OpenRaiser/Intern-Atlas huggingface dataset:
AutoIE: An Automated Framework for Information Extraction from Scientific Literature
Venue: arXiv cs.IR 2024
Year: 2024

In the rapidly evolving field of scientific research, efficiently extracting key information from the burgeoning volume of scientific papers remains a formidable challenge. This paper introduces an innovative framework designed to automate the extraction of vital data from scientific PDF documents, enabling researchers to discern future research trajectories more readily. AutoIE uniquely integrates four novel components: (1) A multi-semantic feature fusion-based approach for PDF document layout analysis; (2) Advanced functional block recognition in scientific texts; (3) A synergistic technique for extracting and correlating information on molecular sieve synthesis; (4) An online learning paradigm tailored for molecular sieve literature. Our SBERT model achieves high Marco F1 scores of 87.19 and 89.65 on CoNLL04 and ADE datasets. In addition, a practical application of AutoIE in the petrochemical molecular sieve synthesis domain demonstrates its efficacy, evidenced by an impressive 78\% accuracy rate. This research paves the way for enhanced data management and interpretation in molecular sieve synthesis. It is a valuable asset for seasoned experts and newcomers in this specialized field.
Towards Precise 3D Human Pose Estimation with Multi-Perspective Spatial-Temporal Relational Transformers
Venue: arXiv cs.CV 2024
Year: 2024

3D human pose estimation captures the human joint points in three-dimensional space while keeping the depth information and physical structure. That is essential for applications that require precise pose information, such as human-computer interaction, scene understanding, and rehabilitation training. Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content. Given the remarkable self-attention mechanism of transformers, capable of capturing the spatial-temporal correlation from multi-view video datasets, we propose a multi-stage framework for 3D sequence-to-sequence (seq2seq) human pose detection. Firstly, the spatial module represents the human pose feature by intra-image content, while the frame-image relation module extracts temporal relationships and 3D spatial positional relationship features between the multi-perspective images. Secondly, the self-attention mechanism is adopted to eliminate the interference from non-human body parts and reduce computing resources. Our method is evaluated on Human3.6M, a popular 3D human pose detection dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on this dataset. The source code will be available at https://github.com/WUJINHUAN/3D-human-pose.
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator
Venue: arXiv cs.LG 2024
Year: 2024

Imitation learning is often used in addition to reinforcement learning in environments where reward design is difficult or where the reward is sparse, but it is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning do not require sampling data, but usually suffer from distribution shift. The methods based on reinforcement learning, such as inverse reinforcement learning and Generative Adversarial imitation learning (GAIL), can learn from only a few expert data. However, they often need to interact with the environment. Soft Q imitation learning (SQIL) addressed the problems, and it was shown that it could learn efficiently by combining Behavioral Cloning and soft Q-learning with constant rewards. In order to make this algorithm more robust to distribution shift, we propose more efficient and robust algorithm by adding to this method a reward function based on adversarial inverse reinforcement learning that rewards the agent for performing actions in status similar to the demo. We call this algorithm Discriminator Soft Q Imitation Learning (DSQIL). We evaluated it on MuJoCo environments.
Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
Venue: arXiv cs.LG 2024
Year: 2024

Activity detection is an important task in the next generation grant-free multiple access. While there are a number of existing algorithms designed for this purpose, they mostly require precise information about the network, such as large-scale fading coefficients, small-scale fading channel statistics, noise variance at the access points, and user activity probability. Acquiring these information would take a significant overhead and their estimated values might not be accurate. This problem is even more severe in cell-free networks as there are many of these parameters to be acquired. Therefore, this paper sets out to investigate the activity detection problem without the above-mentioned information. In order to handle so many unknown parameters, this paper employs the Bayesian approach, where the unknown variables are endowed with prior distributions which effectively act as regularizations. Together with the likelihood function, a maximum a posteriori (MAP) estimator and a variational inference algorithm are derived. Extensive simulations demonstrate that the proposed methods, even without the knowledge of these system parameters, perform better than existing state-of-the-art methods, such as covariance-based and approximate message passing methods.
Accelerated Cloud for Artificial Intelligence (ACAI)
Venue: arXiv cs.LG 2024
Year: 2024

Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions. Vertically, a single pipeline typically includes an initial ETL (Extract, Transform, Load) of raw datasets, a model training stage, and an evaluation stage where the practitioners obtain statistics of the model performance. Horizontally, many such pipelines may be required to find the best model within a search space of model configurations. Many practitioners resort to maintaining logs manually and writing simple glue code to automate the workflow. However, carrying out this process on the cloud is not a trivial task in terms of resource provisioning, data management, and bookkeeping of job histories to make sure the results are reproducible. We propose an end-to-end cloud-based machine learning platform, Accelerated Cloud for AI (ACAI), to help improve the productivity of ML practitioners. ACAI achieves this goal by enabling cloud-based storage of indexed, labeled, and searchable data, as well as automatic resource provisioning, job scheduling, and experiment tracking. Specifically, ACAI provides practitioners (1) a data lake for storing versioned datasets and their corresponding metadata, and (2) an execution engine for executing ML jobs on the cloud with automatic resource provisioning (auto-provision), logging and provenance tracking. To evaluate ACAI, we test the efficacy of our auto-provisioner on the MNIST handwritten digit classification task, and we study the usability of our system using experiments and interviews. We show that our auto-provisioner produces a 1.7x speed-up and 39% cost reduction, and our system reduces experiment time for ML scientists by 20% on typical ML use cases.
WideSA: A High Array Utilization Mapping Scheme for Uniform Recurrences on the Versal ACAP Architecture
Venue: arXiv cs.AR 2024
Year: 2024

The Versal Adaptive Compute Acceleration Platform (ACAP) is a new architecture that combines AI Engines (AIEs) with reconfigurable fabric. This architecture offers significant acceleration potential for uniform recurrences in various domains, such as deep learning, high-performance computation, and signal processing. However, efficiently mapping these computations onto the Versal ACAP architecture while achieving high utilization of AIEs poses a challenge. To address this issue, we propose a mapping scheme called \fname, which aims to accelerate uniform recurrences on the Versal ACAP architecture by leveraging the features of both the hardware and the computations. Considering the array architecture of AIEs, our approach utilizes space-time transformations based on the polyhedral model to generate legally optimized systolic array mappings. Concurrently, we have developed a routing-aware PLIO assignment algorithm tailored for communication on the AIE array, and the algorithm aims at successful compilation while maximizing array utilization. Furthermore, we introduce an automatic mapping framework. This framework is designed to generate the corresponding executable code for uniform recurrences, which encompasses the AIE kernel program, programmable logic bitstreams, and the host program. The experimental results validate the effectiveness of our mapping scheme. Specifically, when applying our scheme to matrix multiplication computations on the VCK5000 board, we achieve a throughput of 4.15TOPS on float data type, which is 1.11$\times$ higher compared to the state-of-the-art accelerator on the Versal ACAP architecture.
LATENTPATCH: A Non-Parametric Approach for Face Generation and Editing
Venue: arXiv cs.MM 2024
Year: 2024

This paper presents LatentPatch, a new method for generating realistic images from a small dataset of only a few images. We use a lightweight model with only a few thousand parameters. Unlike traditional few-shot generation methods that finetune pre-trained large-scale generative models, our approach is computed directly on the latent distribution by sequential feature matching, and is explainable by design. Avoiding large models based on transformers, recursive networks, or self-attention, which are not suitable for small datasets, our method is inspired by non-parametric texture synthesis and style transfer models, and ensures that generated image features are sampled from the source distribution. We extend previous single-image models to work with a few images and demonstrate that our method can generate realistic images, as well as enable conditional sampling and image editing. We conduct experiments on face datasets and show that our simplistic model is effective and versatile.
Analysis of Knowledge Tracing performance on synthesised student data
Venue: arXiv cs.CY 2024
Year: 2024

Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.
State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Tracking
Venue: arXiv cs.CL 2024
Year: 2024

Recently, low-resource dialogue state tracking (DST) has received increasing attention. First obtaining state values then based on values to generate slot types has made great progress in this task. However, obtaining state values is still an under-studied problem. Existing extraction-based approaches cannot capture values that require the understanding of context and are not generalizable either. To address these issues, we propose a novel State VAlue Generation based framework (SVAG), decomposing DST into state value generation and domain slot generation. Specifically, we propose to generate state values and use self-training to further improve state value generation. Moreover, we design an estimator aiming at detecting incomplete generation and incorrect generation for pseudo-labeled data selection during self-training. Experimental results on the MultiWOZ 2.1 dataset show that our method which has only less than 1 billion parameters achieves state-of-the-art performance under the data ratio settings of 5%, 10%, and 25% when limited to models under 100 billion parameters. Compared to models with more than 100 billion parameters, SVAG still reaches competitive results.
Enabling the Digital Democratic Revival: A Research Program for Digital Democracy
Venue: arXiv cs.CY 2024
Year: 2024

This white paper outlines a long-term scientific vision for the development of digital-democracy technology. We contend that if digital democracy is to meet the ambition of enabling a participatory renewal in our societies, then a comprehensive multi-methods research effort is required that could, over the years, support its development in a democratically principled, empirically and computationally informed way. The paper is co-authored by an international and interdisciplinary team of researchers and arose from the Lorentz Center Workshop on ``Algorithmic Technology for Democracy'' (Leiden, October 2022).
Computational Power of Opaque Robots
Venue: arXiv cs.DC 2024
Year: 2024

In the field of distributed computing by robot swarms, the research comprehends manifold models where robots operate in the Euclidean plane through a sequence of look-compute-move cycles. Models under study differ for (i) the possibility of storing constant-size information, (ii) the possibility of communicating constant-size information, and (iii) the synchronization mode. By varying features (i,ii), we obtain the noted four base models: OBLOT (silent and oblivious robots), FSTA (silent and finite-state robots), FCOM (oblivious and finite-communication robots), and LUMI (finite-state and finite-communication robots). Combining each base model with the three main synchronization modes (fully synchronous, semi-synchronous, and asynchronous), we obtain the well-known 12 models. Extensive research has studied their computational power, proving the hierarchical relations between different models. However, only transparent robots have been considered. In this work, we study the taxonomy of the 12 models considering collision-intolerant opaque robots. We present six witness problems that prove the majority of the computational relations between the 12 models. In particular, the last witness problem depicts a peculiar issue occurring in the case of obstructed visibility and asynchrony.
Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems
Venue: arXiv cs.IT 2024
Year: 2024

The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.
Quantum-Secure Hybrid Blockchain System for DID-based Verifiable Random Function with NTRU Linkable Ring Signature
Venue: arXiv cs.CR 2024
Year: 2024

In this study, we present a secure smart contract-based Verifiable Random Function (VRF) model, addressing the shortcomings of existing systems. As quantum computing emerges, conventional public key cryptography faces potential vulnerabilities. To enhance our VRF's robustness, we employ post-quantum Ring-LWE encryption for generating pseudo-random sequences. Given the computational intensity of this approach and associated on-chain gas costs, we propose a hybrid architecture of VRF system where on-chain and off-chain can communicate in a scalable and secure way. To ensure the validity and integrity of the off-chain computations (e.g., Ring-LWE encryption), we employ a quantum-secure linkable ring signature scheme on NTRU lattice and also delegated key generation (DKG) with a secure key encapsulation mechanism (KEM). Our decentralized VRF employs multi-party computation (MPC) with blockchain-based decentralized identifiers (DID), ensuring the collective efforts of enhanced randomness and security. We show the security and privacy advantages of our proposed VRF model with the approximated estimation of overall temporal and spatial complexities. We also evaluate our VRF MPC model's entropy and outline its Solidity smart contract integration. This research also provides a method to produce and verify the VRF output's proof, optimal for scenarios necessitating randomness and validation. Lastly, using NIST SP800-22 test suite for randomness, we demonstrate the commendable result with a 97.73% overall pass rate on 11 standard tests and 0.5459 of average p-value for the total 176 tests.
Interactive Byzantine-Resilient Gradient Coding for General Data Assignments
Venue: arXiv cs.IT 2024
Year: 2024

We tackle the problem of Byzantine errors in distributed gradient descent within the Byzantine-resilient gradient coding framework. Our proposed solution can recover the exact full gradient in the presence of $s$ malicious workers with a data replication factor of only $s+1$. It generalizes previous solutions to any data assignment scheme that has a regular replication over all data samples. The scheme detects malicious workers through additional interactive communication and a small number of local computations at the main node, leveraging group-wise comparisons between workers with a provably optimal grouping strategy. The scheme requires at most $s$ interactive rounds that incur a total communication cost logarithmic in the number of data samples.
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data
Venue: arXiv cs.LG 2024
Year: 2024

World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Wind energy has significant potential to not only reduce greenhouse emission, but also meet the ever increasing demand for energy. To enable the effective utilization of wind energy, addressing the following three challenges in wind data analysis is crucial. Firstly, improving data resolution in various climate conditions to ensure an ample supply of information for assessing potential energy resources. Secondly, implementing dimensionality reduction techniques for data collected from sensors/simulations to efficiently manage and store large datasets. Thirdly, extrapolating wind data from one spatial specification to another, particularly in cases where data acquisition may be impractical or costly. We propose a deep learning based approach to achieve multi-modal continuous resolution wind data prediction from discontinuous wind data, along with data dimensionality reduction.
WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN
Venue: arXiv cs.CR 2024
Year: 2024

The importance of addressing security vulnerabilities is indisputable, with software becoming crucial in sectors such as national defense and finance. Consequently, The security issues caused by software vulnerabilities cannot be ignored. Fuzz testing is an automated software testing technology that can detect vulnerabilities in the software. However, most previous fuzzers encounter challenges that fuzzing performance is sensitive to initial input seeds. In the absence of high-quality initial input seeds, fuzzers may expend significant resources on program path exploration, leading to a substantial decrease in the efficiency of vulnerability detection. To address this issue, we propose WGAN-AFL. By collecting high-quality testcases, we train a generative adversarial network (GAN) to learn their features, thereby obtaining high-quality initial input seeds. To overcome drawbacks like mode collapse and training instability inherent in GANs, we utilize the Wasserstein GAN (WGAN) architecture for training, further enhancing the quality of the generated seeds. Experimental results demonstrate that WGAN-AFL significantly outperforms the original AFL in terms of code coverage, new paths, and vulnerability discovery, demonstrating the effective enhancement of seed quality by WGAN-AFL.
An ARGoS plug-in for the Crazyflie drone
Venue: arXiv cs.RO 2024
Year: 2024

We present a new plug-in for the ARGoS swarm robotic simulator to implement the Crazyflie drone, including its controllers, sensors, and some expansion decks. We have based our development on the former Spiri drone, upgrading the position controller, adding a new speed controller, LED ring, onboard camera, and battery discharge model. We have compared this new plug-in in terms of accuracy and efficiency with data obtained from real Crazyflie drones. All our experiments showed that the proposed plug-in worked well, presenting high levels of accuracy. We believe that this is an important contribution to robot simulations which will extend ARGoS capabilities through the use of our proposed, open-source plug-in.
Lattice-Based Analog Mappings for Low-Latency Wireless Sensor Networks
Venue: arXiv cs.IT 2024
Year: 2024

We consider the transmission of spatially correlated analog information in a wireless sensor network (WSN) through fading single-input and multiple-output (SIMO) multiple access channels (MACs) with low-latency requirements. A lattice-based analog joint source-channel coding (JSCC) approach is considered where vectors of consecutive source symbols are encoded at each sensor using an n-dimensional lattice and then transmitted to a multiantenna central node. We derive a minimum mean square error (MMSE) decoder that accounts for both the multidimensional structure of the encoding lattices and the spatial correlation. In addition, a sphere decoder is considered to simplify the required searches over the multidimensional lattices. Different lattice-based mappings are approached and the impact of their size and density on performance and latency is analyzed. Results show that, while meeting low-latency constraints, lattice-based analog JSCC provides performance gains and higher reliability with respect to the state-of-the-art JSCC schemes.
BCM-Broadcast: A Byzantine-Tolerant Causal Broadcast Algorithm for Distributed Mobile Systems
Venue: arXiv cs.DC 2024
Year: 2024

This paper presents an algorithm, called BCM-Broadcast, for the implementation of causal broadcast in distributed mobile systems in the presence of Byzantine failures. The BCM-Broadcast algorithm simultaneously focuses on three critical challenges in distributed systems: Byzantine failures, Causality, and Mobility. We first present a hierarchical architecture for BCM-Broadcast. Then, we define twelve properties for BCM-Broadcast, including validity, integrity, termination, and causality. We then show that BCM-Broadcast satisfies all these properties. We also prove the safety of BCM-Broadcast; i.e., no healthy process delivers a message from any Byzantine process, assuming that the number of Byzantine processes is less than a third of the total number of mobile nodes. Subsequently, we show that the message complexity of BCM-Broadcast is linear. Finally, using the Poisson process, we analyze the probability of the violation of the safety condition under different mobility scenarios.