OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Maximize Gains From Trade in Small Markets
Venue: arXiv cs.GT 2024
Year: 2024

We study the problem of designing a two-sided market (double auction) to maximize the gains from trade (social welfare) under the constraints of (dominant-strategy) incentive compatibility and budget-balance. Our goal is to do so for an unknown distribution from which we are given a polynomial number of samples. Our first result is a general impossibility for the case of correlated distributions of values even between just one seller and two buyers, in contrast to the case of one seller and one buyer (bilateral trade) where this is possible. Our second result is an efficient learning algorithm for one seller and two buyers in the case of independent distributions which is based on a novel algorithm for computing optimal mechanisms for finitely supported and explicitly given independent distributions. Both results rely heavily on characterizations of (dominant-strategy) incentive compatible mechanisms that are strongly budget-balanced.
The State of Computer Vision Research in Africa
Venue: arXiv cs.CV 2024
Year: 2024

Despite significant efforts to democratize artificial intelligence (AI), computer vision which is a sub-field of AI, still lags in Africa. A significant factor to this, is the limited access to computing resources, datasets, and collaborations. As a result, Africa's contribution to top-tier publications in this field has only been 0.06% over the past decade. Towards improving the computer vision field and making it more accessible and inclusive, this study analyzes 63,000 Scopus-indexed computer vision publications from Africa. We utilize large language models to automatically parse their abstracts, to identify and categorize topics and datasets. This resulted in listing more than 100 African datasets. Our objective is to provide a comprehensive taxonomy of dataset categories to facilitate better understanding and utilization of these resources. We also analyze collaboration trends of researchers within and outside the continent. Additionally, we conduct a large-scale questionnaire among African computer vision researchers to identify the structural barriers they believe require urgent attention. In conclusion, our study offers a comprehensive overview of the current state of computer vision research in Africa, to empower marginalized communities to participate in the design and development of computer vision systems.
Next Visit Diagnosis Prediction via Medical Code-Centric Multimodal Contrastive EHR Modelling with Hierarchical Regularisation
Venue: arXiv cs.LG 2024
Year: 2024

Predicting next visit diagnosis using Electronic Health Records (EHR) is an essential task in healthcare, critical for devising proactive future plans for both healthcare providers and patients. Nonetheless, many preceding studies have not sufficiently addressed the heterogeneous and hierarchical characteristics inherent in EHR data, inevitably leading to sub-optimal performance. To this end, we propose NECHO, a novel medical code-centric multimodal contrastive EHR learning framework with hierarchical regularisation. First, we integrate multifaceted information encompassing medical codes, demographics, and clinical notes using a tailored network design and a pair of bimodal contrastive losses, all of which pivot around a medical codes representation. We also regularise modality-specific encoders using a parental level information in medical ontology to learn hierarchical structure of EHR data. A series of experiments on MIMIC-III data demonstrates effectiveness of our approach.
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show potential in medical data but suffer from issues like overfitting and lack of interpretability due to imbalanced and scarce data. Traditional Gray Wolf Optimization (GWO) also has its drawbacks, such as a lack of population diversity and premature convergence. This paper addresses these problems by introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid function. This algorithm was extensively compared to four leading algorithms using six well-known test functions, outperforming them effectively. Moreover, by utilizing the ACGWO, we increase the robustness and generalization of the neural network, resulting in more interpretable predictions. Applied to the publicly accessible Cleveland Heart Disease dataset, our technique surpasses ten other methods, achieving 86.8% accuracy, indicating its potential for efficient heart disease prediction in the clinical setting.
Domain-Aware Cross-Attention for Cross-domain Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Cross-domain recommendation (CDR) is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing cross-domain recommendations fail to fully utilize the target domain's special features and are hard to be generalized to new domains. The designed network is complex and is not suitable for rapid industrial deployment. Our method introduces a two-step domain-aware cross-attention, extracting transferable features of the source domain from different granularity, which allows the efficient expression of both domain and user interests. In addition, we simplify the training process, and our model can be easily deployed on new domains. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method. We have also deployed the model in an online advertising system and observed significant improvements in both Click-Through-Rate (CTR) and effective cost per mille (ECPM).
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search
Venue: arXiv cs.NE 2024
Year: 2024

Evolutionary neural architecture search (ENAS) employs evolutionary algorithms to find high-performing neural architectures automatically, and has achieved great success. However, compared to the empirical success, its rigorous theoretical analysis has yet to be touched. This work goes preliminary steps toward the mathematical runtime analysis of ENAS. In particular, we define a binary classification problem $\textsc{UNIFORM}$, and formulate an explicit fitness function to represent the relationship between neural architecture and classification accuracy. Furthermore, we consider (1+1)-ENAS algorithm with mutation to optimize the neural architecture, and obtain the following runtime bounds: both the local and global mutations find the optimum in an expected runtime of $Θ(n)$, where $n$ is the problem size. The theoretical results show that the local and global mutations achieve nearly the same performance on $\textsc{UNIFORM}$. Empirical results also verify the equivalence of these two mutation operators.
Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey
Venue: arXiv cs.CV 2024
Year: 2024

Colorectal polyp segmentation (CPS), an essential problem in medical image analysis, has garnered growing research attention. Recently, the deep learning-based model completely overwhelmed traditional methods in the field of CPS, and more and more deep CPS methods have emerged, bringing the CPS into the deep learning era. To help the researchers quickly grasp the main techniques, datasets, evaluation metrics, challenges, and trending of deep CPS, this paper presents a systematic and comprehensive review of deep-learning-based CPS methods from 2014 to 2023, a total of 115 technical papers. In particular, we first provide a comprehensive review of the current deep CPS with a novel taxonomy, including network architectures, level of supervision, and learning paradigm. More specifically, network architectures include eight subcategories, the level of supervision comprises six subcategories, and the learning paradigm encompasses 12 subcategories, totaling 26 subcategories. Then, we provided a comprehensive analysis the characteristics of each dataset, including the number of datasets, annotation types, image resolution, polyp size, contrast values, and polyp location. Following that, we summarized CPS's commonly used evaluation metrics and conducted a detailed analysis of 40 deep SOTA models, including out-of-distribution generalization and attribute-based performance analysis. Finally, we discussed deep learning-based CPS methods' main challenges and opportunities.
zkLogin: Privacy-Preserving Blockchain Authentication with Existing Credentials
Venue: arXiv cs.CR 2024
Year: 2024

For many users, a private key based wallet serves as the primary entry point to blockchains. Commonly recommended wallet authentication methods, such as mnemonics or hardware wallets, can be cumbersome. This difficulty in user onboarding has significantly hindered the adoption of blockchain-based applications. We develop zkLogin, a novel technique that leverages identity tokens issued by popular platforms (any OpenID Connect enabled platform e.g., Google, Facebook, etc.) to authenticate transactions. At the heart of zkLogin lies a signature scheme allowing the signer to sign using their existing OpenID accounts and nothing else. This improves the user experience significantly as users do not need to remember a new secret and can reuse their existing accounts. zkLogin provides strong security and privacy guarantees. Unlike prior works, zkLogin's security relies solely on the underlying platform's authentication mechanism without the need for any additional trusted parties (e.g., trusted hardware or oracles). As the name suggests, zkLogin leverages zero-knowledge proofs (ZKP) to ensure that the sensitive link between a user's off-chain and on-chain identities is hidden, even from the platform itself. zkLogin enables a number of important applications outside blockchains. It allows billions of users to produce \textit{verifiable digital content leveraging their existing digital identities}, e.g., email address. For example, a journalist can use zkLogin to sign a news article with their email address, allowing verification of the article's authorship by any party. We have implemented and deployed zkLogin on the Sui blockchain as an additional alternative to traditional digital signature-based addresses.
From Knowledge Organization to Knowledge Representation and Back
Venue: arXiv cs.AI 2024
Year: 2024

Knowledge Organization (KO) and Knowledge Representation (KR) have been the two mainstream methodologies of knowledge modelling in the Information Science community and the Artificial Intelligence community, respectively. The facet-analytical tradition of KO has developed an exhaustive set of guiding canons for ensuring quality in organising and managing knowledge but has remained limited in terms of technology-driven activities to expand its scope and services beyond the bibliographic universe of knowledge. KR, on the other hand, boasts of a robust ecosystem of technologies and technology-driven service design which can be tailored to model any entity or scale to any service in the entire universe of knowledge. This paper elucidates both the facet-analytical KO and KR methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KR-enriched KO methodology with all the standard components of a KO methodology plus the advanced technologies provided by the KR approach. The practical benefits of the methodological integration has been exemplified through the flagship application of the Digital University at the University of Trento, Italy.
FedGTA: Topology-aware Averaging for Federated Graph Learning
Venue: arXiv cs.LG 2024
Year: 2024

Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs across multiple local systems. Existing FGL studies fall into two categories: (i) FGL Optimization, which improves multi-client training in existing machine learning models; (ii) FGL Model, which enhances performance with complex local models and multi-client interactions. However, most FGL optimization strategies are designed specifically for the computer vision domain and ignore graph structure, presenting dissatisfied performance and slow convergence. Meanwhile, complex local model architectures in FGL Models studies lack scalability for handling large-scale subgraphs and have deployment limitations. To address these issues, we propose Federated Graph Topology-aware Aggregation (FedGTA), a personalized optimization strategy that optimizes through topology-aware local smoothing confidence and mixed neighbor features. During experiments, we deploy FedGTA in 12 multi-scale real-world datasets with the Louvain and Metis split. This allows us to evaluate the performance and robustness of FedGTA across a range of scenarios. Extensive experiments demonstrate that FedGTA achieves state-of-the-art performance while exhibiting high scalability and efficiency. The experiment includes ogbn-papers100M, the most representative large-scale graph database so that we can verify the applicability of our method to large-scale graph learning. To the best of our knowledge, our study is the first to bridge large-scale graph learning with FGL using this optimization strategy, contributing to the development of efficient and scalable FGL methods.
Data-oriented Coordinated Uplink Transmission for Massive IoT System
Venue: arXiv cs.IT 2024
Year: 2024

Recently, the paradigm of massive ultra-reliable low-latency IoT communications (URLLC-IoT) has gained growing interest. Reliable delay-critical uplink transmission in IoT is a challenging task since low-complex devices typically do not support multiple antennas or demanding signal processing tasks. However, in many IoT services the data volumes are small and deployments may include massive number of devices. We consider on a clustered uplink transmission with two cooperation approaches: First, we focus on scenario where location-based channel knowledge map (CKM) is applied to enable cooperation. Second, we consider a scenario where scarce channel side-information is applied in transmission. In both scenarios we also model and analyse the impact of erroneous information. In the performance evaluation we apply the recently introduced data-oriented approach that has gathered significant attention in the context of short-packet transmissions. Specifically, it introduces a transient performance metric for small data transmissions, where the amount of data and available bandwidth play crucial roles. Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range. It is noticed that the performance is heavily depending on the strength of the static channel component in the CKM based cooperation. The channel side-information based cooperation is robust against changes in the radio environment but sensitive to possible errors in the channel side-information. Even with large IoT device clusters, side-information errors may set a limit for the use of services assuming high-reliability and low-latency. Analytic results are verified against simulations, showing only minor differences at low probability levels.
Identity-Driven Multimedia Forgery Detection via Reference Assistance
Venue: arXiv cs.MM 2024
Year: 2024

Recent advancements in "deepfake" techniques have paved the way for generating various media forgeries. In response to the potential hazards of these media forgeries, many researchers engage in exploring detection methods, increasing the demand for high-quality media forgery datasets. Despite this, existing datasets have certain limitations. Firstly, most datasets focus on manipulating visual modality and usually lack diversity, as only a few forgery approaches are considered. Secondly, the quality of media is often inadequate in clarity and naturalness. Meanwhile, the size of the dataset is also limited. Thirdly, it is commonly observed that real-world forgeries are motivated by identity, yet the identity information of the individuals portrayed in these forgeries within existing datasets remains under-explored. For detection, identity information could be an essential clue to boost performance. Moreover, official media concerning relevant identities on the Internet can serve as prior knowledge, aiding both the audience and forgery detectors in determining the true identity. Therefore, we propose an identity-driven multimedia forgery dataset, IDForge, which contains 249,138 video shots sourced from 324 wild videos of 54 celebrities collected from the Internet. The fake video shots involve 9 types of manipulation across visual, audio, and textual modalities. Additionally, IDForge provides extra 214,438 real video shots as a reference set for the 54 celebrities. Correspondingly, we propose the Reference-assisted Multimodal Forgery Detection Network (R-MFDN), aiming at the detection of deepfake videos. Through extensive experiments on the proposed dataset, we demonstrate the effectiveness of R-MFDN on the multimedia detection task.
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
Venue: arXiv cs.LG 2024
Year: 2024

Property prediction is a fundamental task in crystal material research. To model atoms and structures, structures represented as graphs are widely used and graph learning-based methods have achieved significant progress. Bond angles and bond distances are two key structural information that greatly influence crystal properties. However, most of the existing works only consider bond distances and overlook bond angles. The main challenge lies in the time cost of handling bond angles, which leads to a significant increase in inference time. To solve this issue, we first propose a crystal structure modeling based on dual scale neighbor partitioning mechanism, which uses a larger scale cutoff for edge neighbors and a smaller scale cutoff for angle neighbors. Then, we propose a novel Atom-Distance-Angle Graph Neural Network (ADA-GNN) for property prediction tasks, which can process node information and structural information separately. The accuracy of predictions and inference time are improved with the dual scale modeling and the specially designed architecture of ADA-GNN. The experimental results validate that our approach achieves state-of-the-art results in two large-scale material benchmark datasets on property prediction tasks.
Collaborative Position Reasoning Network for Referring Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Given an image and a natural language expression as input, the goal of referring image segmentation is to segment the foreground masks of the entities referred by the expression. Existing methods mainly focus on interactive learning between vision and language to enhance the multi-modal representations for global context reasoning. However, predicting directly in pixel-level space can lead to collapsed positioning and poor segmentation results. Its main challenge lies in how to explicitly model entity localization, especially for non-salient entities. In this paper, we tackle this problem by executing a Collaborative Position Reasoning Network (CPRN) via the proposed novel Row-and-Column interactive (RoCo) and Guided Holistic interactive (Holi) modules. Specifically, RoCo aggregates the visual features into the row- and column-wise features corresponding two directional axes respectively. It offers a fine-grained matching behavior that perceives the associations between the linguistic features and two decoupled visual features to perform position reasoning over a hierarchical space. Holi integrates features of the two modalities by a cross-modal attention mechanism, which suppresses the irrelevant redundancy under the guide of positioning information from RoCo. Thus, with the incorporation of RoCo and Holi modules, CPRN captures the visual details of position reasoning so that the model can achieve more accurate segmentation. To our knowledge, this is the first work that explicitly focuses on position reasoning modeling. We also validate the proposed method on three evaluation datasets. It consistently outperforms existing state-of-the-art methods.
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in Video
Venue: arXiv cs.CV 2024
Year: 2024

We developed REVEX, a removal-based video explanations framework. This work extends fine-grained explanation frameworks for computer vision data and adapts six existing techniques to video by adding temporal information and local explanations. The adapted methods were evaluated across networks, datasets, image classes, and evaluation metrics. By decomposing explanation into steps, strengths and weaknesses were revealed in the studied methods, for example, on pixel clustering and perturbations in the input. Video LIME outperformed other methods with deletion values up to 31\% lower and insertion up to 30\% higher, depending on method and network. Video RISE achieved superior performance in the average drop metric, with values 10\% lower. In contrast, localization-based metrics revealed low performance across all methods, with significant variation depending on network. Pointing game accuracy reached 53\%, and IoU-based metrics remained below 20\%. Drawing on the findings across XAI methods, we further examine the limitations of the employed XAI evaluation metrics and highlight their suitability in different applications.
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Venue: arXiv cs.LG 2024
Year: 2024

Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only 3% of network parameters.