Reviewerly/exHarmony huggingface dataset:
Coupling Dynamic Model of Natural Environment Protection and Environmental Pollution Based on Machine Learning

As artificial intelligence develops, Machine Learning (ML) has gained more development opportunities.In the ecological field, the application of ML to Natural Environment Protection (hereinafter referred to as NEP) and Environmental Pollution (hereinafter referred to as EP) is currently a relatively hot field, which has been studied by many scholars.In order to explore the relationship between NEP and EP, this article constructed a Coupling Dynamic Model (hereinafter referred to as CDM) of NEP and EP based on ML through discussing ML and dynamic model.The model could reveal the stability of the positive balance of the natural ecosystem.Compared with the traditional way of building the dynamic model, the positive equilibrium stability values of the dynamic model in this method were controlled between 0.5 and 1.5.Its peak value changed less and its stability was better.This paper analyzed the relationship between natural environmental protection and EP by optimizing and innovating the dynamic model, thus providing more basis for the sustainable development of ecology.
Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. Finally, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.
Quantum temporal optics

Temporal optics, and specifically time-lenses and time-stretch systems, revolutionized the field of ultrafast measurements. Over the last years, we utilized time-lenses to measure different quantum schemes and studied the temporal modes of correlated photons with high temporal resolution. We developed temporal schemes based on quantum light, for realizing quantum tomography in the time-domain. We developed low-resolution time-lenses for weak quantum measurements. Finally, we suggest how quantum time-lens can lead to optical deep learning systems. In the talk, I will give an overview of the different types of quantum temporal schemes, elaborate on the future challenges in the field, and discuss the prospects and future applications which may be possible.
IHUMN: an improved high-utility itemsets mining algorithm with negative utility items

High-utility itemset mining is to mine high profit itemsets from transaction databases. But if there are some itemsets with negative utility values in the transaction database, the high-utility itemsets with the negative values may be pruned incorrectly and the subset of the low-utility itemsets may be the high-utility itemsets. In this paper, an improved high-utility itemsets mining algorithm with negative utility items (IHUMN) is proposed. A novel utility-list buffer structure with negative unit profits is proposed to efficiently store and retrieve utility-list, and reduce the memory consumption during the mining process. Moreover, Transitive Extension with Negative utility formula is constructed to compute the upper bound of utility avoiding the overestimation of low-utility itemsets as high-utility itemsets. The performance of IHUMN is evaluated, and compared against the FHN and GHUM method. The results of the experiments confirm that IHUMN has a favorable improvement in terms of time costs, the memory utilization and the number of visited nodes. The IHUMN algorithm consumes 40% less memory than GHUM. Moreover, the algorithm has good performance on dense datasets.
A Multi-Armed Bandit Recommender Algorithm Based on Conversation and KNN

With the wide application of recommendation systems in various fields, in order to effectively solve the cold-start problem in recommendation systems, contextual bandit algorithm uses user feedback to update user preferences online, converting the cold-start problem of recommendation systems into an exploration and exploitation problem. However, traditional contextual bandit algorithm is slow to learn due to the extensive exploration required. With the development of conversational recommendation, conversational contextual bandit algorithm learns the user's preference for key-term through conversation thus accelerating the learning speed. However, it only considers user feedback on key-term and ignores the relevance of key-term to each other. To solve the problem, a multi-armed bandit based on conversation and KNN (K-Nearest Neighbors) algorithm is proposed by introducing a more refined collaboration (KNNConUCB). Experiments on Synthetic data, as well as real datasets from Movielens and Last.FM, demonstrate the efficacy of the KNNConUCB algorithm.
Optical Flow-Attention Fusion Model for Deepfake Detection

With the development of deepfake technology, fake videos are being widely spread on media, which has caused serious social attention. Deepfake detection task has become a hot topic in the field of computer vision. In this paper, we propose a deepfake detection method that combines RGB images under the attention mechanism and optical flow characteristics to enhance the generalization of deepfake detection. In the RGB images module, we focus on the local area most relevant to tampering by erasing the most sensitive area of the attention block. In the optical flow module, the optical flow between frames is extracted and input into the backbone as the basis for classification. We compare our approach with state-of-the-art methods on FF++ and Celeb-DF. Experiment results have shown that our method achieves the same performance on the same dataset as state-of-the-art. In the Cross-dataset, our method outperforms most deepfake detection approaches.
Adversarial Training on Weights for Graph Neural Networks

Despite the fact that Graph Neural Networks (GNNs) have been extensively used for graph embedding representation, it is challenging to train well-performing GNNs on graphs with good generalization due to the limitation of overfitting. Previous research in Computer Vision (CV) has shown that the lack of generalization usually corresponds to the convergence of model parameters to sharp local minima. However, there is still a lack of related research in the field of graph analysis. In this paper, we investigate the loss landscape of models from the weight change perspective and show that the vanilla training method tends to cause GNNs to fall into sharp local minima with poor generalization. To tackle this problem, we propose a method named Adversarial Training on Weights (ATW) to flatten the weight loss landscape using adversarial training, thus improving the generalization of GNNs. Extensive experiments with multiple backbones on various datasets demonstrate the effectiveness of our method.
A new decoding method of the grammatical evolution

GE (grammatical Evolution) is an evolutionary algorithm that evolves programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules from the grammar definition of the Backs-Naur form(BNF) are used for the program in the genotype-phenotype mapping process. However, the matter relating to the completeness of individual phenotype throughout population formation isn't well self-addressed, so limiting both the convergence speed and the accuracy of the evaluations to some extent. In this paper, we propose an improved GE algorithm that aims to guarantee the completeness of individual phenotypes during initialization as well as subsequent evolution. A comparison of the improved algorithm (IGE) with the classical GE algorithm (CGE) and NGE(integer-coded grammatical evolution) conducted on the symbolic regression problems shows that the improved algorithm (IGE) not only reduces the search space and improves the accuracy of the algorithm, but also speeds up the convergence of the algorithm in constructing both.
An Improving List Scheduling Algorithm Based on Reinforcement Learning and Task Duplication

Task scheduling plays an important role in query execution, which affects the response time and system throughput of queries. Current database systems use simple heuristic algorithms to determine the order of scheduled tasks and executor allocation. This makes it hard for the scheduler to make full use of the characteristics of the task graph and the state information of executors to optimize the scheduling process dynamically. Especially in heterogeneous environments, it is difficult for heuristic algorithms to generate a better sequence for task execution and balance loads of executors. To address these challenges, we design a DAG scheduler based on graph attention network and reinforcement learning to make scheduling decisions. At first, the scheduler extracts features of each node in the DAG through the graph attention network and utilizes an LSTM module to obtain the high-level representations. Then, the RL agent calculates the probabilities by these representations and selects the node with the maximum probability for each step. Finally, the parameters of the agent will be updated until all the nodes of a DAG have been scheduled. The experimental results based on random DAGs and TPC-H workload reveal that the proposed model can outperform the existing heuristic algorithms by 30% at most on average makespan, which also has significant improvements on TPC-H workload.
VSLayout: Visual-Semantic Representation Learning For Document Layout Analysis

Document layout analysis (DLA), aiming to extract and classify the structural regions, is a rather challenging and critical step for many downstream document understanding tasks. Although the fusion of text (semantics) and image (vision) features has shown significant advantages for DLA, existing methods either require simultaneous text-image pair inputs, which is not applicable when only document images are available, or have to resort to an optical character recognition (OCR) preprocessing. This paper learns the visual-sematic representation for DLA only from the imaging modality of documents, which greatly extends the applicability of DLA to practical applications. Our method consists of three phases. Firstly, we train a text feature extractor (TFE) for document images via cross-modal supervision that enforces the coherence between the outputs of TFE and the text embedding map generated by Sent2Vec. Then the pretrained TFE gets further adapted using only the document images and extracts shallow semantic features that will be further fed into the third stage. Finally, a two-stream network is employed to extract the deep semantic and visual features, and their fusion is used as the input to a detector module, e.g., the RPN (Region Proposal Network), to generate the final results. On benchmark datasets, we demonstrate that the proposed TFE model outperforms main-stream semantic embedding counterparts and that our approach achieves superior DLA performance to baseline methods.
A Blockchain-based Decentralized Federated Learning Framework with Dual-Committees Consensus

Federated learning has gotten more and more attention in recent years. At the same time, blockchain is fast becoming a key instrument for achieving decentralization. The combination of blockchain and federated learning is becoming a hot topic of research. The general architecture for blockchain-based federated learning is to replace the central server with the blockchain with a single committee directly. However, the absence of partial nodes in the committee will make the distribution of samples uneven. In this paper, we introduce a blockchain-based decentralized federated learning Framework(BFLDC), and BFLDC creatively introduces the dual-committees. Different from the single committee, our dual-committees can guarantee to choose the good nodes and the others to construct the committee, which can make more nodes into the committee, as well as prevent the training process from the attack of the malicious nodes. Experimental results demonstrate the proposed framework can achieve the preset goals.
AutoAUG : Automatic Data Augmentation for Graph Neural Networks

Graph data augmentation has recently experienced significant attention, which is considered one of the effective solutions to solve the over-fitting problem of Graph Neural Networks (GNNs). However, the existing work tends to design a data augmentation strategy for specific datasets, which is difficult to ensure the diversity and consistency of the augmented graph distribution. It results in limited promotion beyond the specific datasets. Meanwhile, designing such graph augmentation strategies on different datasets require prior knowledge due to the different distributions with different graphs. It is challenging to design a general augmentation strategy suitable for different datasets. Our work proposes a learnable automatic augmentation framework AutoAUG to enhance the representation of GNNs. Specifically, we integrate multiple simple augmentation strategies into a set of candidate strategies and construct a search space based on the candidate strategy set. We use a search algorithm based on distribution matching to automatically explore the optimal augmentation strategy. The learned key parameters are used to assign different applying weights and augmentation magnitudes to balance the contribution of different graph augmentation strategies. Extensive experiments on multiple benchmarks show that our framework AutoAUG can improve the performance of downstream tasks in different GNN architectures and datasets with semi-supervised settings.
Retrieval Augmented via Execution Guidance in Open-domain Table QA

The goal of the open-domain table QA task is to answer a question based on retrieving and extracting information from a large corpus of structured tables. Currently, the accuracy of the most popular framework in open-domain QA: the two-stage retrieval, is limited by the table retriever. Inspired by the research on Text-to-SQL, this paper proposes to use execution guidance to enhance the effect of table retrieval. Our contributions are mainly threefold: 1. Proposed using execution-guided method to enhance table retrieval to fully leveraging schema information of tables. 2. Proposed the pure Text-to-SQL task for open domains. We design a two-stage Table QA framework based on semantic parsing to generate logical forms and answers simultaneously. 3. Proposed an open-domain Text-to-SQL dataset: Open-domain WikiSQL. We change the original WikiSQL to become suitable for the Open-domain setting, by removing the approximate tables, decontextualizing the questions, etc. We conducted experiments on the new dataset using BM25 and DPR as the retriever, and HydraNet as the generator of SQL. The results show that the execute-guided significantly improves the table retrieval by 19% (DPR in [email protected]) and achieves good performance (accuracy of logical form and execution improves by 12.7% and 13.1%) on end-to-end open-domain Text-to-SQL tasks as well.
Matching Long-form Document with Topic Extraction and Aggregation

BERT-based models have been widely used for document matching, but they generally do not perform well on the matching of long-form documents, as the sequence length limitation could lead to loss of information in the document. Also, the increased noise of a long-form document would further complicate the capture of key matching signals. To tackle these existing problems, we propose a new long-form document matching model, named EA-BERT. In this model, a set of topics are first extracted from the pair of documents. For each topic, we gather related sentences from both documents to form a "bag of sentences", which is then encoded by BERT into a vector containing topic-level information. All topic vectors are then passed to a transformer encoder to aggregate topic-level information into a document-level matching result for the pair of documents. In this way, our approach overcomes the sequence length limitation of BERT-based models, filters out the increased noise of long-form documents, and integrates matching signals from multiple topics. Experiments show that the proposed method outperforms current long-form document matching models on both Chinese and English document-level datasets.
Text abstraction based on user intent and deep analysis

In recent years, text analysis is mostly inaccurate and incomplete. To solve the above problem, a text abstraction method based on user intent and deep analysis is proposed. This method analyzes user intent, constructs user intent subtree by ontology theory, fully understands the various forms of user search keywords, mines and integrates features that conform to reality, uses deep learning model to train, and outputs text information that meets the requirement. Experiment shows that compared with keyword method and user intent method, by using the text abstraction method based on user intent and deep analysis, the number of returned result of abstraction text on related topics is higher, which indicates that the accuracy rate has been improved to a certain extent.
Three-stage Logical Table-to-Text Generation based on Type Control

Table-to-Text generation is to express the information in the table in words. Considering the simplicity and logic of the statement, the task of logical Table-to-Text generation is derived. Logical Table-to-Text generation is to generate logically faithful and smooth text based on tables. However, there are few studies on this task at present, and the effect of related generative models is not ideal, which makes this task still very challenging. In this paper, we propose a three-stage generative model that conforms to the human thinking of generating logical texts. On the basis of generating surface fact texts from tables, coarse templates are generated first, and then logical texts are generated. At the same time, in order to enhance the rationality of generating texts of different logical types from the same table input, we propose to take the logical type of the reference text as a known premise and input it together with the table and table title to generate texts with higher similarity. Experiments on the LogicNLG dataset show that both ideas have their effectiveness, and the model outperforms the baseline model overall. Combining the two ideas for experiments, the overall effect of the model is better, especially in terms of text similarity.
Social Media Text Summarisation Techniques and Approaches: A Literature Review

The great explosion in the abundance of information in the Social Media sector urgently demands proficient text summarization so that people are amply offered concise knowledge avoiding time-consuming procedures. Various text summarization techniques have already been broadly in use. Undeniably, though, the use of suitable Artificial Intelligence techniques could certainly lead to more efficient results. On these grounds, significant summarization techniques are reviewed in this paper and recent relevant literature work is illustrated. Finally, our approach based on implementation of AI techniques is also roughly presented.
Evaluating Quality of DIBR-synthesized Views based on Texture and Perceptual Hashing Similarity

Depth-Image-Based Rendering (DIBR) technology is widely used in 3D video systems to synthesize virtual views. However, the DIBR rendering process tends to introduce local and global distortions, especially local geometric distortion, that will severely affect the perception. In addition, traditional 2D quality metrics may fail to handle this issue since only global distortion is considered. Therefore, in order to evaluate the quality of virtual views more accurately, we propose a full reference DIBR-synthesized views quality assessment model that considers both local and global aspects. Local standard deviation texture images of the reference and distorted images are used to detect local distortions due to local distortions in the virtual view result in a large degree of variation in texture information. The intensity similarity and gradient similarity of the texture images are fused to obtain the final local distortion map. The perceptual hash similarity between the reference and the distorted image is used to quantify the global sharpness due to its powerful frequency domain analysis capability. Depending on the experimental results on the IRCCyN/IVC and IETR databases, the performance of our metric is competitive with the state-of-the-art methods.
Time interval-aware graph with self-attention for sequential recommendation

Sequential recommendation, as a branch under the recommendation system, obtains the user’s interest changes from the user’s interaction history to predict the next item. The neural network structure, Transformer and Graph Neural Networks (GNN) have been widely used in recommendation systems due to their ability to represent sequences and capture high-order information. However, previous models only rank actions in the time order of occurrence, ignoring the effect of the time interval between adjacent actions, which usually reflects the user’s preferences. To fully use time information, we design the Time Interval-aware Graph with Self-attention for sequential recommendation (TIGSA). Specifically, we first construct a time interval-aware graph, which integrates the information of different time intervals in all user action sequences. The time interval of two items determines the weight of each edge in the graph. Then the item model combined with the time interval information is obtained through the Graph Convolutional Networks (GCN). Finally, the self-attention block is used to adaptively compute the attention weights of the items in the sequence. Experiments show that our method outperforms other recommendation models on three public datasets and different evaluation metrics.