Reviewerly/exHarmony huggingface dataset:
Model Representation Considerations for Artificial Intelligence Opponent Development in Combat Games

The performance and behavior of an Artificial Intelligence (AI) opponent in games requires coordination of multiple agents and complex tasks depends on many design choices made during implementation. Currently, gaming agents developed with Reinforcement Learning (RL) methods are constructed to play the game, leading to natural design choices for observations, actions, and rewards that are congruent with a human player's actions and objectives. However, in simulation and serious games, the objective of the implemented opponent should be developed in a way that supports the learning objectives for the user, such as by including additional ground truth environment data in the observation space or action structure. Therefore, the reward structure for the AI needs to incorporate more sophisticated considerations than just whether the game was won or lost by the AI. In this way the design space for opponent AI in these settings is considerably broader than what is traditionally used for RL gaming AI. This paper considers the implications of observation representation and reward design for the AI agent and associated actions in the context of 2-player battlefield-type games that are not strictly zero-sum. Semi-cooperative and fully competitive models are considered. The environment in these games is a spatially extended battlefield in which agents must maneuver their forces to bring them into combat range of each other. The objective of the game is control over a pre-specified location in the game, and combat is executed via Lanchester attrition. We demonstrate the impact of aggregation on stochasticity of the model, where aggregation of the state model is controlled by various entropy-based metrics, as well as on the policy learned by an RL agent. Generalizations to alternative scenarios and objectives are discussed, as well as applications to the development of an AI combat opponent that can cohesively manage its forces over multiple scales.
Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering

Prediction of user preferences is a challenge, in particular when the objective is to learn them without requiring the user to provide a profile or a significant number of interactions. Many collaborative filtering algorithms exist but all of them require the availability of huge datasets of user information and expensive computations. In this paper, a novel architecture is introduced which aims to predict a new user’s interests in the context of previous users’ interactions with minimal feedback interactions. Here, a Siamese Network is used to generate an embedding space for data from existing users. This information is then used in a Gaussian Mixture Model to generate multiple soft clusters. Based on the embedding space, system responses to the user are generated using a Conditional Generative Adversarial Network which uses a vector drawn from the Gaussian Mixture in embedding space from the Siamese Network as the conditional input. The predictive model then interacts with the new user and based on their feedback adjusts the Gaussian Mixture to find the distribution with the highest probability of generating the user’s preferred data. The approach is applied in the context of an image generation task where the goal is to learn to generate images that match the preferences of the user using only a minimal number of direct user interactions. Testing in this domain has shown promising results that exemplify the ability of the approach to capture the user’s preferences while presenting only a minimal number of image examples.
A Comparison of Behavior Cloning Methods in Developing Interactive Opposing-Force Agents

Modern modeling and simulation environments, such as commercial games or military training systems, frequently demand interactive agents that exhibit realistic and responsive behavior in accordance with a predetermined specification, such as a storyboard or military tactics document.Traditional methods for creating agents, such as state machines or behavior trees, necessitate a significant amount of effort for developing state representations and transition processes through manual knowledge engineering. On the other hand, newer techniques for behavior generation, such as deep reinforcement learning, require a vast amount of training data (centuries in many cases), and there is no guarantee that the generated behavior will align with intended objectives and courses of action. This paper examines the application of behavior cloning approaches in designing interactive agents. In our approach, users start by defining desired behavior through straightforward methods such as state machine models or behavior trees. Behavior cloning methods are then used to transform ground-truth trajectory data sampled from these models into differentiable policies that are further refined through engagement with interactive game environments. This method results in improvements in training results when compared on dimensions of task performance and stability of training.
Investigating the risk of international construction projects using artificial-fuzzy neural networks technique

The purpose of implementing the risk management process is to identify and root the causes of each risk, analyze it, plan response methods and assign risks to appropriate factors, in order to effectively control the preferred risks. This article aims to analyze risk management in construction projects using fuzzy artificial neural networks in the form of a descriptive-survey study and in the category of applied research. Data analysis was done using MATLAB software using MATLAB software to ensure the soundness of the method and to implement project risk management in international construction projects, and the results indicate success in terms of risk.
A New Revolution in Healthcare Transformation Using Hyper-Automation Technologies

As someone who has been following the development of hyper-automation technologies in healthcare, I wanted to write to you about the many optimistic outcomes that these technologies have already produced. I am writing to express my excitement about many potential and benefits of hyper-automation technologies in healthcare. Hyper-automation, which includes the use of smart technologies such as artificial intelligence, low-code/no-code (LCNC) platforms, machine learning, robotics and other technologies to automate and optimize processes, has the possibility to transform healthcare in many ways [1].
Factors Affecting the Implementation of Accrual-Based Accounting Information Systems and The Impact on Accounting Information Quality-Evidence from Indonesia

This study aims to examine the effect of implementing regional financial regulations, employee competence and organizational commitment on the successful implementation of accrual-based accounting information systems and their impact on the quality of accounting information. This research is included in survey research with the type of investigation, this type of research is verification and explanatory research. This research is verificative (verificative research) and is explanatory (explanatory research) or causality (causal study) because this study aims to find out what and to what extent the factors that are estimated to affect a variable with the object of research h implementation of regional financial regulations (X1) , employee competence (X2), organizational commitment (X3), successful implementation of accrual-based accounting information system (Y) and quality of accounting information (Z). The population in this study were all Provincial/District/City Governments in Indonesia. The analytical design used in this research is quantitative data analysis with descriptive method. The quantitative analysis that will be carried out aims to determine the relationship between variables through hypothesis testing using structural equation modeling (Structural Equation Model-SEM) with the Partial Least Square (PLS) approach. The results of the study show that: (1) The implementation of regional financial regulations affects the success of the implementation of an accrual-based accounting information system, (2) Employee competence affects the success of the implementation of an accrual-based accounting information system, (3) Organizational commitment affects the success of the implementation of an accrual-based accounting information system. accruals, (4) The success of the application of accrual-based accounting information system affects the quality of accounting information.
Complete Graphical Language for Hermiticity-Preserving Superoperators

Universal and complete graphical languages have been successfully designed for pure state quantum mechanics, corresponding to linear maps between Hilbert spaces, and mixed states quantum mechanics, corresponding to completely positive superoperators. In this paper, we go one step further and present a universal and complete graphical language for Hermiticity-preserving superoperators. Such a language opens the possibility of diagrammatic compositional investigations of antilinear transformations featured in various physical situations, such as the Choi-Jamiołkowski isomorphism, spin-flip, or entanglement witnesses. Our construction relies on an extension of the ZW-calculus exhibiting a normal form for Hermitian matrices.
Model Fusion via Optimal Transport

Combining different models is a widely used paradigm in machine learning applications. While the most common approach is to form an ensemble of models and average their individual predictions, this approach is often rendered infeasible by given resource constraints in terms of memory and computation, which grow linearly with the number of models. We present a layer-wise model fusion algorithm for neural networks that utilizes optimal transport to (soft-) align neurons across the models before averaging their associated parameters. We show that this can successfully yield "one-shot" knowledge transfer (i.e, without requiring any retraining) between neural networks trained on heterogeneous non-i.i.d. data. In both i.i.d. and non-i.i.d. settings , we illustrate that our approach significantly outperforms vanilla averaging, as well as how it can serve as an efficient replacement for the ensemble with moderate fine-tuning, for standard convolutional networks (like VGG11), residual networks (like ResNet18), and multi-layer perceptrons on CIFAR10, CIFAR100, and MNIST. Finally, our approach also provides a principled way to combine the parameters of neural networks with different widths, and we explore its application for model compression. The code is available at the following link, https://github.com/sidak/otfusion.
HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform

In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. In this paper, we propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency. Our proposed approach achieves competitive subjective results compared to state-of-the-art end-to-end learned image compression methods and classic methods, while requiring less decoding time and offering human-friendly compression. Through empirical evaluation, we demonstrate the effectiveness of our proposed method in achieving outstanding performance, with more than 25% bit-rate saving at the same subjective quality.
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization

Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-document summarization (MDS), where the handling of cross-document information is more complex. Previous works either design new MDS architectures or apply PLMs bluntly with concatenated source documents as a reformulated SDS task. While the former does not utilize previous pre-training efforts and may not generalize well across different domains, the latter may not sufficiently attend to the intricate cross-document relationships unique to MDS tasks. Instead, we enforce hierarchy on both the encoder and decoder to better utilize a PLM to facilitate multi-document interactions for the MDS task. Across 10 MDS benchmarks from various domains, our method outperforms or is competitive with the previous best models, including those with additional MDS pre-training or with more parameters. It outperforms its corresponding PLM backbone by up to 3 Rouge-L and is favored by humans.
MIMEx: Intrinsic Rewards from Masked Input Modeling

Exploring in environments with high-dimensional observations is hard. One promising approach for exploration is to use intrinsic rewards, which often boils down to estimating "novelty" of states, transitions, or trajectories with deep networks. Prior works have shown that conditional prediction objectives such as masked autoencoding can be seen as stochastic estimation of pseudo-likelihood. We show how this perspective naturally leads to a unified view on existing intrinsic reward approaches: they are special cases of conditional prediction, where the estimation of novelty can be seen as pseudo-likelihood estimation with different mask distributions. From this view, we propose a general framework for deriving intrinsic rewards -- Masked Input Modeling for Exploration (MIMEx) -- where the mask distribution can be flexibly tuned to control the difficulty of the underlying conditional prediction task. We demonstrate that MIMEx can achieve superior results when compared against competitive baselines on a suite of challenging sparse-reward visuomotor tasks.
Resource-efficient utilization of quantum computers

The current state of quantum computing is commonly described as the Noisy Intermediate-Scale Quantum era. Available computers contain a few dozens of qubits and can perform a few dozens of operations before the inevitable noise erases all information encoded in the calculation. Even if the technology advances fast within the next years, any use of quantum computers will be limited to short and simple tasks, serving as subroutines of more complex classical procedures. Even for these applications the resource efficiency, measured in the number of quantum computer runs, will be a key parameter. Here we suggest a general optimization procedure for hybrid quantum-classical algorithms that allows finding the optimal approach with limited quantum resources. We demonstrate this procedure on a specific example of variational quantum algorithm used to find the ground state energy of a hydrogen molecule.
The Hessian perspective into the Nature of Convolutional Neural Networks

While Convolutional Neural Networks (CNNs) have long been investigated and applied, as well as theorized, we aim to provide a slightly different perspective into their nature -- through the perspective of their Hessian maps. The reason is that the loss Hessian captures the pairwise interaction of parameters and therefore forms a natural ground to probe how the architectural aspects of CNN get manifested in its structure and properties. We develop a framework relying on Toeplitz representation of CNNs, and then utilize it to reveal the Hessian structure and, in particular, its rank. We prove tight upper bounds (with linear activations), which closely follow the empirical trend of the Hessian rank and hold in practice in more general settings. Overall, our work generalizes and establishes the key insight that, even in CNNs, the Hessian rank grows as the square root of the number of parameters.
Adaptive Network Embedding with Arbitrary Multiple Information Sources in Attributed Graphs

Graph representation learning (a.k.a. network embedding) is a significant topic of network analysis, due to its effectiveness to support various graph inference tasks. In this paper, we study the representation learning with multiple information sources in attributed graphs. Recent studies usually focus on several specific sources (e.g., high-order proximity and node attributes) but few of them can be extended to incorporate other available sources not specified. In addition, most existing methods assume that all the integrated sources share consistent latent features but may ignore the possible inconsistency among them, lacking the required robustness. To address these issues, we propose a novel adaptive hybrid graph representation (AHGR) method from a view of graph reweighting, where each information source is formulated as a corresponding auxiliary graph, enabling AHGR to integrate arbitrary available information sources. Moreover, a new transition relation among the reweighted graphs is then introduced to perceive and resist the possible inconsistency among multiple sources, enhancing the robustness of AHGR. We verify the effectiveness of AHGR on a series of synthetic and real attributed graphs, where it presents superior performance over other baselines.
Pre-Training to Learn in Context

In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. To this end, we propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability by pre-training the model on a large collection of "intrinsic tasks" in the general plain-text corpus using the simple language modeling objective. PICL encourages the model to infer and perform tasks by conditioning on the contexts while maintaining task generalization of pre-trained models. We evaluate the in-context learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark, which contains 100+ NLP tasks formulated to text generation. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters. The code is publicly available at https://github.com/thu-coai/PICL.
Transfer Learning for Causal Effect Estimation

We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data. Limited data is very common in medical applications, where some rare medical conditions, such as sepsis, are of interest. Our proposed method, named \texttt{$\ell_1$-TCL}, incorporates $\ell_1$ regularized TL for nuisance models (e.g., propensity score model); the TL estimator of the nuisance parameters is plugged into downstream average causal/treatment effect estimators (e.g., inverse probability weighted estimator). We establish non-asymptotic recovery guarantees for the \texttt{$\ell_1$-TCL} with generalized linear model (GLM) under the sparsity assumption in the high-dimensional setting, and demonstrate the empirical benefits of \texttt{$\ell_1$-TCL} through extensive numerical simulation for GLM and recent neural network nuisance models. Our method is subsequently extended to real data and generates meaningful insights consistent with medical literature, a case where all baseline methods fail.
Trust-Worthy Semantic Communications for the Metaverse Relying on Federated Learning

As an evolving successor to the mobile Internet, the Metaverse creates the impression of an immersive environment, integrating the virtual as well as the real world. In contrast to the traditional mobile Internet based on servers, the Metaverse is constructed by billions of cooperating users by harnessing their smart edge devices having limited communication and computation resources. In this immersive environment an unprecedented amount of multi-modal data has to be processed. To circumvent this impending bottleneck, low-rate semantic communication might be harnessed in support of the Metaverse. But given that private multi-modal data is exchanged in the Metaverse, we have to guard against security breaches and privacy invasions. Hence we conceive a trust-worthy semantic communication system for the Metaverse based on a federated learning architecture by exploiting its distributed decision-making and privacy-preserving capability. We conclude by identifying a suite of promising research directions and open issues.
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works building their meta-learner based on Prototypical Networks are unsatisfactory in learning discriminative text representations between similar classes, which may lead to contradictions during label prediction. In addition, the tasklevel and instance-level overfitting problems in few-shot text classification caused by a few training examples are not sufficiently tackled. In this work, we propose a contrastive learning framework named ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. ContrastNet learns to pull closer text representations belonging to the same class and push away text representations belonging to different classes, while simultaneously introducing unsupervised contrastive regularization at both task-level and instance-level to prevent overfitting. Experiments on 8 few-shot text classification datasets show that ContrastNet outperforms the current state-of-the-art models.
A new node-shift encoding representation for the travelling salesman problem

This paper presents a new genetic algorithm encoding representation to solve the travelling salesman problem. To assess the performance of the proposed chromosome structure, we compare it with state-of-the-art encoding representations. For that purpose, we use 14 benchmarks of different sizes taken from TSPLIB. Finally, after conducting the experimental study, we report the obtained results and draw our conclusion.
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation

This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST). Recent efforts for ZST often utilize the Transformer architecture as the backbone, with LayerNorm at the input of layers (PreNorm) set as the default. However, Xu et al. (2019) has revealed that PreNorm carries the risk of overfitting the training data. Based on this, we hypothesize that PreNorm may overfit supervised directions and thus have low generalizability for ZST. Through experiments on OPUS, IWSLT, and Europarl datasets for 54 ZST directions, we demonstrate that the original Transformer setting of LayerNorm after residual connections (PostNorm) consistently outperforms PreNorm by up to 12.3 BLEU points. We then study the performance disparities by analyzing the differences in off-target rates and structural variations between PreNorm and PostNorm. This study highlights the need for careful consideration of the LayerNorm setting for ZST.