pwc-archive/papers-with-abstracts huggingface dataset:
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Authors: Rajeswari Chengoden, Nancy Victor, Thien Huynh-The, Gokul Yenduri, Rutvij H. Jhaveri, Mamoun Alazab, Sweta Bhattacharya, Pawan Hegde, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
Date: 2022-09-09 00:00:00

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment with reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies for adopting the Metaverse for healthcare, the potential applications and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.
SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation
Authors: Taejun Lim, Siqu Long, Josiah Poon, Soyeon Caren Han
Date: 2022-09-09 00:00:00
Tasks: Collaborative Filtering, Matrix Completion, Missing Values, Position, Representation Learning, Semantic Similarity, Semantic Textual Similarity

Collaborative filtering problems are commonly solved based on matrix completion techniques which recover the missing values of user-item interaction matrices. In a matrix, the rating position specifically represents the user given and the item rated. Previous matrix completion techniques tend to neglect the position of each element (user, item and ratings) in the matrix but mainly focus on semantic similarity between users and items to predict the missing value in a matrix. This paper proposes a novel position-enhanced user/item representation training model for recommendation, SUPER-Rec. We first capture the rating position in the matrix using the relative positional rating encoding and store the position-enhanced rating information and its user-item relationship to the fixed dimension of embedding that is not affected by the matrix size. Then, we apply the trained position-enhanced user and item representations to the simplest traditional machine learning models to highlight the pure novelty of our representation learning model. We contribute the first formal introduction and quantitative analysis of position-enhanced item representation in the recommendation domain and produce a principled discussion about our SUPER-Rec to the outperformed performance of typical collaborative filtering recommendation tasks with both explicit and implicit feedback.
In-situ animal behavior classification using knowledge distillation and fixed-point quantization
Authors: Reza Arablouei, Liang Wang, Caitlin Phillips, Lachlan Currie, Jordan Yates, Greg Bishop-hurley
Date: 2022-09-09 00:00:00
Tasks: Classification, Knowledge Distillation, Quantization, TAG, Time Series Analysis, Time Series Classification

We explore the use of knowledge distillation (KD) for learning compact and accurate models that enable classification of animal behavior from accelerometry data on wearable devices. To this end, we take a deep and complex convolutional neural network, known as residual neural network (ResNet), as the teacher model. ResNet is specifically designed for multivariate time-series classification. We use ResNet to distill the knowledge of animal behavior classification datasets into soft labels, which consist of the predicted pseudo-probabilities of every class for each datapoint. We then use the soft labels to train our significantly less complex student models, which are based on the gated recurrent unit (GRU) and multilayer perceptron (MLP). The evaluation results using two real-world animal behavior classification datasets show that the classification accuracy of the student GRU-MLP models improves appreciably through KD, approaching that of the teacher ResNet model. To further reduce the computational and memory requirements of performing inference using the student models trained via KD, we utilize dynamic fixed-point quantization (DQ) through an appropriate modification of the computational graph of the considered models. We implement both unquantized and quantized versions of the developed KD-based models on the embedded systems of our purpose-built collar and ear tag devices to classify animal behavior in situ and in real time. Our evaluations corroborate the effectiveness of KD and DQ in improving the accuracy and efficiency of in-situ animal behavior classification.
Fast Neural Kernel Embeddings for General Activations
Authors: Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi
Date: 2022-09-09 00:00:00

Infinite width limit has shed light on generalization and optimization aspects of deep learning by establishing connections between neural networks and kernel methods. Despite their importance, the utility of these kernel methods was limited in large-scale learning settings due to their (super-)quadratic runtime and memory complexities. Moreover, most prior works on neural kernels have focused on the ReLU activation, mainly due to its popularity but also due to the difficulty of computing such kernels for general activations. In this work, we overcome such difficulties by providing methods to work with general activations. First, we compile and expand the list of activation functions admitting exact dual activation expressions to compute neural kernels. When the exact computation is unknown, we present methods to effectively approximate them. We propose a fast sketching method that approximates any multi-layered Neural Network Gaussian Process (NNGP) kernel and Neural Tangent Kernel (NTK) matrices for a wide range of activation functions, going beyond the commonly analyzed ReLU activation. This is done by showing how to approximate the neural kernels using the truncated Hermite expansion of any desired activation functions. While most prior works require data points on the unit sphere, our methods do not suffer from such limitations and are applicable to any dataset of points in $\mathbb{R}^d$. Furthermore, we provide a subspace embedding for NNGP and NTK matrices with near input-sparsity runtime and near-optimal target dimension which applies to any \emph{homogeneous} dual activation functions with rapidly convergent Taylor expansion. Empirically, with respect to exact convolutional NTK (CNTK) computation, our method achieves $106\times$ speedup for approximate CNTK of a 5-layer Myrtle network on CIFAR-10 dataset.
Robust and Lossless Fingerprinting of Deep Neural Networks via Pooled Membership Inference
Authors: Hanzhou Wu
Date: 2022-09-09 00:00:00

Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society. However, it also raises new security problems, among which how to protect the intellectual property (IP) of DNNs against infringement is one of the most important yet very challenging topics. To deal with this problem, recent studies focus on the IP protection of DNNs by applying digital watermarking, which embeds source information and/or authentication data into DNN models by tuning network parameters directly or indirectly. However, tuning network parameters inevitably distorts the DNN and therefore surely impairs the performance of the DNN model on its original task regardless of the degree of the performance degradation. It has motivated the authors in this paper to propose a novel technique called pooled membership inference (PMI) so as to protect the IP of the DNN models. The proposed PMI neither alters the network parameters of the given DNN model nor fine-tunes the DNN model with a sequence of carefully crafted trigger samples. Instead, it leaves the original DNN model unchanged, but can determine the ownership of the DNN model by inferring which mini-dataset among multiple mini-datasets was once used to train the target DNN model, which differs from previous arts and has remarkable potential in practice. Experiments also have demonstrated the superiority and applicability of this work.
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
Authors: Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji
Date: 2022-09-09 00:00:00
Proceeding: COLING 2022 10
Tasks: Emotion-Cause Pair Extraction, Multi-Task Learning

Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE results. However, existing MTL-based methods either fail to simultaneously model the specific features and the interactive feature in between, or suffer from the inconsistency of label prediction. In this work, we consider addressing the above challenges for improving ECPE by performing two alignment mechanisms with a novel A^2Net model. We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature. Besides, an inter-task alignment is implemented, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency. Evaluations of benchmarks show that our methods outperform current best-performing systems on all ECA subtasks. Further analysis proves the importance of our proposed alignment mechanisms for the task.
MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification
Authors: Xiaokai Liu, Menghua Zhang
Date: 2022-09-09 00:00:00
Tasks: Genre classification, Information Retrieval, Music Genre Classification, Music Information Retrieval, Retrieval

Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field for identifying the long-tail, data-poor genre based on the related music audio segments, which is very prevalent in real-world scenarios. Most of the existing models are designed for class-balanced music datasets, resulting in poor performance in accuracy and generalization when identifying the music genres at the tail of the distribution. Inspired by the success of introducing Multi-instance Learning (MIL) in various classification tasks, we propose a novel mechanism named Multi-instance Attention (MATT) to boost the performance for identifying tail classes. Specifically, we first construct the bag-level datasets by generating the album-artist pair bags. Second, we leverage neural networks to encode the music audio segments. Finally, under the guidance of a multi-instance attention mechanism, the neural network-based models could select the most informative genre to match the given music segment. Comprehensive experimental results on a large-scale music genre benchmark dataset with long-tail distribution demonstrate MATT significantly outperforms other state-of-the-art baselines.
Task-Agnostic Learning to Accomplish New Tasks
Authors: Xianqi Zhang, Xingtao Wang, Xu Liu, Wenrui Wang, Xiaopeng Fan, Debin Zhao
Date: 2022-09-09 00:00:00
Tasks: Imitation Learning, Offline RL, Reinforcement Learning (RL)

Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic control in recent years. However, these methods show obvious deterioration for new tasks that need to be completed through new combinations of actions. RL methods heavily rely on reward functions that cannot generalize well for new tasks, while IL methods are limited by expert demonstrations which do not cover new tasks. In contrast, humans can easily complete these tasks with the fragmented knowledge learned from task-agnostic experience. Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge from task-agnostic data to accomplish new tasks. TAL consists of four stages. First, the task-agnostic exploration is performed to collect data from interactions with the environment. The collected data is organized via a knowledge graph. Compared with the previous sequential structure, the knowledge graph representation is more compact and fits better for environment exploration. Second, an action feature extractor is proposed and trained using the collected knowledge graph data for task-agnostic fragmented knowledge learning. Third, a candidate action generator is designed, which applies the action feature extractor on a new task to generate multiple candidate action sets. Finally, an action proposal is designed to produce the probabilities for actions in a new task according to the environmental information. The probabilities are then used to select actions to be executed from multiple candidate action sets to form the plan. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art offline RL method: CQL by 35.28% and the IL method: BC by 22.22%.
Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classification
Authors: Finley Walden, Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam
Date: 2022-09-09 00:00:00
Tasks: Audio Classification, Autonomous Vehicles, Classification

Sense of hearing is crucial for autonomous vehicles (AVs) to better perceive its surrounding environment. Although visual sensors of an AV, such as camera, lidar, and radar, help to see its surrounding environment, an AV cannot see beyond those sensors line of sight. On the other hand, an AV s sense of hearing cannot be obstructed by line of sight. For example, an AV can identify an emergency vehicle s siren through audio classification even though the emergency vehicle is not within the line of sight of the AV. Thus, auditory perception is complementary to the camera, lidar, and radar-based perception systems. This paper presents a deep learning-based robust audio classification framework aiming to achieve improved environmental perception for AVs. The presented framework leverages a deep Convolution Neural Network (CNN) to classify different audio classes. UrbanSound8k, an urban environment dataset, is used to train and test the developed framework. Seven audio classes i.e., air conditioner, car horn, children playing, dog bark, engine idling, gunshot, and siren, are identified from the UrbanSound8k dataset because of their relevancy related to AVs. Our framework can classify different audio classes with 97.82% accuracy. Moreover, the audio classification accuracies with all ten classes are presented, which proves that our framework performed better in the case of AV-related sounds compared to the existing audio classification frameworks.
Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles
Authors: Sagar Dasgupta, Kazi Shakib, Mizanur Rahman, Silvana V Croope, Steven Jones
Date: 2022-09-09 00:00:00
Tasks: Audio Classification, Autonomous Vehicles

Human trafficking is a universal problem, persistent despite numerous efforts to combat it globally. Individuals of any age, race, ethnicity, sex, gender identity, sexual orientation, nationality, immigration status, cultural background, religion, socioeconomic class, and education can be a victim of human trafficking. With the advancements in technology and the introduction of autonomous vehicles (AVs), human traffickers will adopt new ways to transport victims, which could accelerate the growth of organized human trafficking networks, which can make the detection of trafficking in persons more challenging for law enforcement agencies. The objective of this study is to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles. The primary contributions of this study are to: (i) define four non-trivial, feasible, and realistic human trafficking scenarios for AVs; (ii) create a new and comprehensive audio dataset related to human trafficking with five classes i.e., crying, screaming, car door banging, car noise, and conversation; and (iii) develop a deep 1-D Convolution Neural Network (CNN) architecture for audio data classification related to human trafficking. We have also conducted a case study using the new audio dataset and evaluated the audio classification performance of the deep 1-D CNN. Our analyses reveal that the deep 1-D CNN can distinguish sound coming from a human trafficking victim from a non-human trafficking sound with an accuracy of 95%, which proves the efficacy of our framework.
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
Authors: Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh, Reazul Russel
Date: 2022-09-09 00:00:00
Tasks: Reinforcement Learning (RL), Safe Reinforcement Learning

Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to randomness in dynamics (aleatory) and to model uncertainty (epistemic) in isolation. We propose and analyze a new framework to jointly model the risk associated with epistemic and aleatory uncertainties in finite-horizon and discounted infinite-horizon MDPs. We call this framework that combines Risk-Averse and Soft-Robust methods RASR. We show that when the risk-aversion is defined using either EVaR or the entropic risk, the optimal policy in RASR can be computed efficiently using a new dynamic program formulation with a time-dependent risk level. As a result, the optimal risk-averse policies are deterministic but time-dependent, even in the infinite-horizon discounted setting. We also show that particular RASR objectives reduce to risk-averse RL with mean posterior transition probabilities. Our empirical results show that our new algorithms consistently mitigate uncertainty as measured by EVaR and other standard risk measures.
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics
Authors: Masataro Asai
Date: 2022-09-08 00:00:00

The symbolic AI community is increasingly trying to embrace machine learning in neuro-symbolic architectures, yet is still struggling due to cultural barriers. To break the barrier, this rather opinionated personal memo attempts to explain and rectify the conventions in Statistics, Machine Learning, and Deep Learning from the viewpoint of outsiders. It provides a step-by-step protocol for designing a machine learning system that satisfies a minimum theoretical guarantee necessary for being taken seriously by the symbolic AI community, i.e., it discusses "in what condition we can stop worrying and accept statistical machine learning." Unlike most textbooks which are written for students trying to specialize in Stat/ML/DL and willing to accept jargons, this memo is written for experienced symbolic researchers that hear a lot of buzz but are still uncertain and skeptical. Information on Stat/ML/DL is currently too scattered or too noisy to invest in. This memo prioritizes compactness, citations to old papers (many in early 20th century), and concepts that resonate well with symbolic paradigms in order to offer time savings. It prioritizes general mathematical modeling and does not discuss any specific function approximator, such as neural networks (NNs), SVMs, decision trees, etc. Finally, it is open to corrections. Consider this memo as something similar to a blog post taking the form of a paper on Arxiv.
Who Pays? Personalization, Bossiness and the Cost of Fairness
Authors: Paresha Farastu, Nicholas Mattei, Robin Burke
Date: 2022-09-08 00:00:00
Tasks: Fairness, Recommendation Systems

Fairness-aware recommender systems that have a provider-side fairness concern seek to ensure that protected group(s) of providers have a fair opportunity to promote their items or products. There is a ``cost of fairness'' borne by the consumer side of the interaction when such a solution is implemented. This consumer-side cost raises its own questions of fairness, particularly when personalization is used to control the impact of the fairness constraint. In adopting a personalized approach to the fairness objective, researchers may be opening their systems up to strategic behavior on the part of users. This type of incentive has been studied in the computational social choice literature under the terminology of ``bossiness''. The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others. This position paper introduces the concept of bossiness, shows its application in fairness-aware recommendation and discusses strategies for reducing this strategic incentive.
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
Authors: Chulin Xie, Yunhui Long, Pin-Yu Chen, Qinbin Li, Arash Nourian, Sanmi Koyejo, Bo Li
Date: 2022-09-08 00:00:00
Tasks: Federated Learning

Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users. As local training data comes from different users who may not be trustworthy, several studies have shown that FL is vulnerable to poisoning attacks. Meanwhile, to protect the privacy of local users, FL is usually trained in a differentially private way (DPFL). Thus, in this paper, we ask: What are the underlying connections between differential privacy and certified robustness in FL against poisoning attacks? Can we leverage the innate privacy property of DPFL to provide certified robustness for FL? Can we further improve the privacy of FL to improve such robustness certification? We first investigate both user-level and instance-level privacy of FL and provide formal privacy analysis to achieve improved instance-level privacy. We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels. Theoretically, we provide the certified robustness of DPFL based on both criteria given a bounded number of adversarial users or instances. Empirically, we conduct extensive experiments to verify our theories under a range of poisoning attacks on different datasets. We find that increasing the level of privacy protection in DPFL results in stronger certified attack inefficacy; however, it does not necessarily lead to a stronger certified prediction. Thus, achieving the optimal certified prediction requires a proper balance between privacy and utility loss.