehensive experiments show that our scheme can more faithfully model lighting conditions and object features as well as preserve fine texture details and smooth view transitions even when angular and spatial samples are highly imbalanced.
Investigating the students’ behavior towards the temptation to do academic misconduct in higher education: The moderation of religiosity

The purpose of this study was to investigate the factors that influence cheating intention (CI) and cheating behavior (CB) on accounting students with Islamic religiosity (IR) as a moderating factor. This study used the theory of planned behavior (TPB) concept in predicting academic cheating. The sample of this study used a cluster sampling approach. The data collecting technique used a survey method with a questionnaire. The population of this study was accounting students at an Islamic university in Yogyakarta, Indonesia with a total of 146 respondents. Regression analysis was employed to test this hypothesis by using a structural equation modeling (SEM) approach using smart-PLS. The results of this study indicated that attitude had a negative effect on CI and perceived behavioral control (PBC) had a positive effect on CI. Furthermore, the level of religiosity had a negative effect on CI. This finding also implied that religiosity was unable to moderate TPB to reduce students’ desire to commit academic misconduct. This paper contributes to the literature by providing empirical evidence of the moderating effect of religiosity on academic cheating behavior of students in higher education.
Learning from Label Proportions with Prototypical Contrastive Clustering

The use of priors to avoid manual labeling for training machine learning methods has received much attention in the last few years. One of the critical subthemes in this regard is Learning from Label Proportions (LLP), where only the information about class proportions is available for training the models. While various LLP training settings verse in the literature, most approaches focus on bag-level label proportions errors, often leading to suboptimal solutions. This paper proposes a new model that jointly uses prototypical contrastive learning and bag-level cluster proportions to implement efficient LLP classification. Our proposal explicitly relaxes the equipartition constraint commonly used in prototypical contrastive learning methods and incorporates the exact cluster proportions into the optimal transport algorithm used for cluster assignments. At inference time, we compute the clusters' assignment, delivering instance-level classification. We experimented with our method on two widely used image classification benchmarks and report a new state-of-art LLP performance, achieving results close to fully supervised methods.
Towards Automating Model Explanations with Certified Robustness Guarantees

Providing model explanations has gained significant popularity recently. In contrast with the traditional feature-level model explanations, concept-based explanations can provide explanations in the form of high-level human concepts. However, existing concept-based explanation methods implicitly follow a two-step procedure that involves human intervention. Specifically, they first need the human to be involved to define (or extract) the high-level concepts, and then manually compute the importance scores of these identified concepts in a post-hoc way. This laborious process requires significant human effort and resource expenditure due to manual work, which hinders their large-scale deployability. In practice, it is challenging to automatically generate the concept-based explanations without human intervention due to the subjectivity of defining the units of concept-based interpretability. In addition, due to its data-driven nature, the interpretability itself is also potentially susceptible to malicious manipulations. Hence, our goal in this paper is to free human from this tedious process, while ensuring that the generated explanations are provably robust to adversarial perturbations. We propose a novel concept-based interpretation method, which can not only automatically provide the prototype-based concept explanations but also provide certified robustness guarantees for the generated prototype-based explanations. We also conduct extensive experiments on real-world datasets to verify the desirable properties of the proposed method.
Reinforcement Learning with Stochastic Reward Machines

Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them. Our algorithm, based on constraint solving, learns minimal stochastic reward machines from the explorations of a reinforcement learning agent. This algorithm can easily be paired with existing reinforcement learning algorithms for reward machines and guarantees to converge to an optimal policy in the limit. We demonstrate the effectiveness of our algorithm in two case studies and show that it outperforms both existing methods and a naive approach for handling noisy reward functions.