% Define an abbreviation (acronym)
% Acronyms for indonesian thesis
\newacronym{ml}{ML}{\textit{machine learning}}
\newacronym{stft}{STFT}{\textit{short-time} Fourier \textit{transform}}
\newacronym{ai}{AI}{\textit{artificial intelligence}}
\newacronym{dl}{DL}{\textit{deep learning}}
\newacronym{nn}{NN}{\textit{neural network}}
\newacronym{fft}{FFT}{\textit{fast} Fourier \textit{transform}}
\newacronym{svm}{SVM}{\textit{support vector machine}}
\newacronym{cnn}{CNN}{\textit{convolutional neural network}}
\newacronym{rnn}{RNN}{\textit{recurrent neural network}}
\newacronym{vbi}{VBI}{\textit{vibration-based inspection}}
\newacronym{shm}{SHM}{\textit{structural health monitoring}}
\newacronym{fea}{FEA}{\textit{finite element analysis}}
\newacronym{1d-cnn}{1-D CNN}{\textit{one-dimensional convolutional neural network}}
\newacronym{pca}{PCA}{\textit{principal component analysis}}
\newacronym{rbf}{RBF}{\textit{radial basis function}}
\newacronym{cm}{CM}{\textit{confusion matrix}}
\newacronym{tsne}{t-SNE}{\textit{t-Distributed Stochastic Neighbor Embedding}}
\newacronym{pacmap}{PaCMAP}{\textit{Pairwise Controlled Manifold Approximation Projection}}
\newacronym{ar}{AR}{\textit{autoregressive}}
\newacronym{re}{RE}{\textit{residual error}}
\newacronym{ae}{AE}{\textit{autoencoder}}
\newacronym{lr}{LR}{\textit{logistic regression}}
\newacronym{ann}{ANN}{\textit{artificial neural network}}
\newacronym{wt}{WT}{\textit{wavelet transform}}
\newacronym{oc-svm}{OC-SVM}{\textit{one-class support vector machine}}
\newacronym{lstm}{LSTM}{\textit{long short-term memory}}
\newacronym{ga}{GA}{\textit{genetic algorithm}}
\newacronym{ht}{HT}{Hilbert \textit{transform}}
\newacronym{vmd}{VMD}{\textit{variational mode decomposition}}
\newacronym{qugs}{QUGS}{\textit{Qatar University Grandstand Simulator}}
\newacronym{r-cnn}{R-CNN}{\textit{region-based convolutional neural network}}
\newacronym{pso}{PSO}{\textit{particle swarm optimization}}

% ADDED - Common AI/ML acronyms with proper names
\newacronym{hmm}{HMM}{\textit{hidden} Markov \textit{model}}
\newacronym{gru}{GRU}{\textit{gated recurrent unit}}
\newacronym{gan}{GAN}{\textit{generative adversarial network}}
\newacronym{vae}{VAE}{\textit{variational autoencoder}}
\newacronym{adam}{Adam}{Adam} % optimizer named after "adaptive moment estimation"
\newacronym{sgd}{SGD}{\textit{stochastic gradient descent}}
\newacronym{relu}{ReLU}{\textit{rectified linear unit}}
\newacronym{elu}{ELU}{\textit{exponential linear unit}}
\newacronym{selu}{SELU}{\textit{scaled exponential linear unit}}
\newacronym{gelu}{GELU}{Gaussian \textit{error linear unit}}
\newacronym{bert}{BERT}{\textit{Bidirectional Encoder Representations from Transformers}}
\newacronym{gpt}{GPT}{\textit{Generative Pre-trained Transformer}}
\newacronym{resnet}{ResNet}{\textit{Residual Network}}
\newacronym{yolo}{YOLO}{\textit{You Only Look Once}}
\newacronym{fcn}{FCN}{\textit{fully convolutional network}}
\newacronym{rlhf}{RLHF}{\textit{reinforcement learning from human feedback}}
\newacronym{ppo}{PPO}{\textit{proximal policy optimization}}
\newacronym{dqn}{DQN}{\textit{deep Q-network}}
\newacronym{ddpg}{DDPG}{\textit{deep deterministic policy gradient}}
\newacronym{a3c}{A3C}{\textit{asynchronous advantage actor-critic}}
\newacronym{sac}{SAC}{\textit{soft actor-critic}}
\newacronym{td}{TD}{\textit{temporal difference}}
\newacronym{mdp}{MDP}{Markov \textit{decision process}}
\newacronym{pomdp}{POMDP}{\textit{partially observable} Markov \textit{decision process}}
\newacronym{mcmc}{MCMC}{Markov \textit{chain} Monte Carlo}
\newacronym{em}{EM}{\textit{expectation-maximization}}
\newacronym{map}{MAP}{\textit{maximum a posteriori}}
\newacronym{mle}{MLE}{\textit{maximum likelihood estimation}}
\newacronym{kl}{KL}{Kullback-Leibler}
\newacronym{js}{JS}{Jensen-Shannon}
\newacronym{knn}{k-NN}{\textit{k-nearest neighbors}}
\newacronym{dbscan}{DBSCAN}{\textit{density-based spatial clustering of applications with noise}}
\newacronym{kmeans}{k-means}{\textit{k-means}}
\newacronym{gmm}{GMM}{Gaussian \textit{mixture model}}
\newacronym{gp}{GP}{Gaussian \textit{process}}
\newacronym{gpr}{GPR}{Gaussian \textit{process regression}}
\newacronym{bo}{BO}{Bayesian \textit{optimization}}
\newacronym{tpe}{TPE}{\textit{tree-structured} Parzen \textit{estimator}}
\newacronym{smbo}{SMBO}{\textit{sequential model-based optimization}}
\newacronym{nas}{NAS}{\textit{neural architecture search}}
\newacronym{automl}{AutoML}{\textit{automated machine learning}}
\newacronym{xgboost}{XGBoost}{\textit{eXtreme Gradient Boosting}}
\newacronym{gbdt}{GBDT}{\textit{gradient boosting decision tree}}
\newacronym{rf}{RF}{\textit{random forest}}
\newacronym{dt}{DT}{\textit{decision tree}}
\newacronym{cart}{CART}{\textit{classification and regression tree}}
\newacronym{id3}{ID3}{\textit{Iterative Dichotomiser 3}}
\newacronym{c45}{C4.5}{C4.5}
\newacronym{adaboost}{AdaBoost}{\textit{adaptive boosting}}
\newacronym{lasso}{LASSO}{\textit{least absolute shrinkage and selection operator}}
\newacronym{ridge}{Ridge}{Ridge \textit{regression}}
\newacronym{enet}{ElasticNet}{ElasticNet}
\newacronym{svr}{SVR}{\textit{support vector regression}}
\newacronym{kpca}{KPCA}{\textit{kernel principal component analysis}}
\newacronym{lda}{LDA}{\textit{linear discriminant analysis}}
\newacronym{ica}{ICA}{\textit{independent component analysis}}
\newacronym{nmf}{NMF}{\textit{non-negative matrix factorization}}
\newacronym{umap}{UMAP}{\textit{Uniform Manifold Approximation and Projection}}
\newacronym{isomap}{Isomap}{\textit{Isometric Mapping}}
\newacronym{lle}{LLE}{\textit{locally linear embedding}}
\newacronym{mds}{MDS}{\textit{multidimensional scaling}}
\newacronym{som}{SOM}{\textit{self-organizing map}}
\newacronym{bpnn}{BPNN}{\textit{backpropagation neural network}}
\newacronym{elm}{ELM}{\textit{extreme learning machine}}
\newacronym{sae}{SAE}{\textit{stacked autoencoder}}
\newacronym{dae}{DAE}{\textit{denoising autoencoder}}
\newacronym{vq-vae}{VQ-VAE}{\textit{vector quantized variational autoencoder}}
\newacronym{wgan}{WGAN}{Wasserstein GAN}
\newacronym{dcgan}{DCGAN}{\textit{deep convolutional generative adversarial network}}
\newacronym{cgan}{cGAN}{\textit{conditional generative adversarial network}}
\newacronym{cyclegan}{CycleGAN}{CycleGAN}
\newacronym{pix2pix}{Pix2Pix}{Pix2Pix}
\newacronym{stylegan}{StyleGAN}{StyleGAN}
\newacronym{clip}{CLIP}{\textit{Contrastive Language-Image Pre-training}}
\newacronym{vit}{ViT}{\textit{Vision Transformer}}
\newacronym{swin}{Swin}{\textit{Shifted Window Transformer}}
\newacronym{detr}{DETR}{\textit{DEtection TRansformer}}
\newacronym{rcnn}{R-CNN}{\textit{region-based convolutional neural network}}
\newacronym{fast-rcnn}{Fast R-CNN}{Fast R-CNN}
\newacronym{faster-rcnn}{Faster R-CNN}{Faster R-CNN}
\newacronym{mask-rcnn}{Mask R-CNN}{Mask R-CNN}
\newacronym{fpn}{FPN}{\textit{feature pyramid network}}
\newacronym{ssd}{SSD}{\textit{single shot detector}}
\newacronym{seresnet}{SE-ResNet}{\textit{Squeeze-and-Excitation ResNet}}
\newacronym{nlp}{NLP}{\textit{natural language processing}}
\newacronym{nlu}{NLU}{\textit{natural language understanding}}
\newacronym{nlg}{NLG}{\textit{natural language generation}}
\newacronym{seq2seq}{Seq2Seq}{\textit{sequence-to-sequence}}
\newacronym{bleu}{BLEU}{\textit{Bilingual Evaluation Understudy}}
\newacronym{rouge}{ROUGE}{\textit{Recall-Oriented Understudy for Gisting Evaluation}}
\newacronym{meteor}{METEOR}{\textit{Metric for Evaluation of Translation with Explicit ORdering}}
\newacronym{cider}{CIDEr}{\textit{Consensus-based Image Description Evaluation}}
\newacronym{wer}{WER}{\textit{word error rate}}
\newacronym{cer}{CER}{\textit{character error rate}}
\newacronym{perplexity}{PPL}{\textit{perplexity}}
\newacronym{bpe}{BPE}{\textit{byte pair encoding}}
\newacronym{wordpiece}{WordPiece}{WordPiece}
\newacronym{sentencepiece}{SentencePiece}{SentencePiece}
\newacronym{tfidf}{TF-IDF}{\textit{term frequency-inverse document frequency}}
\newacronym{bow}{BoW}{\textit{bag of words}}
\newacronym{cbow}{CBOW}{\textit{continuous bag of words}}
\newacronym{skipgram}{Skip-gram}{Skip-gram}
\newacronym{word2vec}{Word2Vec}{Word2Vec}
\newacronym{glove}{GloVe}{\textit{Global Vectors for Word Representation}}
\newacronym{fasttext}{FastText}{FastText}
\newacronym{elmo}{ELMo}{\textit{Embeddings from Language Models}}
\newacronym{ulmfit}{ULMFiT}{\textit{Universal Language Model Fine-tuning}}
\newacronym{t5}{T5}{\textit{Text-to-Text Transfer Transformer}}
\newacronym{bart}{BART}{\textit{Bidirectional and Auto-Regressive Transformers}}
\newacronym{electra}{ELECTRA}{\textit{Efficiently Learning an Encoder that Classifies Token Replacements Accurately}}
\newacronym{roberta}{RoBERTa}{\textit{Robustly Optimized BERT Pretraining Approach}}
\newacronym{ernie}{ERNIE}{\textit{Enhanced Representation through kNowledge IntEgration}}
\newacronym{flash-attention}{FlashAttention}{FlashAttention}
\newacronym{mha}{MHA}{\textit{multi-head attention}}
\newacronym{mqa}{MQA}{\textit{multi-query attention}}
\newacronym{gqa}{GQA}{\textit{grouped-query attention}}
\newacronym{kv-cache}{KV-cache}{\textit{key-value cache}}
\newacronym{rag}{RAG}{\textit{retrieval-augmented generation}}
\newacronym{dpr}{DPR}{\textit{dense passage retrieval}}
\newacronym{faiss}{FAISS}{\textit{Facebook AI Similarity Search}}
\newacronym{annoy}{Annoy}{\textit{Approximate Nearest Neighbors Oh Yeah}}
\newacronym{hnsw}{HNSW}{\textit{Hierarchical Navigable Small World}}
\newacronym{ivf}{IVF}{\textit{inverted file index}}
\newacronym{pq}{PQ}{\textit{product quantization}}
\newacronym{lsh}{LSH}{\textit{locality-sensitive hashing}}
\newacronym{mips}{MIPS}{\textit{maximum inner product search}}
\newacronym{rl}{RL}{\textit{reinforcement learning}}
\newacronym{drl}{DRL}{\textit{deep reinforcement learning}}
\newacronym{irl}{IRL}{\textit{inverse reinforcement learning}}
\newacronym{gail}{GAIL}{\textit{generative adversarial imitation learning}}
\newacronym{il}{IL}{\textit{imitation learning}}
\newacronym{bc}{BC}{\textit{behavioral cloning}}
\newacronym{marl}{MARL}{\textit{multi-agent reinforcement learning}}
\newacronym{maddpg}{MADDPG}{\textit{multi-agent deep deterministic policy gradient}}
\newacronym{vdn}{VDN}{\textit{value decomposition network}}
\newacronym{coma}{COMA}{\textit{counterfactual multi-agent policy gradient}}
\newacronym{mappo}{MAPPO}{\textit{multi-agent proximal policy optimization}}
\newacronym{trpo}{TRPO}{\textit{trust region policy optimization}}
\newacronym{td3}{TD3}{\textit{twin delayed deep deterministic policy gradient}}
\newacronym{her}{HER}{\textit{hindsight experience replay}}
\newacronym{per}{PER}{\textit{prioritized experience replay}}
\newacronym{c51}{C51}{\textit{categorical 51}}
\newacronym{qr-dqn}{QR-DQN}{\textit{quantile regression DQN}}
\newacronym{iqn}{IQN}{\textit{implicit quantile network}}
\newacronym{icm}{ICM}{\textit{intrinsic curiosity module}}
\newacronym{rnd}{RND}{\textit{random network distillation}}
\newacronym{ngu}{NGU}{\textit{Never Give Up}}
\newacronym{curl}{CURL}{\textit{Contrastive Unsupervised Representations for Reinforcement Learning}}
\newacronym{mcts}{MCTS}{Monte Carlo \textit{tree search}}
\newacronym{uct}{UCT}{\textit{upper confidence bound applied to trees}}
\newacronym{puct}{PUCT}{\textit{predictor + upper confidence bound applied to trees}}
\newacronym{ucb}{UCB}{\textit{upper confidence bound}}
\newacronym{ts}{TS}{Thompson \textit{sampling}}
\newacronym{mab}{MAB}{\textit{multi-armed bandit}}
\newacronym{cmab}{CMAB}{\textit{contextual multi-armed bandit}}
\newacronym{exp3}{EXP3}{\textit{exponential-weight algorithm for exploration and exploitation}}
\newacronym{linucb}{LinUCB}{\textit{linear upper confidence bound}}
\newacronym{scaffold}{SCAFFOLD}{\textit{Stochastic Controlled Averaging for Federated Learning}}
\newacronym{fedbn}{FedBN}{\textit{Federated learning with Batch Normalization}}
\newacronym{moon}{MOON}{\textit{Model-Contrastive Federated Learning}}
\newacronym{feddf}{FedDF}{\textit{Federated Learning via Knowledge Distillation with Dynamic Regularization}}
\newacronym{fl}{FL}{\textit{federated learning}}
\newacronym{dp}{DP}{\textit{differential privacy}}
\newacronym{ldp}{LDP}{\textit{local differential privacy}}
\newacronym{pate}{PATE}{\textit{Private Aggregation of Teacher Ensembles}}
\newacronym{smpc}{SMPC}{\textit{secure multi-party computation}}
\newacronym{he}{HE}{\textit{homomorphic encryption}}
\newacronym{phe}{PHE}{\textit{partial homomorphic encryption}}
\newacronym{fhe}{FHE}{\textit{fully homomorphic encryption}}
\newacronym{mpc}{MPC}{\textit{multi-party computation}}
\newacronym{tee}{TEE}{\textit{trusted execution environment}}
\newacronym{sgx}{SGX}{\textit{Software Guard Extensions}}
\newacronym{zkp}{ZKP}{\textit{zero-knowledge proof}}
\newacronym{snark}{SNARK}{\textit{Succinct Non-interactive ARgument of Knowledge}}
\newacronym{stark}{STARK}{\textit{Scalable Transparent ARgument of Knowledge}}
\newacronym{zksnark}{zkSNARK}{\textit{zero-knowledge Succinct Non-interactive ARgument of Knowledge}}
\newacronym{zkstark}{zkSTARK}{\textit{zero-knowledge Scalable Transparent ARgument of Knowledge}}
\newacronym{mlops}{MLOps}{\textit{Machine Learning Operations}}
\newacronym{cicd}{CI/CD}{\textit{continuous integration/continuous deployment}}
\newacronym{dag}{DAG}{\textit{directed acyclic graph}}
\newacronym{etl}{ETL}{\textit{extract, transform, load}}
\newacronym{elt}{ELT}{\textit{extract, load, transform}}
\newacronym{oltp}{OLTP}{\textit{online transaction processing}}
\newacronym{olap}{OLAP}{\textit{online analytical processing}}
\newacronym{dwh}{DWH}{\textit{data warehouse}}
\newacronym{datalake}{Data Lake}{\textit{data lake}}
\newacronym{lakehouse}{Lakehouse}{\textit{lakehouse}}
\newacronym{edw}{EDW}{\textit{enterprise data warehouse}}
\newacronym{dim}{DIM}{\textit{dimension table}}
\newacronym{fact}{FACT}{\textit{fact table}}
\newacronym{scd}{SCD}{\textit{slowly changing dimension}}
\newacronym{star-schema}{Star Schema}{\textit{star schema}}
\newacronym{snowflake-schema}{Snowflake Schema}{\textit{snowflake schema}}
\newacronym{eer}{EER}{\textit{equal error rate}}
\newacronym{far}{FAR}{\textit{false acceptance rate}}
\newacronym{frr}{FRR}{\textit{false rejection rate}}
\newacronym{fpr}{FPR}{\textit{false positive rate}}
\newacronym{tpr}{TPR}{\textit{true positive rate}}
\newacronym{fnr}{FNR}{\textit{false negative rate}}
\newacronym{tnr}{TNR}{\textit{true negative rate}}
\newacronym{ppv}{PPV}{\textit{positive predictive value}}
\newacronym{npv}{NPV}{\textit{negative predictive value}}
\newacronym{auroc}{AUROC}{\textit{area under the receiver operating characteristic curve}}
\newacronym{auprc}{AUPRC}{\textit{area under the precision-recall curve}}
\newacronym{roc}{ROC}{\textit{receiver operating characteristic}}
\newacronym{pr-curve}{PR Curve}{\textit{precision-recall curve}}
\newacronym{f1}{F1}{\textit{F1 score}}
\newacronym{f2}{F2}{\textit{F2 score}}
\newacronym{fbeta}{F-beta}{\textit{F-beta score}}
\newacronym{mcc}{MCC}{Matthews \textit{correlation coefficient}}
\newacronym{iou}{IoU}{\textit{intersection over union}}
\newacronym{jaccard}{Jaccard}{Jaccard \textit{index}}
\newacronym{hausdorff}{Hausdorff}{Hausdorff \textit{distance}}
\newacronym{mse}{MSE}{\textit{mean squared error}}
\newacronym{rmse}{RMSE}{\textit{root mean squared error}}
\newacronym{mae}{MAE}{\textit{mean absolute error}}
\newacronym{mape}{MAPE}{\textit{mean absolute percentage error}}
\newacronym{smape}{SMAPE}{\textit{symmetric mean absolute percentage error}}
\newacronym{r2}{R²}{\textit{coefficient of determination}}
\newacronym{adjusted-r2}{Adjusted R²}{\textit{adjusted coefficient of determination}}
\newacronym{aic}{AIC}{Akaike \textit{information criterion}}
\newacronym{bic}{BIC}{Bayesian \textit{information criterion}}
\newacronym{cv}{CV}{\textit{cross-validation}}
\newacronym{loocv}{LOOCV}{\textit{leave-one-out cross-validation}}
\newacronym{kfold}{k-fold}{\textit{k-fold cross-validation}}
\newacronym{smote}{SMOTE}{\textit{Synthetic Minority Over-sampling Technique}}
\newacronym{adasyn}{ADASYN}{\textit{Adaptive Synthetic Sampling}}
\newacronym{enn}{ENN}{\textit{edited nearest neighbors}}
\newacronym{ncr}{NCR}{\textit{neighborhood cleaning rule}}
\newacronym{rus}{RUS}{\textit{random under-sampling}}
\newacronym{ros}{ROS}{\textit{random over-sampling}}
\newacronym{bce}{BCE}{\textit{binary cross-entropy}}
\newacronym{cce}{CCE}{\textit{categorical cross-entropy}}
\newacronym{scce}{SCCE}{\textit{sparse categorical cross-entropy}}
\newacronym{nll}{NLL}{\textit{negative log-likelihood}}
\newacronym{huber}{Huber}{Huber \textit{loss}}
\newacronym{focal}{Focal}{\textit{focal loss}}
\newacronym{dice-loss}{Dice Loss}{Dice \textit{loss}}
\newacronym{tversky}{Tversky}{Tversky \textit{loss}}
\newacronym{lovasz}{Lovász}{Lovász \textit{loss}}
\newacronym{bn}{BN}{\textit{batch normalization}}
\newacronym{ln}{LN}{\textit{layer normalization}}
\newacronym{in}{IN}{\textit{instance normalization}}
\newacronym{gn}{GN}{\textit{group normalization}}
\newacronym{wn}{WN}{\textit{weight normalization}}
\newacronym{sn}{SN}{\textit{spectral normalization}}
\newacronym{rmsnorm}{RMSNorm}{\textit{root mean square layer normalization}}
\newacronym{adain}{AdaIN}{\textit{adaptive instance normalization}}
\newacronym{adagn}{AdaGN}{\textit{adaptive group normalization}}
\newacronym{spade}{SPADE}{\textit{Spatially-Adaptive Normalization}}
\newacronym{radam}{RAdam}{\textit{Rectified Adam}}
\newacronym{lamb}{LAMB}{\textit{Layer-wise Adaptive Moments optimizer for Batch training}}
\newacronym{lars}{LARS}{\textit{Layer-wise Adaptive Rate Scaling}}
\newacronym{lookahead}{Lookahead}{Lookahead \textit{optimizer}}
\newacronym{swats}{SWATS}{\textit{Switching from Adam to SGD}}
\newacronym{slr}{SLR}{\textit{stochastic learning rate}}
\newacronym{tta}{TTA}{\textit{test-time augmentation}}
\newacronym{ema}{EMA}{\textit{exponential moving average}}
\newacronym{swa}{SWA}{\textit{stochastic weight averaging}}
\newacronym{ptq}{PTQ}{\textit{post-training quantization}}
\newacronym{qat}{QAT}{\textit{quantization-aware training}}
\newacronym{int8}{INT8}{\textit{8-bit integer}}
\newacronym{fp16}{FP16}{\textit{16-bit floating point}}
\newacronym{bf16}{BF16}{\textit{bfloat16}}
\newacronym{fp32}{FP32}{\textit{32-bit floating point}}
\newacronym{amp}{AMP}{\textit{automatic mixed precision}}
\newacronym{ode}{ODE}{\textit{ordinary differential equation}}
\newacronym{pde}{PDE}{\textit{partial differential equation}}
\newacronym{sde}{SDE}{\textit{stochastic differential equation}}
\newacronym{node}{NODE}{\textit{neural ordinary differential equation}}
\newacronym{anode}{ANODE}{\textit{augmented neural ordinary differential equation}}
\newacronym{pinn}{PINN}{\textit{physics-informed neural network}}
\newacronym{deeponet}{DeepONet}{\textit{Deep Operator Network}}
\newacronym{gnn}{GNN}{\textit{graph neural network}}
\newacronym{gcn}{GCN}{\textit{graph convolutional network}}
\newacronym{gat}{GAT}{\textit{graph attention network}}
\newacronym{gin}{GIN}{\textit{Graph Isomorphism Network}}
\newacronym{mpnn}{MPNN}{\textit{message passing neural network}}
\newacronym{gatedgcn}{Gated GCN}{\textit{gated graph convolutional network}}
\newacronym{line}{LINE}{\textit{Large-scale Information Network Embedding}}
\newacronym{gran}{GRAN}{\textit{Graph Recurrent Attention Network}}
\newacronym{dgmg}{DGMG}{\textit{Deep Generative Model of Graphs}}
\newacronym{molgan}{MolGAN}{\textit{Molecular Generative Adversarial Network}}
\newacronym{jtvae}{JT-VAE}{\textit{Junction Tree Variational Autoencoder}}
\newacronym{rgcn}{R-GCN}{\textit{Relational Graph Convolutional Network}}
\newacronym{hgt}{HGT}{\textit{Heterogeneous Graph Transformer}}
\newacronym{han}{HAN}{\textit{Heterogeneous Graph Attention Network}}
\newacronym{rgat}{R-GAT}{\textit{Relational Graph Attention Network}}
\newacronym{compgcn}{CompGCN}{\textit{Composition-based Graph Convolutional Network}}
\newacronym{kg}{KG}{\textit{knowledge graph}}
\newacronym{kge}{KGE}{\textit{knowledge graph embedding}}
\newacronym{kgc}{KGC}{\textit{knowledge graph completion}}
\newacronym{qa}{QA}{\textit{question answering}}
\newacronym{vqa}{VQA}{\textit{visual question answering}}
\newacronym{squad}{SQuAD}{\textit{Stanford Question Answering Dataset}}
\newacronym{glue}{GLUE}{\textit{General Language Understanding Evaluation}}
\newacronym{superglue}{SuperGLUE}{\textit{Super General Language Understanding Evaluation}}
\newacronym{xnli}{XNLI}{\textit{Cross-lingual Natural Language Inference}}
\newacronym{mnli}{MNLI}{\textit{Multi-Genre Natural Language Inference}}
\newacronym{snli}{SNLI}{Stanford \textit{Natural Language Inference}}
\newacronym{qqp}{QQP}{Quora Question Pairs}
\newacronym{qnli}{QNLI}{\textit{Question Natural Language Inference}}
\newacronym{sts}{STS}{\textit{Semantic Textual Similarity}}
\newacronym{cola}{CoLA}{\textit{Corpus of Linguistic Acceptability}}
\newacronym{sst}{SST}{Stanford \textit{Sentiment Treebank}}
\newacronym{mrpc}{MRPC}{Microsoft Research Paraphrase Corpus}
\newacronym{rte}{RTE}{\textit{Recognizing Textual Entailment}}
\newacronym{wnli}{WNLI}{Winograd \textit{Natural Language Inference}}
\newacronym{wsc}{WSC}{Winograd \textit{Schema Challenge}}
\newacronym{copa}{COPA}{\textit{Choice of Plausible Alternatives}}
\newacronym{piqa}{PIQA}{\textit{Physical Interaction Question Answering}}
\newacronym{siqa}{SIQA}{\textit{Social Interaction Question Answering}}
\newacronym{commonsenseqa}{CommonsenseQA}{CommonsenseQA}
\newacronym{arc}{ARC}{\textit{AI2 Reasoning Challenge}}
\newacronym{race}{RACE}{\textit{ReAding Comprehension from Examinations}}
\newacronym{drop}{DROP}{\textit{Discrete Reasoning Over Paragraphs}}
\newacronym{hotpotqa}{HotpotQA}{HotpotQA}
\newacronym{naturalquestions}{Natural Questions}{Natural Questions}
\newacronym{triviaqa}{TriviaQA}{TriviaQA}
\newacronym{quac}{QuAC}{\textit{Question Answering in Context}}
\newacronym{coqa}{CoQA}{\textit{Conversational Question Answering}}
\newacronym{multirc}{MultiRC}{\textit{Multi-Sentence Reading Comprehension}}
\newacronym{record}{ReCoRD}{\textit{Reading Comprehension with Commonsense Reasoning Dataset}}
\newacronym{wmt}{WMT}{\textit{Workshop on Machine Translation}}
\newacronym{iwslt}{IWSLT}{\textit{International Workshop on Spoken Language Translation}}