pwc-archive/papers-with-abstracts huggingface dataset:
GPoeT-2: A GPT-2 Based Poem Generator
Authors: Kai-Ling Lo, Rami Ariss, Philipp Kurz
Date: 2022-05-18 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling

This project aims to produce the next volume of machine-generated poetry, a complex art form that can be structured and unstructured, and carries depth in the meaning between the lines. GPoeT-2 is based on fine-tuning a state of the art natural language model (i.e. GPT-2) to generate limericks, typically humorous structured poems consisting of five lines with a AABBA rhyming scheme. With a two-stage generation system utilizing both forward and reverse language modeling, GPoeT-2 is capable of freely generating limericks in diverse topics while following the rhyming structure without any seed phrase or a posteriori constraints.Based on the automated generation process, we explore a wide variety of evaluation metrics to quantify "good poetry," including syntactical correctness, lexical diversity, and subject continuity. Finally, we present a collection of 94 categorized limericks that rank highly on the explored "good poetry" metrics to provoke human creativity.
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization
Authors: Christoph Linse, Thomas Martinetz
Date: 2022-05-18 00:00:00
Tasks: Fine-Grained Image Classification, Image Augmentation, image-classification, Image Classification

Recent findings have shown that highly over-parameterized Neural Networks generalize without pretraining or explicit regularization. It is achieved with zero training error, i.e., complete over-fitting by memorizing the training data. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that a randomly initialized VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.
Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer
Authors: Weishan Cai, Wenjun Ma, Jieyu Zhan, Yuncheng Jiang
Date: 2022-05-18 00:00:00
Tasks: Entity Alignment, Knowledge Graphs, Relation

Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs, which is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 11 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems
Authors: Wangyang Xu, Jiancheng An, Chongwen Huang, Lu Gan, Chau Yuen
Date: 2022-05-18 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design in an RIS-aided mmWave multiple-input multiple-output system. Specifically, we design a neural network to imitate the transmission environment based on the geometric relationship between the user's location and the mmWave channel. Following this, a novel DRL-based method is developed that interacts with the imitation environment using the easily available location information. Finally, simulation results demonstrate that the proposed DRL-based algorithm provides more robust performance without excessive interaction overhead compared to the existing DRL-based approaches.
Entity Alignment For Knowledge Graphs: Progress, Challenges, and Empirical Studies
Authors: Deepak Chaurasiya, Anil Surisetty, Nitish Kumar, Alok Singh, Vikrant Dey, Aakarsh Malhotra, Gaurav Dhama, Ankur Arora
Date: 2022-05-18 00:00:00
Tasks: Benchmarking, Entity Alignment, Knowledge Graphs

Entity Alignment (EA) identifies entities across databases that refer to the same entity. Knowledge graph-based embedding methods have recently dominated EA techniques. Such methods map entities to a low-dimension space and align them based on their similarities. With the corpus of EA methodologies growing rapidly, this paper presents a comprehensive analysis of various existing EA methods, elaborating their applications and limitations. Further, we distinguish the methods based on their underlying algorithms and the information they incorporate to learn entity representations. Based on challenges in industrial datasets, we bring forward $4$ research questions (RQs). These RQs empirically analyse the algorithms from the perspective of \textit{Hubness, Degree distribution, Non-isomorphic neighbourhood,} and \textit{Name bias}. For Hubness, where one entity turns up as the nearest neighbour of many other entities, we define an $h$-score to quantify its effect on the performance of various algorithms. Additionally, we try to level the playing field for algorithms that rely primarily on name-bias existing in the benchmarking open-source datasets by creating a low name bias dataset. We further create an open-source repository for $14$ embedding-based EA methods and present the analysis for invoking further research motivations in the field of EA.
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
Authors: Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen
Date: 2022-05-18 00:00:00
Tasks: Classification, Graph Attention, Graph Embedding, POS, Sentiment Analysis, Sentiment Classification

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.
A reproducible experimental survey on biomedical sentence similarity: a string-based method sets the state of the art
Authors: Alicia Lara-Clares, Juan J. Lastra-Díaz, Ana Garcia-Serrano
Date: 2022-05-18 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, Sentence Similarity

This registered report introduces the largest, and for the first time, reproducible experimental survey on biomedical sentence similarity with the following aims: (1) to elucidate the state of the art of the problem; (2) to solve some reproducibility problems preventing the evaluation of most of current methods; (3) to evaluate several unexplored sentence similarity methods; (4) to evaluate an unexplored benchmark, called Corpus-Transcriptional-Regulation; (5) to carry out a study on the impact of the pre-processing stages and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (6) to bridge the lack of reproducibility resources for methods and experiments in this line of research. Our experimental survey is based on a single software platform that is provided with a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments. In addition, we introduce a new aggregated string-based sentence similarity method, called LiBlock, together with eight variants of current ontology-based methods and a new pre-trained word embedding model trained on the full-text articles in the PMC-BioC corpus. Our experiments show that our novel string-based measure sets the new state of the art on the sentence similarity task in the biomedical domain and significantly outperforms all the methods evaluated herein, except one ontology-based method. Likewise, our experiments confirm that the pre-processing stages, and the choice of the NER tool, have a significant impact on the performance of the sentence similarity methods. We also detail some drawbacks and limitations of current methods, and warn on the need of refining the current benchmarks. Finally, a noticeable finding is that our new string-based method significantly outperforms all state-of-the-art Machine Learning models evaluated herein.
$(O,G)$-granular variable precision fuzzy rough sets based on overlap and grouping functions
Authors: Wei Li, Bin Yang, Junsheng Qiao
Date: 2022-05-18 00:00:00

Since Bustince et al. introduced the concepts of overlap and grouping functions, these two types of aggregation functions have attracted a lot of interest in both theory and applications. In this paper, the depiction of $(O,G)$-granular variable precision fuzzy rough sets ($(O,G)$-GVPFRSs for short) is first given based on overlap and grouping functions. Meanwhile, to work out the approximation operators efficiently, we give another expression of upper and lower approximation operators by means of fuzzy implications and co-implications. Furthermore, starting from the perspective of construction methods, $(O,G)$-GVPFRSs are represented under diverse fuzzy relations. Finally, some conclusions on the granular variable precision fuzzy rough sets (GVPFRSs for short) are extended to $(O,G)$-GVPFRSs under some additional conditions.
It Isn't Sh!tposting, It's My CAT Posting
Authors: Parthsarthi Rawat, Sayan Das, Jorge Aguirre, Akhil Daphara
Date: 2022-05-18 00:00:00
Tasks: Image Captioning

In this paper, we describe a novel architecture which can generate hilarious captions for a given input image. The architecture is split into two halves, i.e. image captioning and hilarious text conversion. The architecture starts with a pre-trained CNN model, VGG16 in this implementation, and applies attention LSTM on it to generate normal caption. These normal captions then are fed forward to our hilarious text conversion transformer which converts this text into something hilarious while maintaining the context of the input image. The architecture can also be split into two halves and only the seq2seq transformer can be used to generate hilarious caption by inputting a sentence.This paper aims to help everyday user to be more lazy and hilarious at the same time by generating captions using CATNet.
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning
Authors: Yufan Zhang, Honglin Wen, Qiuwei Wu, Qian Ai
Date: 2022-05-18 00:00:00
Tasks: Load Forecasting, Prediction Intervals, quantile regression, Reinforcement Learning (RL)

Prediction intervals offer an effective tool for quantifying the uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to unforeseen changes in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles. It relies on the online learning ability of reinforcement learning to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve the quality of PIs. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.
The Solvability of Interpretability Evaluation Metrics
Authors: Yilun Zhou, Julie Shah
Date: 2022-05-18 00:00:00

Feature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency. In this paper, we highlight an intriguing property of these metrics: their solvability. Concretely, we can define the problem of optimizing an explanation for a metric, which can be solved by beam search. This observation leads to the obvious yet unaddressed question: why do we use explainers (e.g., LIME) not based on solving the target metric, if the metric value represents explanation quality? We present a series of investigations showing strong performance of this beam search explainer and discuss its broader implication: a definition-evaluation duality of interpretability concepts. We implement the explainer and release the Python solvex package for models of text, image and tabular domains.
Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting
Authors: Aoyu Liu, Yaying Zhang
Date: 2022-05-18 00:00:00
Tasks: Traffic Prediction

Accurate traffic forecasting is essential for smart cities to achieve traffic control, route planning, and flow detection. Although many spatial-temporal methods are currently proposed, these methods are deficient in capturing the spatial-temporal dependence of traffic data synchronously. In addition, most of the methods ignore the dynamically changing correlations between road network nodes that arise as traffic data changes. We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) to address the above challenges for traffic forecasting. Specifically, we propose an interactive dynamic graph convolution structure, which divides the sequences at intervals and synchronously captures the traffic data's spatial-temporal dependence through an interactive learning strategy. The interactive learning strategy makes STIDGCN effective for long-term prediction. We also propose a novel dynamic graph convolution module to capture the dynamically changing correlations in the traffic network, consisting of a graph generator and fusion graph convolution. The dynamic graph convolution module can use the input traffic data and pre-defined graph structure to generate a graph structure. It is then fused with the defined adaptive adjacency matrix to generate a dynamic adjacency matrix, which fills the pre-defined graph structure and simulates the generation of dynamic associations between nodes in the road network. Extensive experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
Authors: Jinwei Xing, Takashi Nagata, Xinyun Zou, Emre Neftci, Jeffrey L. Krichmar
Date: 2022-05-18 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Reinforcement Learning (RL)

Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural networks. However, in the RL domain, existing saliency map approaches are either computationally expensive and thus cannot satisfy the real-time requirement of real-world scenarios or cannot produce interpretable saliency maps for RL policies. In this work, we propose an approach of Distillation with selective Input Gradient Regularization (DIGR) which uses policy distillation and input gradient regularization to produce new policies that achieve both high interpretability and computation efficiency in generating saliency maps. Our approach is also found to improve the robustness of RL policies to multiple adversarial attacks. We conduct experiments on three tasks, MiniGrid (Fetch Object), Atari (Breakout) and CARLA Autonomous Driving, to demonstrate the importance and effectiveness of our approach.
Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
Authors: Florian Richoux
Date: 2022-05-18 00:00:00
Tasks: Combinatorial Optimization, Real-Time Strategy Games, Starcraft

Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation. On StarCraft games, all previous works on terrain analysis propose a crisp analysis based on connected component detection, Voronoi diagram computation and pruning, and region merging. Those methods have been implemented as game-specific libraries, and they can only offer the same kind of analysis for all maps and all users. In this paper, we propose a way to consider terrain analysis as a combinatorial optimization problem. Our method allows different kinds of analysis by changing constraints or the objective function in the problem model. We also present a library, Taunt, implementing our method and able to handle both StarCraft 1 and StarCraft 2 maps. This makes our library a universal tool for StarCraft bots with different spatial representation needs. We believe our library unlocks the possibility to have real adaptive AIs playing StarCraft, and can be the starting point of a new wave of bots.
A Pulse-and-Glide-driven Adaptive Cruise Control System for Electric Vehicle
Authors: Zhaofeng Tian, Liangkai Liu, Weisong Shi
Date: 2022-05-18 00:00:00

As the adaptive cruise control system (ACCS) on vehicles is well-developed today, vehicle manufacturers have increasingly employed this technology in new-generation intelligent vehicles. Pulse-and-glide (PnG) strategy is an efficacious driving strategy to diminish fuel consumption in traditional oil-fueled vehicles. However, current studies rarely focus on the verification of the energy-saving effect of PnG on an electric vehicle (EV) and embedding PnG in ACCS. This paper proposes a pulse-and-glide-driven adaptive cruise control system (PGACCS) model which leverages PnG strategy as a parallel function with cruise control (CC) and verifies that PnG is an efficacious energy-saving strategy on EV by optimizing the energy cost of the PnG operation using Intelligent Genetic Algorithm and Particle Swarm Optimization (IGPSO). This paper builds up a simulation model of an EV with regenerative braking and ACCS based on which the performance of PGACCS and regenerative braking is evaluated; the PnG energy performance is optimized and the effect of regenerative braking on PnG energy performance is evaluated. As a result of PnG optimization, the PnG operation in the PGACCS could cut down 28.3% energy cost of the EV compared to the CC operation in the traditional ACCS which verifies that PnG is an effective energy-saving strategy for EV and PGACCS is a promising option for EV.
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
Authors: Kevin Yang, Olivia Deng, Charles Chen, Richard Shin, Subhro Roy, Benjamin Van Durme
Date: 2022-05-18 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Data Augmentation, Semantic Parsing

We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al., 2020), we observe 33% relative improvement over a non-data-augmented baseline in top-1 match.