OpenRaiser/Intern-Atlas huggingface dataset:
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks
Venue: arXiv cs.AI 2023
Year: 2023

Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource constraints. This planning problem has previously been modeled as restless multi-armed bandits (RMAB), where each arm is an intervention-dependent Markov Decision Process. However, the existing literature assumes all intervention resources belong to a single uniform pool, limiting their applicability to real-world settings where interventions are carried out by a set of workers, each with their own costs, budgets, and intervention effects. In this work, we consider a novel RMAB setting, called multi-worker restless bandits (MWRMAB) with heterogeneous workers. The goal is to plan an intervention schedule that maximizes the expected reward while satisfying budget constraints on each worker as well as fairness in terms of the load assigned to each worker. Our contributions are two-fold: (1) we provide a multi-worker extension of the Whittle index to tackle heterogeneous costs and per-worker budget and (2) we develop an index-based scheduling policy to achieve fairness. Further, we evaluate our method on various cost structures and show that our method significantly outperforms other baselines in terms of fairness without sacrificing much in reward accumulated.
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition
Venue: arXiv cs.CL 2023
Year: 2023

The awareness for biased ASR datasets or models has increased notably in recent years. Even for English, despite a vast amount of available training data, systems perform worse for non-native speakers. In this work, we improve an accent-conversion model (ACM) which transforms native US-English speech into accented pronunciation. We include phonetic knowledge in the ACM training to provide accurate feedback about how well certain pronunciation patterns were recovered in the synthesized waveform. Furthermore, we investigate the feasibility of learned accent representations instead of static embeddings. Generated data was then used to train two state-of-the-art ASR systems. We evaluated our approach on native and non-native English datasets and found that synthetically accented data helped the ASR to better understand speech from seen accents. This observation did not translate to unseen accents, and it was not observed for a model that had been pre-trained exclusively with native speech.
Distortion Minimization with Age of Information and Cost Constraints
Venue: arXiv cs.IT 2023
Year: 2023

We consider a source monitoring a stochastic process with a transmitter to transmit timely information through a wireless ON/OFF channel to a destination. We assume that once the source samples the data, the sampled data has to be processed to identify the state of the stochastic process. The processing can take place either at the source before transmission or after transmission at the destination. The objective is to minimize the distortion while keeping the age of information (AoI) that measures the timeliness of information under a certain threshold. We use a stationary randomized policy (SRP) framework to solve the formulated problem. We show that the two-dimensional discrete-time Markov chain considering the AoI and instantaneous distortion as the state is lumpable and we obtain the expression for the expected AoI under the SRP.
Transmission-Guided Bayesian Generative Model for Smoke Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Smoke segmentation is essential to precisely localize wildfire so that it can be extinguished in an early phase. Although deep neural networks have achieved promising results on image segmentation tasks, they are prone to be overconfident for smoke segmentation due to its non-rigid shape and transparent appearance. This is caused by both knowledge level uncertainty due to limited training data for accurate smoke segmentation and labeling level uncertainty representing the difficulty in labeling ground-truth. To effectively model the two types of uncertainty, we introduce a Bayesian generative model to simultaneously estimate the posterior distribution of model parameters and its predictions. Further, smoke images suffer from low contrast and ambiguity, inspired by physics-based image dehazing methods, we design a transmission-guided local coherence loss to guide the network to learn pair-wise relationships based on pixel distance and the transmission feature. To promote the development of this field, we also contribute a high-quality smoke segmentation dataset, SMOKE5K, consisting of 1,400 real and 4,000 synthetic images with pixel-wise annotation. Experimental results on benchmark testing datasets illustrate that our model achieves both accurate predictions and reliable uncertainty maps representing model ignorance about its prediction. Our code and dataset are publicly available at: https://github.com/redlessme/Transmission-BVM.
The Impact of Data Persistence Bias on Social Media Studies
Venue: arXiv cs.SI 2023
Year: 2023

Social media studies often collect data retrospectively to analyze public opinion. Social media data may decay over time and such decay may prevent the collection of the complete dataset. As a result, the collected dataset may differ from the complete dataset and the study may suffer from data persistence bias. Past research suggests that the datasets collected retrospectively are largely representative of the original dataset in terms of textual content. However, no study analyzed the impact of data persistence bias on social media studies such as those focusing on controversial topics. In this study, we analyze the data persistence and the bias it introduces on the datasets of three types: controversial topics, trending topics, and framing of issues. We report which topics are more likely to suffer from data persistence among these datasets. We quantify the data persistence bias using the change in political orientation, the presence of potentially harmful content and topics as measures. We found that controversial datasets are more likely to suffer from data persistence and they lean towards the political left upon recollection. The turnout of the data that contain potentially harmful content is significantly lower on non-controversial datasets. Overall, we found that the topics promoted by right-aligned users are more likely to suffer from data persistence. Account suspensions are the primary factor contributing to data removals, if not the only one. Our results emphasize the importance of accounting for the data persistence bias by collecting the data in real time when the dataset employed is vulnerable to data persistence bias.
HasChor: Functional Choreographic Programming for All (Functional Pearl)
Venue: arXiv cs.PL 2023
Year: 2023

Choreographic programming is an emerging paradigm for programming distributed systems. In choreographic programming, the programmer describes the behavior of the entire system as a single, unified program -- a choreography -- which is then compiled to individual programs that run on each node, via a compilation step called endpoint projection. We present a new model for functional choreographic programming where choreographies are expressed as computations in a monad. Our model supports cutting-edge choreographic programming features that enable modularity and code reuse: in particular, it supports higher-order choreographies, in which a choreography may be passed as an argument to another choreography, and location-polymorphic choreographies, in which a choreography can abstract over nodes. Our model is implemented in a Haskell library, HasChor, which lets programmers write choreographic programs while using the rich Haskell ecosystem at no cost, bringing choreographic programming within reach of everyday Haskellers. Moreover, thanks to Haskell's abstractions, the implementation of the HasChor library itself is concise and understandable, boiling down endpoint projection to its short and simple essence.
Communication Trade-offs in Federated Learning of Spiking Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in the training of SNNs in a distributed scheme are not well understood. Here, we consider SNNs in a federated learning setting where a high-quality global model is created by aggregating multiple local models from the clients without sharing any data. We investigate federated learning for training multiple SNNs at clients when two mechanisms reduce the uplink communication cost: i) random masking of the model updates sent from the clients to the server; and ii) client dropouts where some clients do not send their updates to the server. We evaluated the performance of the SNNs using a subset of the Spiking Heidelberg digits (SHD) dataset. The results show that a trade-off between the random masking and the client drop probabilities is crucial to obtain a satisfactory performance for a fixed number of clients.
Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Venue: arXiv cs.CV 2023
Year: 2023

We present an attention-based spatial graph convolution (AGC) for graph neural networks (GNNs). Existing AGCs focus on only using node-wise features and utilizing one type of attention function when calculating attention weights. Instead, we propose two methods to improve the representational power of AGCs by utilizing 1) structural information in a high-dimensional space and 2) multiple attention functions when calculating their weights. The first method computes a local structure representation of a graph in a high-dimensional space. The second method utilizes multiple attention functions simultaneously in one AGC. Both approaches can be combined. We also propose a GNN for the classification of point clouds and that for the prediction of point labels in a point cloud based on the proposed AGC. According to experiments, the proposed GNNs perform better than existing methods. Our codes open at https://github.com/liyang-tuat/SFAGC.
Image Labels Are All You Need for Coarse Seagrass Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Seagrass meadows serve as critical carbon sinks, but estimating the amount of carbon they store requires knowledge of the seagrass species present. Underwater and surface vehicles equipped with machine learning algorithms can help to accurately estimate the composition and extent of seagrass meadows at scale. However, previous approaches for seagrass detection and classification have required supervision from patch-level labels. In this paper, we reframe seagrass classification as a weakly supervised coarse segmentation problem where image-level labels are used during training (25 times fewer labels compared to patch-level labeling) and patch-level outputs are obtained at inference time. To this end, we introduce SeaFeats, an architecture that uses unsupervised contrastive pre-training and feature similarity, and SeaCLIP, a model that showcases the effectiveness of large language models as a supervisory signal in domain-specific applications. We demonstrate that an ensemble of SeaFeats and SeaCLIP leads to highly robust performance. Our method outperforms previous approaches that require patch-level labels on the multi-species 'DeepSeagrass' dataset by 6.8% (absolute) for the class-weighted F1 score, and by 12.1% (absolute) for the seagrass presence/absence F1 score on the 'Global Wetlands' dataset. We also present two case studies for real-world deployment: outlier detection on the Global Wetlands dataset, and application of our method on imagery collected by the FloatyBoat autonomous surface vehicle.
Heterogeneous Graph Contrastive Learning for Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity
Venue: arXiv cs.LG 2023
Year: 2023

The dynamic and evolutionary nature of service requirements in wireless networks has motivated the telecom industry to consider intelligent self-adapting Reinforcement Learning (RL) agents for controlling the growing portfolio of network services. Infusion of many new types of services is anticipated with future adoption of 6G networks, and sometimes these services will be defined by applications that are external to the network. An RL agent trained for managing the needs of a specific service type may not be ideal for managing a different service type without domain adaptation. We provide a simple heuristic for evaluating a measure of proximity between a new service and existing services, and show that the RL agent of the most proximal service rapidly adapts to the new service type through a well defined process of domain adaptation. Our approach enables a trained source policy to adapt to new situations with changed dynamics without retraining a new policy, thereby achieving significant computing and cost-effectiveness. Such domain adaptation techniques may soon provide a foundation for more generalized RL-based service management under the face of rapidly evolving service types.
On the Consistency of Circuit Lower Bounds for Non-Deterministic Time
Venue: arXiv cs.CC 2023
Year: 2023

We prove the first unconditional consistency result for superpolynomial circuit lower bounds with a relatively strong theory of bounded arithmetic. Namely, we show that the theory V$^0_2$ is consistent with the conjecture that NEXP $\not\subseteq$ P/poly, i.e., some problem that is solvable in non-deterministic exponential time does not have polynomial size circuits. We suggest this is the best currently available evidence for the truth of the conjecture. The same techniques establish the same results with NEXP replaced by the class of problems that are decidable in non-deterministic barely superpolynomial time such as NTIME$(n^{O(\log\log\log n)})$. Additionally, we establish a magnification result on the hardness of proving circuit lower bounds.
SFC Deployment in Space-Air-Ground Integrated Networks Based on Matching Game
Venue: arXiv cs.NI 2023
Year: 2023

The space-air-ground integrated network (SAGIN) is dynamic and flexible, which can support transmitting data in environments lacking ground communication facilities. However, the nodes of SAGIN are heterogeneous and it is intractable to share the resources to provide multiple services. Therefore, in this paper, we consider using network function virtualization technology to handle the problem of agile resource allocation. In particular, the service function chains (SFCs) are constructed to deploy multiple virtual network functions of different tasks. To depict the dynamic model of SAGIN, we propose the reconfigurable time extension graph. Then, an optimization problem is formulated to maximize the number of completed tasks, i.e., the successful deployed SFC. It is a mixed integer linear programming problem, which is hard to solve in limited time complexity. Hence, we transform it as a many-to-one two-sided matching game problem. Then, we design a Gale-Shapley based algorithm. Finally, via abundant simulations, it is verified that the designed algorithm can effectively deploy SFCs with efficient resource utilization.
An Evaluation System for DeFi Lending Protocols
Venue: arXiv cs.SI 2023
Year: 2023

With the development of decentralized finance (DeFi), lending protocols have been increasingly proposed in the market. A comprehensive and in-depth evaluation of lending protocol is essential to the DeFi market participants. Due to the short development time of DeFi, the current evaluation is limited to the single evaluation indicator, such as total locked value (TVL). To our knowledge, we are the first to study the evaluation model for DeFi lending protocol. In this paper, we build a four-layer lending protocol evaluation model based on Analytic Hierarchy Process (AHP). The model contains three first-level indicators and ten second-level indicators, covering various aspects of the performance of lending protocol. We calculated these indicators by obtaining on-chain and off-chain data of lending protocol. Then we evaluated and ranked six mainstream lending protocols utilizing the proposed model during the period 2021/8/20-2022/11/10. Through comparative analysis of evaluation result, we found that the violent decline in the price of ETH increase the market share of stablecoin in the lending protocols. In addition, we also revealed the reasons for various fluctuations.
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data. However, common challenges faced by many GNNs in the literature include the problem of graph node embedding under various geometries and the over-smoothing problem. To address these issues, we propose a novel graph information propagation strategy called Hamiltonian Dynamic GNN (HDG) that uses a Hamiltonian mechanics approach to learn node embeddings in a graph. The Hamiltonian energy function in HDG is learnable and can adapt to the underlying geometry of any given graph dataset. We demonstrate the ability of HDG to automatically learn the underlying geometry of graph datasets, even those with complex and mixed geometries, through comprehensive evaluations against state-of-the-art baselines on various downstream tasks. We also verify that HDG is stable against small perturbations and can mitigate the over-smoothing problem when stacking many layers.
Performance Comparison of Binary Machine Learning Classifiers in Identifying Code Comment Types: An Exploratory Study
Venue: arXiv cs.SE 2023
Year: 2023

Code comments are vital to source code as they help developers with program comprehension tasks. Written in natural language (usually English), code comments convey a variety of different information, which are grouped into specific categories. In this study, we construct 19 binary machine learning classifiers for code comment categories that belong to three different programming languages. We present a comparison of performance scores for different types of machine learning classifiers and show that the Linear SVC classifier has the highest average F1 score of 0.5474.
Task-Specific Context Decoupling for Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Classification and localization are two main sub-tasks in object detection. Nonetheless, these two tasks have inconsistent preferences for feature context, i.e., localization expects more boundary-aware features to accurately regress the bounding box, while more semantic context is preferred for object classification. Exsiting methods usually leverage disentangled heads to learn different feature context for each task. However, the heads are still applied on the same input features, which leads to an imperfect balance between classifcation and localization. In this work, we propose a novel Task-Specific COntext DEcoupling (TSCODE) head which further disentangles the feature encoding for two tasks. For classification, we generate spatially-coarse but semantically-strong feature encoding. For localization, we provide high-resolution feature map containing more edge information to better regress object boundaries. TSCODE is plug-and-play and can be easily incorperated into existing detection pipelines. Extensive experiments demonstrate that our method stably improves different detectors by over 1.0 AP with less computational cost. Our code and models will be publicly released.
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning
Venue: arXiv cs.RO 2023
Year: 2023

High altitude balloons have proved useful for ecological aerial surveys, atmospheric monitoring, and communication relays. However, due to weight and power constraints, there is a need to investigate alternate modes of propulsion to navigate in the stratosphere. Very recently, reinforcement learning has been proposed as a control scheme to maintain the balloon in the region of a fixed location, facilitated through diverse opposing wind-fields at different altitudes. Although air-pump based station keeping has been explored, there is no research on the control problem for venting and ballasting actuated balloons, which is commonly used as a low-cost alternative. We show how reinforcement learning can be used for this type of balloon. Specifically, we use the soft actor-critic algorithm, which on average is able to station-keep within 50\;km for 25\% of the flight, consistent with state-of-the-art. Furthermore, we show that the proposed controller effectively minimises the consumption of resources, thereby supporting long duration flights. We frame the controller as a continuous control reinforcement learning problem, which allows for a more diverse range of trajectories, as opposed to current state-of-the-art work, which uses discrete action spaces. Furthermore, through continuous control, we can make use of larger ascent rates which are not possible using air-pumps. The desired ascent-rate is decoupled into desired altitude and time-factor to provide a more transparent policy, compared to low-level control commands used in previous works. Finally, by applying the equations of motion, we establish appropriate thresholds for venting and ballasting to prevent the agent from exploiting the environment. More specifically, we ensure actions are physically feasible by enforcing constraints on venting and ballasting.