Reviewerly/exHarmony huggingface dataset:
Differential Butterfly Optimization Algorithm—A Hybrid Variant

During global search, a butterfly attracts towards the fittest butterfly where as in local search it performs a random walk due to which BOA get trapped in local optima and results in slow or poor convergence rate. To overcome this limitation, the basic structure of BOA is enhanced by embedding DE/current-to-best/1 mutation strategy of Differential Evolution (DE) optimization algorithm with a concept of bidirectional search. The proposed modified algorithm is termed as Differential Butterfly Optimization Algorithm (DBOA). This chapter also presents the complexity of the DBOA and the constraint handling process.
Discourse Component Recognition via Graph Neural Network in Chinese Student Argumentative Essays

Identifying and classifying original discourse components is a prerequisite task for constructing the knowledge graph structure. Previous work suffers from the following problems. (i) Existing methods only rely on the discourse components themselves to extract the features of the text, and do not fully take into account the potential help of the context information of the discourse to consummate its own features. (ii) Most of the current methods usually combine multiple tasks for joint inference, with complementary effects among them, lacking methods focusing on the single-target task of discourse component recognition. To address these issues, we propose a graph neural network-based discourse component recognition model (DCRGNN), which enhances the interaction of sentence-level discourse component features through graph structure. Our experimental results show that DCRGNN achieves a relative improvement of up to 6% on Macro-F1 for specific discourse component types compared to the previous state-of-the-art methods on the Chinese dataset, and exceeds the baseline model in the single-target task on the English dataset.
Privacy-Preserving Deep Learning in Internet of Healthcare Things with Blockchain-Based Incentive

Privacy-preserving deep learning has drawn tremendous attention recently, especially in the IoHT-enabled medical field. As a representative, federated learning can guarantee the privacy of training data and training models, but there are still many security issues that are ignored. During the training process, the content of parameters may be tampered with to affect the overall accuracy, and the parameter server may also be malicious. In this paper, we propose a blockchain architecture to solve these problems, which uses blockchain-based payment incentive method to force miners and medical institutions to behave honestly, thereby speeding up convergence. In addition, considering that the miners are disconnected in the real network environment, which leads to the interruption of the consensus protocol and affects the convergence speed, we design the Robust Proof-of-Stake (RPoS) consensus based on PVSS to solve this problem. Experiments show that the incentive mechanism we design can improve the accuracy of predictions and reduce the possibility of dishonesty among participants.
BLSHF: Broad Learning System with Hybrid Features

Broad Learning System (BLS), a type of neural network with a non-iterative training mechanism and adaptive network structure, has attracted much attention in recent years. In BLS, since the mapped features are obtained by mapping the training data based on a set of random weights, their quality is unstable, which in turn leads to the instability of the generalization ability of the model. To improve the diversity and stability of mapped features in BLS, we propose the BLS with Hybrid Features (BLSHF) algorithm in this study. Unlike original BLS, which uses a single uniform distribution to assign random values for the input weights of mapped feature nodes, BLSHF uses different distributions to initialize the mapped feature nodes in each group, thereby increasing the diversity of mapped features. This method enables BLSHF to extract high-level features from the original data better than the original BLS and further improves the feature extraction effect of the subsequent enhancement layer. Diverse features are beneficial to algorithms that use non-iterative training mechanisms, so BLSHF can achieve better generalization ability than BLS. We apply BLSHF to solve the problem of air quality evaluation, and the relevant experimental results empirically prove the effectiveness of this method. The learning mechanism of BLSHF can be easily applied to BLS and its variants to improve their generalization ability, which makes it have good application value.
Sentiment Analysis in Twitter by Resnet-50 and VGG-19

This study analyzes the feasibility of applying a number of convolutional neural network models to classify tweeter data. The messages of interest being the text notes of social communication are the objects for sentiment analysis. The study contributes a robust three-stage method for sentiment classification using text data from a social website, such as the Tweeter. Stage one is designed for text features extraction covering initial text processing and statistical examination that facilitates to convert text data to numerical features. In the stage two, implementation of convolution operations in many layers of convolutional neural networks is proposed to produce recognizable features from the numerical features. The use of Resnet-50 and VGG-19 is addressed in the stage allowing to get divergent sets of convolutional features for the same initial social dataset. Finally, classification is provided in the stage three which applies the Support Vector Machines in statistical learning the convolutional features. The description of the method is aligned with the Bayesian inference ensuring the robustness of the method. The results of experiments using a benchmark dataset are reported to show the validity of method.
Towards Robust Fingerprinting of Relational Databases by Mitigating Correlation Attacks

Database fingerprinting is widely adopted to prevent unauthorized data sharing and identify the source of data leakages. Although existing schemes are robust against common attacks, their robustness degrades significantly if attackers utilize inherent correlations among database entries. In this paper, we demonstrate the vulnerability of existing schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and their integration. We provide robust fingerprinting against these attacks by developing mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes and preserve the utility of databases. We investigate the impact of correlation attacks and the performance of mitigation techniques using a real-world database. Our results show (i) high success rates of correlation attacks against existing fingerprinting schemes (e.g., integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of mitigation techniques (e.g., after mitigation, integrated correlation attack can only distort 3% fingerprint bits). Additionally, the mitigation techniques effectively alleviate correlation attacks even if (i) attackers have access to correlation models directly computed from the original database, while the database owner uses inaccurate correlation models, (ii) or attackers utilize higher order of correlations than the database owner.
Knowledge-Based Neural Pre-training for Intelligent Document Management

Banks are usually large and complex companies that face a number of challenges to support the rapid and effective sharing of information and content across their organizations. Extracting complex metadata from raw bank documents is therefore central to support intelligent data indexing, information circulation and to promote more complex predictive capabilities, e.g., compliance assessment problems. In this paper, we present a weakly-supervised neural methodology for creating semantic metadata from bank documents. It exploits a neural pre-training method optimized against legacy semantic resources able to minimize the training effort. We studied an application to business process design and management in banks and tested the method on documents from the Italian banking community. The measured impact of the proposed training approach to process-related metadata creation confirms its applicability.
Discretization-Based Feature Selection as a Bilevel Optimization Problem

Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), etc. However, these methods share the same shortcoming which consists in encoding the problem solution as a sequence of cut-points. From this cut-points vector, the decision of deleting or selecting any feature is induced. Indeed, the number of generated cut-points varies from one feature to another. Thus, the higher the number of cut-points, the higher the probability of selecting the considered feature; and vice versa. This fact leads to the deletion of possibly important features having a single or a low number of cut-points, such as the infection rate, the glycemia level, and the blood pressure. In order to solve the issue of the dependency relation between the feature selection (or removal) event and the number of its generated potential cut-points, we propose to model the DBFS task as a bilevel optimization problem and then solve it using an improved version of an existing co-evolutionary algorithm, named I-CEMBA. The latter ensures the variation of the number of features during the migration process in order to deal with the multimodality aspect. The resulting algorithm, termed bilevel discretization-based feature selection (Bi-DFS), performs selection at the upper level while discretization is done at the lower level. The experimental results on several high-dimensional datasets show that Bi-DFS outperforms relevant state-of-the-art methods in terms of classification accuracy, generalization ability, and feature selection bias.
Knowledge Graph Embedding with Direct and Disentangled Neighborhood Representation Attention Network

Knowledge Graph Completion (KGC) has become a focus of attention across the deep learning community owing to its excellent contribution to numerous downstream tasks. Despite the recent surge in KGC work, most existing KGC models deal with triples in Knowledge Graphs (KGs) independently, ignoring the inherent and valuable information from the neighborhoods around entities and the dynamic properties of entities in different link prediction tasks. We propose a novel Direct and Disentangled Neighborhood Representation Attention Network (DDNAN) for KGC, with an adaptive selector to decide what kind of neighborhood information should be aggregated to the current entity. With the assistance of the relation-aware attention aggregator, our model can exploit Knowledge Graphs (KGs) to generate dynamic representations of the given different scenarios and prediction tasks. Extensive experiments on public benchmark datasets have been conducted to validate the superiority of DDNAN over existing methods in terms of both accuracy and interpretability. The code is available at https://github.com/Ariel-Gao/DDNAN .
Few-Shot Learning with Self-supervised Classifier for Complex Knowledge Base Question Answering

Complex Question Answering (CQA) over Knowledge Base (KB) involves transferring natural language questions to a sequence of actions, which are utilized to fetch entities and relations for final answer. Typically, meta-learning based models regard question types as standards to divide dataset for pseudo-tasks. However, question type, manually labeled in CQA data set, is indispensable as a filter in the support set retrieving phase, which raises two main problems. First, preset question types could mislead the model to be confined to a non-optimal search space for meta-learning. Second, the annotation dependency makes it difficult to migrate to other datasets. This paper introduces a novel architecture to alleviate above issues by using a co-training scheme featured with self-supervised mechanism for model initialization. Our method utilizes a meta-learning classifier instead of pre-labeled tags to find the optimized search space. Experiments in this paper show that our model achieves state-of-the-art performance on CQA dataset without encoding question type.
A Multi-objective Optimization Method for Joint Feature Selection and Classifier Parameter Tuning

Feature selection is an efficient method to extract useful information embedded in the data so that improving the performance of machine learning. However, as a direct factor that affects the result of feature selection, classifier performance is not widely considered in feature selection. In this paper, we formulate a multi-objective minimization optimization problem to simultaneously minimize the number of features and minimize the classification error rate by jointly considering the optimization of the selected features and the classifier parameters. Then, we propose an Improved Multi-Objective Gray Wolf Optimizer (IMOGWO) to solve the formulated multi-objective optimization problem. First, IMOGWO combines the discrete binary solution and the classifier parameters to form a mixed solution. Second, the algorithm uses the initialization strategy of tent chaotic map, sinusoidal chaotic map and Opposition-based Learning (OBL) to improve the quality of the initial solution, and utilizes a local search strategy to search for a new set of solutions near the Pareto front. Finally, a mutation operator is introduced into the update mechanism to increase the diversity of the population. Experiments are conducted on 15 classic datasets, and the results show that the algorithm outperforms other comparison algorithms.
Attention-based Learning for Multiple Relation Patterns in Knowledge Graph Embedding

Relations in knowledge graphs often exhibit multiple relation patterns. Various knowledge graph embedding methods have been proposed to modelling properties in relation patterns. However, relations with a certain relation pattern actually only account for a small proportion in the knowledge graph. Relations with no explicit relation patterns also show complicated properties which is rarely studied. To this end, we argue that a property of a relation should either be global or be partial, and propose an Attention-based Learning framework for Multi-relation Patterns (ALMP) for expressing complex properties of relations. ALMP adopts a set of affine transformations to express corresponding global relation properties. Furthermore, ALMP utilizes a module of attention mechanism to integrate the representations. Experimental results show that ALMP outperforms baseline models on the link prediction task.
Signo - An Application for Conversion of Sign Language to Text

Sign language’s that the oldest, also one in every one of the foremost natural types of language for the needs of communication, although the majority don't know the fundamentals of linguistic communication and therefore all of the interpreters basically are naturally difficult in returning by, henceforth we've got a true time method that uses the NN’s for fingerspelling’s supported American linguistic communication. In the progress of our method, the hands is first well-fitted with a filter and subsequently the filter is applied the hand is undergone a classifier that shall predict the category of the hand gestures. The used method provides an honest accuracy pertaining to the 26 letters of the alphabets.
Fourier Enhanced MLP with Adaptive Model Pruning for Efficient Federated Recommendation

Federated learning (FL) is gradually gaining traction as the de facto standard for distributed recommendation model training that takes advantage of on-device user data while reducing server costs. However, the computation resources of user devices in FL are usually much more limited compared to servers in a datacenter, which hinders the application of some advanced recommendation models (e.g., Transformer-based models) in FL. In addition, models with better recommendation performance tend to have more parameters, which increases the cost of communication between servers and user devices. Therefore, it is difficult for existing federated recommendation methods to achieve a good trade-off between recommendation accuracy and computation and communication costs. As a response, we propose a novel federated recommendation framework for efficient recommendations. First, we propose an all-MLP model by replacing the self-attention sublayer in a Transformer encoder with a Fourier sublayer, in which the noise information in the user interaction data is effectively attenuated using Fast Fourier Transform and learnable filters. Second, we adopt an adaptive model pruning technique in the FL framework, which can significantly reduce the model size without affecting the recommendation performance. Extensive experiments on four real-world datasets demonstrate that our method outperforms existing federated recommendation methods and strikes a good trade-off between recommendation performance and model size.
A Semi-supervised Video Object Segmentation Method Based on Adaptive Memory Module

Video object segmentation has becoming a hot research topic in the computer vision society, with a wide range of applications, such as autonomous driving, video editing, and video surveillance. However, due to the complexity of video data, video object segmentation still faces challenges like occlusion, object appearance changes, and similar objects. Previous methods mainly tackle this task by using the memory module, but the computation cost will linearly increase along with the length of the video. To deal with the issue of the previous memory-based method, we proposed a cascaded semi-supervised video object framework with an adaptive memory module. In addition, we use a cascaded instance tracker to find the object and reduce the image resolutions, and we further use a boundary estimation branch to improve the accuracy. Experimental results on several benchmarks demonstrate the effectiveness and efficiency of our proposed method.
SSA: A Content-Based Sparse Attention Mechanism

Recently, many scholars have used attention mechanisms to achieve excellent performance results on various neural network applications. However, the attention mechanism also has shortcomings. Firstly, the high computational and storage consumption makes the attention mechanism difficult to apply on long sequences. Second, all tokens are involved in the computation of the attention map, which may increase the influence of noisy tokens on the results and lead to poor training results. Due to these two shortcomings, attention models are usually strictly limited to sequence length. Further, attention models have difficulty in exploiting their excellent properties for modelling long sequences. To solve the above problems, an efficient sparse attention mechanism (SSA) is proposed in this paper. SSA is based on two separate layers: the local layer and the global layer. These two layers jointly encode local sequence information and global context. This new sparse-attention patterns is powerful in accelerating reasoning. The experiments in this paper validate the effectiveness of the SSA mechanism by replacing the self-attentive structure with an SSA structure in a variety of transformer models. The SSA attention mechanism has achieved state-of-the-art performance on several major benchmarks. SSA was validated on a variety of datasets and models encompassing language translation, language modelling and image recognition. With a small improvement in accuracy, the inference calculation speed was increased by 24%.
Deep Neural Factorization Machine for Recommender System

Factorization Machine (FM) and its many deep learning variants are widely used in Recommender Systems. Despite their success in many applications, there still remain inherent challenges. Most existing FM methods are incapable of capturing the similarity of features well and usually suffer from irrelevant features in terms of recommendation tasks. Hence, it is necessary to fully utilize the similarity interaction between different features. In this paper, we propose a Deep Neural Factorization Machine, named DNFM, which contains “wide” and “deep” parts based on Wide&Deep. In the wide part, we elaborately design a Dimension-weighted Factorization Machine (DwFM) to improve the original FM. DwFM assigns different weights vectors to feature interactions from different fields interactions. The “deep” part of DNFM is a neural network, which is used to capture the nonlinear information of the feature interactions. Then we unify both the wide and deep parts to comprehensive learn the feature interactions. Extensive experiments verify that DNFM can significantly outperform state-of-the-art methods on two well-known datasets.
Hardware and Software Co-optimization for Windows Attention

Since the attention mechanism was proposed, there have been many researches on the combination of deep learning and visual attention mechanism. Among them, Models built with self-attention mechanism have achieved SOTA results in the field of computer vision. However, the large number of parameters and the computational complexity of such models hinder their development and limit their use on resource-limited devices and platforms. In this paper, we make improvements to Windows Attention in Swin Transformer from the perspective of software and hardware co-optimization, and parallelize the design on FPGA platform. In Softmax module design, we use Taylor expansion to replace exp function which needs more computing resources under the premise of less accuracy loss; we also optimize the computational process of matrix multiplication, which is used many times, and design the corresponding hardware module. Experimental results show that our resource consumption on the ZCU102 FPGA decreases by 93% compared to the traditional exp function, and the throughput improves by 7.73 $$\times $$ and 1.21 $$\times $$ compared to the CPU and GPU, respectively.
Document-Level Multi-event Extraction via Event Ontology Guiding

Document-level Event Extraction (DEE) aims to extract event information from a whole document, in which extracting multiple events is a fundamental challenge. Previous works struggle to handle the Document-level Multi-Event Extraction (DMEE) due to facing two main issues: (a) the argument in one event can correspond to diverse roles in different events; (b) arguments from multiple events appear in the document in an unorganized way. Event ontology is a schema for describing events that contains types, corresponding roles, and their structural relations, which can provide hints to solve the above issues. In this paper, we propose a document-level Event Ontology Guiding multi-event extraction model (EOG), which utilizes the structural and semantic information of event ontology as role-orientated guidance to distinguish multiple events properties, thus can improve the performance of document-level multi-event extraction. Specifically, EOG constructs Event Ontology Embedding layer to capture the structural and semantic information of event ontology. A transformer-based Guiding Interact Module is then designed to model the structural information cross-events and cross-roles under the guidance of event ontology. Experimental results on the DMEE dataset demonstrate that the proposed EOG can achieve better performance on extracting multiple events from the document over baseline models.