Reviewerly/exHarmony huggingface dataset:
BrainLM: Estimation of Brain Activity Evoked Linguistic Stimuli Utilizing Large Language Models

In recent years, with the recent remarkable development of large-scale language models in natural language processing research, there has been an increasing number of studies employing large-scale language models to investigate the information processing mechanisms of encoding and decoding in the brain. In this study, we developed a new pre-trained language model, BrainLM, which incorporates paired data of brain activity induced by text and stimuli, and verified the accuracy of estimating brain states from natural language in multiple NLP tasks. In essence, our research has achieved several noteworthy accomplishments. Firstly, we successfully developed a multimodal model that incorporates both brain and text. Subsequently, we conducted bi-directional experiments to validate the model and ensure the reliability of both brain encoding and decoding processes. Furthermore, we performed meticulous comparative experiments, wherein we introduced 20 state-of-the-art (SOTA) language models as a control group. Our findings reveal that our proposed model outperforms superior brain encoding ability compared to the control group. Lastly, we designed a discrete Autoencoder module that extracts brain features. This module can be utilized independently to extract brain features in a wider range of brain decoding studies beyond fMRI.
Deep Moore-Penrose Inverse Network with Refinement Strategy for One-class Classification

Multilayer least-square (LS)-based one-class classification networks (MLS-OCNs) have gained great attention for the purpose of identifying anomalies and outliers. However, many MLS-OCNs encounter the issue of loosely connected feature coding because they use two separate mechanisms for feature encoding and final pattern recognition. This paper proposes a solution to this problem by introducing a multilayer algorithm called deep Moore-Penrose inverse network with refinement (DMPINR). In particular, DMPINR employs an end-to-end learning process based on the Moore-Penrose inverse (MPI) to identify optimal latent space and classify objects simultaneously. To enhance the robustness of representations, the DMPINR technique pulls back the residual error from the output layer to the hidden layers sequentially, recalculating the parameters of these hidden layers using MPI. The experimental results on ten popular OCC datasets demonstrate that the proposed approach outperforms many existing MLS-OCNs in G-Mean and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores.
PACO: Efficient Routing in Payment Channel Networks

Payment channel networks (PCNs) are indispensable solutions to the scalability issues of blockchains and act as capacity-enhancing plugins for the Bitcoin settlement mechanism. Compared to on-chain transaction processing, payment channel networks can achieve exponential throughput gains. However, the current PCNs still face bottlenecks in transaction success rates, with routing algorithms often adopting single-path payments, restricting the further development of PCNs. This paper introduces PACO, a multi-path routing algorithm based on the Ant Colony Optimization (ACO), which offers a systematic approach to initialize and update routing table probabilities for ant path-finding and subsequently selects feasible paths for final transaction amount forwarding. Experimental results demonstrate that the PACO algorithm outperforms traditional single-path routing algorithms.
M2SH: A Hybrid Approach to Table Structure Recognition using Two-Stage Multi-Modality Feature Fusion

Automatically recovering the original structure of tables from unstructured images is a challenging task, combining techniques from computer vision (CV) and natural language processing (NLP). Unfortunately, common feature extraction methods, naive fusion strategies, and rigid inductive biases have become roadblocks to the effective improvement of previous approaches. Distinguished from other modes of data representation, tables consist of many dispersed cells that are interdependent. Therefore, in this paper, we aim to propose a novel approach for recognizing table structures by mining the special properties of tables. The method begins by utilizing the adaptive fusion method to fuse visual and textual features acquired through a two-stream network. In the second stage, the layout features will be seamlessly integrated using a Kronecker-based strategy. The table elements with multi-modality features are then modeled based on spatial relationships. Interactions among them are established by a hybrid contextual aggregator that allows message passing at both local and global levels. Finally, table structure recognition is achieved by predicting the relationship between elements. We meticulously evaluate the proposed approach on various public datasets, including ICDAR2013, UNLV, WTW, SciTSR, and SciTSR-COMP, as well as a more complicated private dataset. The proposed method performs excellently on these datasets.
A State Merge-based Proposer-Builder Separation for Mitigating Maximum Extractable Value Threats in State Sharding

Maximum Extractable Value (MEV) refers to the maximum benefit obtained from the Decentralized Finance (DeFi) protocol during the block production process by utilizing the power of transaction ordering in the block. Although MEV plays a role in incentivizing the efficiency of DeFi projects, excessive abuse of this power can pose a series of problems and challenges to the blockchain, including an increased risk of chain forks. The State Sharding technique can fundamentally enhance the scalability of blockchain, but its consensus security also faces the potential threat of MEV. To mitigate the negative impact of MEV on state sharding, a scheme combining the Proposer-Builder Separation (PBS) and the State Merge is proposed. This scheme allows high-performance nodes to synchronize the state of adjacent shards and form a higher-level virtual shard, establishing a logical relationship resembling a complete binary tree among the shards. And through role separation, nodes participate in consensus within a leaf shard and composite virtual shards. At the same time, they can participate in block construction and seize MEV opportunities in other shards that are already synchronized. By confining transaction ordering authority outside the consensus shards, the security risk to the State Sharding consensus network from the MEV is reduced. However completely eliminating the MEV threats is very difficult and requires further research. Analytical and simulation-based validation shows that the proposed approach can mitigate the issue of decreased resistance to censorship and incentivize nodes to fully utilize their performance by leveraging MEV opportunities, thereby improving the blockchain's throughput capacity.
SAMI: A Structure-Aware Multi-Partition Embedding Interaction Model for Accurate Link Prediction in Knowledge Graphs

Knowledge graph embedding (KGE), which applies representation learning to represent entities and relationships in knowledge graphs, has attracted significant attention from researchers due to its potential applications in various domains. However, most of the existing KGE methods suffer from the limitation of using single semantic information. This limitation fails to capture the complex and structural information in knowledge graphs (KGs). In this paper, we introduce a novel method called the Structure-Aware Enhanced Multi- Partition Embedding Interaction (SAMI) model for Knowledge Graph Embedding (KGE). SAMI leverages both graph attention network and tensor decomposition to learn expressive and structural enhanced representations for KGs. Specifically, it uses the graph attention layers to aggregate nodes' features in a neighborhood as an encoder and utilizes an Enhanced Multi- Partition Embedding Interaction (EMEI) to learn independent local features as a decoder. SAMI shows impressive results on several popular datasets compared with baseline methods in terms of both Mean Reciprocal Rank (MRR) and Hits@K.
A Learning-Based Framework for Constrained Shortest Path Problems

The doubly resource constrained elementary shortest path problem (DRCESPP) has important applications in intelligent network scenarios. For solving this strong NP-hard problem, we introduce learning-based techniques and present a solution framework integrating preprocessing, graph neural networks (GNNs) and deep reinforcement learning (DRL). First, the preprocessing procedure reduces the network size and provides initial feasible paths. Then, the classifier, implemented by a graph attention network (GAT), filters out the reduced networks where better paths exist after preprocessing. Finally, a DRL-based heuristic attempts to construct the optimal path for the filtered reduced networks with an end-to-end solution paradigm. We devise a crafted reward function and a shared low-variance baseline for the reinforcement learning optimization algorithm. Our experiments suggest that the proposed framework achieves better performance compared with competitive heuristic algorithms in terms of solution quality and computational efficiency.
A Hash Time Lock Mechanism Based on Threshold Algorithm

Blockchain is a distributed digital ledger, because of its decentralization, tamper-proof and other technical characteristics, has been applied to finance, medical management, smart cities and other fields. However, as new blockchain systems continue to emerge, there are information islands and value islands between different blockchains, which largely limits the scalability and sustainable development of the blockchain. To address the challenge of interoperability between blockchains, cross-chain asset exchange technology has garnered considerable attention as a research focal point. This paper proposes a hash time lock mechanism based on threshold algorithm, which mainly solves the hash lock security problem in hash lock technology, and improves the hash lock security through threshold encryption algorithm. At the same time, a homomorphic encryption scheme is designed to protect the privacy of the transaction verification process during the verification phase of the hash lock transaction.
Short Text Matching Based on Multi-granularity Semantic Information

With the rapid development of online social networks, short text has become the main medium for users to transmit information. Chinese short text matching usually uses a sentence pair as output to obtain better results. However, there may be errors, ambiguities or inconsistencies in Chinese word segmentation, which affect the final laboratory matching effect. In order to solve this problem, we use the word lattice graph as input to retain multi-granularity information, so that the semantic information can be fully utilized. Experimental results on two Chinese data sets show that multi-granularity information improves text matching to some extent.
Two-Phase Election Algorithm for Cross-Chain Notaries

With the development and innovation of blockchain technology, various blockchain networks with distinct characteristics have emerged. These differences are mainly due to variations in system architecture and application scenarios among different blockchain platforms. This diversity has given rise to the challenge of interoperability between data and assets on different blockchains. This has resulted in the creation of multiple data and value silos that negatively affect the adoption and utilization of blockchain technology.In order to overcome this challenge and achieve seamless interconnection and value transfer between different chains, blockchain cross-chain technology plays a vital role. One crucial aspect of cross-chain technology is the notary mechanism, which ensures the security of cross-chain transactions by involving groups of notaries. However, concerns regarding the security of these notaries have emerged. To address this concern and guarantee the security of cross-chain notaries, this paper proposes a two-stage election algorithm. The experimental results demonstrate the effectiveness of this algorithm in preventing malicious nodes from becoming notaries, as well as increasing the likelihood of reliable nodes becoming notaries. Moreover, the algorithm ensures the randomness and fairness of the election process, ultimately enhancing the overall security of cross-chain notary groups.
Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets.
A Node-Collaboration-Informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from big data-connected applications like the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, which is thus incorporated into a GCN model as a node-collaboration module for supplementing the collaboration loss. Based on this essential idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) model is proposed with three-fold ingredients: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on four UWGs emerging from real applications demonstrate that owing to its efficient modeling of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its high scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies.
EPO-S: A Constrained RL Method to Enhance UAV Safety with Spatial Representation

Path planning and collision avoidance are critical components of UAV control algorithms that play a crucial role in executing UAV missions. As scenarios become increasingly complex, the traditional control methods just ain't cutting it to meet the requirements. Reinforcement learning is an emerging decision-making control algorithm that attempts to address these issues as an alternative to traditional methods and has made significant advances. Unfortunately, standard RL approaches only aim to maximize rewards, however balancing task performance and safety in completing UAV tasks poses a challenge since these two objectives sometimes conflict, leading to a trade-off often difficult to manage. This paper proposes three techniques to address this problem. First, we model the path planning and collision avoidance issue in a constrained RL framework, eliminating the need for complex reward engineering. Second, we expand our previous work in the UAV setting and introduce an exact penalty optimization (EPO) algorithm to provide stricter constraint guarantees. We also propose a novel spatial information representation method for the UAV scenario to help UAVs better understand environmental information. The experimental results demonstrate the effectiveness of the EPO and spatial representation modules proposed in this paper, through a significant reduction in collisions as well as a strong improvement in the rate of reaching the destination.
A Hybrid Framework for Video Anomaly Detection Based on Semantic Consistency of Motion and Appearance

Video anomaly detection aims to identify abnormal events deviating from the expected behavior. Because the definition of abnormal and normal behavior is challenging to distinguish directly, and the occurrence of abnormal behavior is rare and random, video anomaly detection becomes challenging. This paper proposes a hybrid architecture MAU-VAD based on the premise that the semantic consistency of appearance features and motion information is high in normal events but low in anomaly events. First, we design a motion feature reconstruction module to reconstruct optical flow, which aims to record the stable motion information in normal events. Afterward, the motion information of the reconstructed optical flow and the appearance information of the corresponding original frame are extracted separately using a two-stream autoencoder, and the extracted motion features and appearance features are constrained and fused to generate predicted future frames. Since the reconstruction quality of optical flow directly affects the generation quality of final future frames, the reconstructed optical flow of abnormal events has a greater impact on the quality of generated future frames, thus improving the efficiency of anomaly detection. Experimental results on three standard public datasets demonstrate the effectiveness of the method.
Self-Supervised Learning Using Noisy-Latent Augmentation

Generally speaking, labeled data is difficult and expensive to provide for applications in machine learning and data mining. One of the earliest approaches to tackle this problem is semi-supervised self-training to take advantages of labeled and unlabeled data to create pseudo-labeled data. However, reaching a high level of confidence to predict the unseen data by a classifier with a limited number of labeled samples is a challenging task. Generating pseudo-labeled data can collaboratively improve the self-training to increase its confidence for further predictions. This paper proposes a collaborative framework between augmentation and self-training to accurately train a model with very limited labeled data. Our framework includes two components where the first component is an unsupervised technique to augment labeled data and feed to the second component which is self-training. The first component uses a custom variational autoencoder architecture (VAE) to generate new samples by adding randomly generated noise to encoded latent representation. As a result, the augmentation component can generate the unique and unexplored images with respect to limited input data distribution. We evaluated the proposed framework on handwritten MNIST image dataset. The conducted experiment shows that the generative component can be helpful in overcoming the problem of inaccurate self-training prediction when sufficient labeled data is not accessible.
A Neighbor-Induced Graph Convolution Network for Undirected Weighted Network Representation

Precise representation learning to an undirected weighted network (UWN) is the foundation of understanding its connection patterns and functional mechanisms. A graph convolution network (GCN) is frequently utilized to tackle this issue. However, it only considers the neighbor information in the forward propagation process, which unfortunately impairs its representation learning ability. Motivated by this discovery, this work proposes a novel Neighbor-induced Graph Convolution Network (N-GCN) that adopts two-fold ideas: a) employing the weighted forward propagation process, which aggregates the neighbor information by considering the interaction strength of node pair; b) incorporating a neighbor-regularizer into the loss function, which induces the neighbor information to illustrate a UWN's intrinsic symmetry, thereby boosting the representation learning ability. Experimental results on four UWNs validate the proposed N-GCN outperforms the state-of-the-art models in achieving a highly accurate representation of UWNs emerging from real applications.
A Physiological Approach of Presence and VR Sickness in Simulated Teleoperated Social Tasks

The presence (or telepresence) feeling and virtual reality (VR) sickness affect the task execution in teleoperation. Most teleoperation works have assessed these concepts using objective (physiological signals) and subjective (questionnaires) measurements. However, these works did not include social tasks. To the best of our knowledge, there was no physiological approach in teleoperation of social tasks. We measured presence and VR sickness in a simulation of teleoperated social tasks by questionnaires and analyzed the correlation between their scores and multimodal biomarkers. The results showed some different correlations from the findings of non-teleoperation studies. These correlations were between presence and neural biomarkers in the frontal-central and central regions (for the beta and delta bands) and between VR sickness and brain biomarkers in the occipital region (for the alpha and beta bands) and the mean temperature. This work revealed significant correlations to support some biomarkers as predictors of the trend of presence and VR sickness in simulated teleoperated social tasks. These biomarkers might also be valid to predict the trend of telepresence and motion sickness in teleoperated social tasks in a remote environment.
ICE-YoloX: An Effective Face Mask Detection Method

Deep learning technologies such as YoloX have achieved impressive progress in face mask detection recently. However, the neck network used in YoloX network may lead to severe confounding effect in feature mapping due to the inherent defect of channel reduction in hybrid fusion, which affects its precise localization ability of mask-wearing targets. To tackle this issue, we present a new FPN network structure (ICE-FPN) based on channel-enhanced feature pyramid network (CE-FPN) in this paper, which can mitigate the YoloX network confounding effect while reducing the number of parameters and computational effort caused by CE-FPN. Experiments conducted on the WMD dataset show that the mAP0.5 of the model improves from 99.54% to 99.62% and the mAP0.75 improves from 89.47% to 91.35%. The ablation and comparison experiments demonstrate that the proposed ICE-YoloX has achieved superior performance over existing methods.