pwc-archive/papers-with-abstracts huggingface dataset:
Multi-scale multi-modal micro-expression recognition algorithm based on transformer
Authors: Fengping Wang, Jie Li, Chun Qi, Lin Wang, Pan Wang
Date: 2023-01-08 00:00:00
Tasks: Contrastive Learning, Micro Expression Recognition, Micro-Expression Recognition, Model Optimization

A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide. Although manual methods have made good progress and deep learning is gaining prominence. Due to the short duration of micro-expression and different scales of expressed in facial regions, existing algorithms cannot extract multi-modal multi-scale facial region features while taking into account contextual information to learn underlying features. Therefore, in order to solve the above problems, a multi-modal multi-scale algorithm based on transformer network is proposed in this paper, aiming to fully learn local multi-grained features of micro-expressions through two modal features of micro-expressions - motion features and texture features. To obtain local area features of the face at different scales, we learned patch features at different scales for both modalities, and then fused multi-layer multi-headed attention weights to obtain effective features by weighting the patch features, and combined cross-modal contrastive learning for model optimization. We conducted comprehensive experiments on three spontaneous datasets, and the results show the accuracy of the proposed algorithm in single measurement SMIC database is up to 78.73% and the F1 value on CASMEII of the combined database is up to 0.9071, which is at the leading level.
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior
Authors: Phillip J. K. Christoffersen, Andrew C. Li, Rodrigo Toro Icarte, Sheila A. McIlraith
Date: 2023-01-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed. If the reward-worthy behavior is known, it can be specified in terms of a non-Markovian reward function - a function that depends on aspects of the state-action history, rather than just the current state and action. Such reward functions yield sparse rewards, necessitating an inordinate number of experiences to find a policy that captures the reward-worthy pattern of behavior. Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR. Providing such a decomposition has been shown to vastly improve learning rates, especially when coupled with algorithms that exploit automaton structure. Nevertheless, such techniques rely on a priori knowledge of the KR. In this work, we explore how to automatically discover useful state abstractions that support learning automata over the state-action history. The result is an end-to-end algorithm that can learn optimal policies with significantly fewer environment samples than state-of-the-art RL on simple non-Markovian domains.
GA-Aided Directivity in Volumetric and Planar Massive-Antenna Array Design
Authors: Bruno Felipe Costa, Taufik Abrão
Date: 2023-01-07 00:00:00

The problem of directivity enhancement, leading to the increase in the directivity gain over a certain desired angle of arrival/departure (AoA/AoD), is considered in this work. A new formulation of the volumetric array directivity problem is proposed using the rectangular coordinates to describe each antenna element and the desired azimuth and elevation angles with a general element pattern. Such a directivity problem is formulated to find the optimal minimum distance between the antenna elements $d_\text{min}$ aiming to achieve as high directivity gains as possible. {An expedited implementation method is developed to place the antenna elements in a distinctive plane dependent on ($\theta_0$; $\phi_0$). A novel concept on optimizing directivity for the uniform planar array (OUPA) is introduced to find a quasi-optimal solution for the non-convex optimization problem with low complexity. This solution is reached by deploying the proposed successive evaluation and validation (SEV) method. {Moreover, the genetic} algorithm (GA) method was deployed to find the directivity optimization solution expeditiously. For a small number of antenna elements {, typically $N\in [4,\dots, 9]$,} the achievable directivity by GA optimization demonstrates gains of $\sim 3$ dBi compared with the traditional beamforming technique, using steering vector for uniform linear arrays (ULA) and uniform circular arrays (UCA), while gains of $\sim1.5$ dBi are attained when compared with an improved UCA directivity method. For a larger number of antenna elements {, two improved GA procedures, namely GA-{\it marginal} and GA-{\it stall}, were} proposed and compared with the OUPA method. OUPA also indicates promising directivity gains surpassing $30$ dBi for massive MIMO scenarios.
Markov Chain Concentration with an Application in Reinforcement Learning
Authors: Debangshu Banerjee
Date: 2023-01-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Given $X_1,\cdot ,X_N$ random variables whose joint distribution is given as $\mu$ we will use the Martingale Method to show any Lipshitz Function $f$ over these random variables is subgaussian. The Variance parameter however can have a simple expression under certain conditions. For example under the assumption that the random variables follow a Markov Chain and that the function is Lipschitz under a Weighted Hamming Metric. We shall conclude with certain well known techniques from concentration of suprema of random processes with applications in Reinforcement Learning
Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings
Authors: Yeskendir Koishekenov
Date: 2023-01-07 00:00:00
Tasks: Graph Attention

Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as the number of layers increases. This effect has known as over-smoothing, which means that the representations of the graph nodes of different classes would become indistinguishable when stacking multiple layers. In this work, we propose a new simple, and efficient method to alleviate the effect of the over-smoothing problem in GNNs by explicitly using relations between node embeddings. Experiments on real-world datasets demonstrate that utilizing node embedding relations makes GNN models such as Graph Attention Network more robust to over-smoothing and achieves better performance with deeper GNNs. Our method can be used in combination with other methods to give the best performance. GNN applications are endless and depend on the user's objective and the type of data that they possess. Solving over-smoothing issues can potentially improve the performance of models on all these tasks.
Image Data Augmentation Approaches: A Comprehensive Survey and Future directions
Authors: Teerath Kumar, Alessandra Mileo, Rob Brennan, Malika Bendechache
Date: 2023-01-07 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation, Survey

Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation techniques. Finally, we discuss the challenges and difficulties, and possible future direction for the research community. We believe, this survey provides several benefits i) readers will understand the data augmentation working mechanism to fix overfitting problems ii) results will save the searching time of the researcher for comparison purposes. iii) Codes of the mentioned data augmentation techniques are available at https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work will spark interest in research community.
App Review Driven Collaborative Bug Finding
Authors: Xunzhu Tang, Haoye Tian, Pingfan Kong, Kui Liu, Jacques Klein, Tegawendé F. Bissyande
Date: 2023-01-07 00:00:00

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys's implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams, respectively.
A Brain-inspired Memory Transformation based Differentiable Neural Computer for Reasoning-based Question Answering
Authors: Yao Liang, Hongjian Fang, Yi Zeng, Feifei Zhao
Date: 2023-01-07 00:00:00
Tasks: Question Answering

Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence. Although the Differentiable Neural Computer (DNC) model could solve such problems to a certain extent, the development is still limited by its high algorithm complexity, slow convergence speed, and poor test robustness. Inspired by the learning and memory mechanism of the brain, this paper proposed a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. MT-DNC incorporates working memory and long-term memory into DNC, and realizes the autonomous transformation of acquired experience between working memory and long-term memory, thereby helping to effectively extract acquired knowledge to improve reasoning ability. Experimental results on bAbI question answering task demonstrated that our proposed method achieves superior performance and faster convergence speed compared to other existing DNN and DNC models. Ablation studies also indicated that the memory transformation from working memory to long-term memory plays essential role in improving the robustness and stability of reasoning. This work explores how brain-inspired memory transformation can be integrated and applied to complex intelligent dialogue and reasoning systems.
RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm
Authors: Hamid Gharagozlou, Javad Mohammadzadeh, Azam Bastanfard, Saeed Shiry Ghidary
Date: 2023-01-07 00:00:00
Tasks: Answer Selection, Decision Making, imbalanced classification, Open-Domain Question Answering, Question Answering, Sequential Decision Making

Answer selection (AS) is a critical subtask of the open-domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedding, enriched by an improved artificial bee colony (ABC) algorithm for pretraining and a reinforcement learning-based algorithm for training backpropagation (BP) algorithm. BERT can be comprised in downstream work and fine-tuned as a united task-specific architecture, and the pretrained BERT model can grab different linguistic effects. Existing algorithms typically train the AS model with positive-negative pairs for a two-class classifier. A positive pair contains a question and a genuine answer, while a negative one includes a question and a fake answer. The output should be one for positive and zero for negative pairs. Typically, negative pairs are more than positive, leading to an imbalanced classification that drastically reduces system performance. To deal with it, we define classification as a sequential decision-making process in which the agent takes a sample at each step and classifies it. For each classification operation, the agent receives a reward, in which the prize of the majority class is less than the reward of the minority class. Ultimately, the agent finds the optimal value for the policy weights. We initialize the policy weights with the improved ABC algorithm. The initial value technique can prevent problems such as getting stuck in the local optimum. Although ABC serves well in most tasks, there is still a weakness in the ABC algorithm that disregards the fitness of related pairs of individuals in discovering a neighboring food source position.
A Lite Fireworks Algorithm for Optimization
Authors: Haimiao Mo, Min Zeng
Date: 2023-01-07 00:00:00
Tasks: Scheduling

The fireworks algorithm is an optimization algorithm for simulating the explosion phenomenon of fireworks. Because of its fast convergence and high precision, it is widely used in pattern recognition, optimal scheduling, and other fields. However, most of the existing research work on the fireworks algorithm is improved based on its defects, and little consideration is given to reducing the number of parameters of the fireworks algorithm. The original fireworks algorithm has too many parameters, which increases the cost of algorithm adjustment and is not conducive to engineering applications. In addition, in the fireworks population, the unselected individuals are discarded, thus causing a waste of their location information. To reduce the number of parameters of the original Fireworks Algorithm and make full use of the location information of discarded individuals, we propose a simplified version of the Fireworks Algorithm. It reduces the number of algorithm parameters by redesigning the explosion operator of the fireworks algorithm and constructs an adaptive explosion radius by using the historical optimal information to balance the local mining and global exploration capabilities. The comparative experimental results of function optimization show that the overall performance of our proposed LFWA is better than that of comparative algorithms, such as the fireworks algorithm, particle swarm algorithm, and bat algorithm.
Linguistic-style-aware Neural Networks for Fake News Detection
Authors: Xinyi Zhou, Jiayu Li, Qinzhou Li, Reza Zafarani
Date: 2023-01-07 00:00:00
Tasks: Fake News Detection

We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. The code and data have been publicly available.
Investigations on convergence behaviour of Physics Informed Neural Networks across spectral ranges and derivative orders
Authors: Mayank Deshpande, Siddharth Agarwal, Vukka Snigdha, Arya Kumar Bhattacharya
Date: 2023-01-07 00:00:00
Tasks: Open-Ended Question Answering

An important inference from Neural Tangent Kernel (NTK) theory is the existence of spectral bias (SB), that is, low frequency components of the target function of a fully connected Artificial Neural Network (ANN) being learnt significantly faster than the higher frequencies during training. This is established for Mean Square Error (MSE) loss functions with very low learning rate parameters. Physics Informed Neural Networks (PINNs) are designed to learn the solutions of differential equations (DE) of arbitrary orders; in PINNs the loss functions are obtained as the residues of the conservative form of the DEs and represent the degree of dissatisfaction of the equations. So there has been an open question whether (a) PINNs also exhibit SB and (b) if so, how does this bias vary across the orders of the DEs. In this work, a series of numerical experiments are conducted on simple sinusoidal functions of varying frequencies, compositions and equation orders to investigate these issues. It is firmly established that under normalized conditions, PINNs do exhibit strong spectral bias, and this increases with the order of the differential equation.
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
Authors: Yinyu Lan, Shizhu He, Kang Liu, Jun Zhao
Date: 2023-01-07 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graph Embeddings, Knowledge Graphs, Question Answering

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7\% and 4.3\% in mean reciprocal rank (MRR).
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Authors: Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li
Date: 2023-01-07 00:00:00
Tasks: Graph Sampling

The success of graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant of the prediction?'' Particularly, parametric explainers prevail in existing approaches because of their more robust capability to decipher the black-box (i.e., target GNNs). In this paper, based on the observation that graphs typically share some common motif patterns, we propose a novel non-parametric subgraph matching framework, dubbed MatchExplainer, to explore explanatory subgraphs. It couples the target graph with other counterpart instances and identifies the most crucial joint substructure by minimizing the node corresponding-based distance. Moreover, we note that present graph sampling or node-dropping methods usually suffer from the false positive sampling problem. To alleviate this issue, we designed a new augmentation paradigm named MatchDrop. It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part. Extensive experiments on synthetic and real-world datasets show the effectiveness of our MatchExplainer by outperforming all state-of-the-art parametric baselines with significant margins. Results also demonstrate that MatchDrop is a general scheme to be equipped with GNNs for enhanced performance. The code is available at: https://github.com/smiles724/MatchExplainer.
Visual Story Generation Based on Emotion and Keywords
Authors: Yuetian Chen, Ruohua Li, Bowen Shi, Peiru Liu, Mei Si
Date: 2023-01-07 00:00:00
Tasks: Image Generation, Object Recognition, Sentence, Story Generation

Automated visual story generation aims to produce stories with corresponding illustrations that exhibit coherence, progression, and adherence to characters' emotional development. This work proposes a story generation pipeline to co-create visual stories with the users. The pipeline allows the user to control events and emotions on the generated content. The pipeline includes two parts: narrative and image generation. For narrative generation, the system generates the next sentence using user-specified keywords and emotion labels. For image generation, diffusion models are used to create a visually appealing image corresponding to each generated sentence. Further, object recognition is applied to the generated images to allow objects in these images to be mentioned in future story development.
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem
Authors: Yanjie Song
Date: 2023-01-07 00:00:00
Tasks: Combinatorial Optimization, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

For complex combinatorial optimization problems, models and algorithms are at the heart of the solution. The complexity of many types of satellite mission planning problems is NP-hard and places high demands on the solution. In this paper, two types of satellite scheduling problem models are introduced and a reinforcement learning based evolutionary algorithm framework based is proposed.