Reviewerly/exHarmony huggingface dataset:
A dynamic adaptive multi-view fusion graph convolutional network recommendation model with dilated mask convolution mechanism

Authors:
raw_author_name: Jian Liao
raw_affiliation_string: School of Computer and Information Technology, Shanxi University, China
raw_author_name: Feng Liu
raw_affiliation_string: School of Computer and Information Technology, Shanxi University, China
raw_author_name: Jianxing Zheng
raw_affiliation_string: Institute of Intelligent Information Processing, Shanxi University, China
raw_author_name: Suge Wang
raw_affiliation_string: Institute of Intelligent Information Processing, Shanxi University, China
raw_author_name: Deyu Li
raw_affiliation_string: Institute of Intelligent Information Processing, Shanxi University, China
raw_author_name: Qian Chen
raw_affiliation_string: School of Computer and Information Technology, Shanxi University, China

Abstract: Graph Convolutional Networks (GCNs) has shown promise in recommendation systems. However, a critical issue known as the over-smoothing problem has been identified in GCN models. This problem arises as the number of layers in the model increases, leading to a decrease in model performance due to node representations becoming excessively similar. Various strategies have been proposed to combat this issue, like preserving node-specific information or introducing high-order neighbors. Unfortunately, these approaches overlook a crucial factor known as Visual Overlap Strength (VOS), which plays a significant role in the over-smoothing problem. VOS measures the extent of neighborhood overlap between nodes in the graph. To address the over-smoothing problem, this paper offers a comprehensive analysis of the issue and introduces the concept of VOS between nodes. We present a novel solution in the form of the Dynamic Adaptive Multi-view fusion GCN model with dilAted maSK convolution mechanism (DAMASK-GCN). This model dynamically adjusts the perceptual field of nodes, captures high-order information, and alleviates the impact of VOS through the utilization of dilated mask convolution and an adaptive attention fusion mechanism. Extensive experiments on six popular recommendation system datasets demonstrate DAMASK-GCN's effectiveness in reducing over-smoothing and outperforming existing GCN-based recommendation models.
A Kernel Measure of Dissimilarity between <i>M</i> Distributions

Authors:
raw_author_name: Zhen Huang
raw_affiliation_string: Department of Statistics, Columbia University, New York, NY
raw_author_name: Bodhisattva Sen
raw_affiliation_string: Department of Statistics, Columbia University, New York, NY

Abstract: Given M≥2 distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD)—a parameter that quantifies the difference between the M distributions. The population KMD, which takes values between 0 and 1, is 0 if and only if all the M distributions are the same, and 1 if and only if all the distributions are mutually singular. Moreover, KMD possesses many properties commonly associated with f-divergences such as the data processing inequality and invariance under bijective transformations. The sample estimate of KMD, based on independent observations from the M distributions, can be computed in near linear time (up to logarithmic factors) using k-nearest neighbor graphs (for k≥1 fixed). We develop an easily implementable test for the equality of M distributions based on the sample KMD that is consistent against all alternatives where at least two distributions are not equal. We prove central limit theorems for the sample KMD, and provide a complete characterization of the asymptotic power of the test, as well as its detection threshold. The usefulness of our measure is demonstrated via real and synthetic data examples; our method is also implemented in an R package. Supplementary materials for this article are available online.
Fitting Latent Non-Gaussian Models Using Variational Bayes and Laplace Approximations

Authors:
raw_author_name: David Bolin
raw_affiliation_string: King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
raw_author_name: Håvard Rue
raw_affiliation_string: King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Abstract: Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to geostatistics, it is easy to find datasets that contain inherently non-Gaussian features, such as sudden jumps or spikes, that adversely affect the inferences and predictions made using an LGM. These datasets require more general latent non-Gaussian models (LnGMs) that can handle automatically these non-Gaussian features. However, fast implementation and easy-to-use software are lacking, preventing the broad applicability of LnGMs. In this article, we derive variational Bayes algorithms for fast and scalable inference of LnGMs. The approximation leads to an LGM that downweights extreme events in the latent process, reducing their influence and leading to more robust inferences. It can be applied to a wide range of models, such as autoregressive processes for time series, simultaneous autoregressive models for areal data, and spatial Matérn models. To facilitate Bayesian inference, we introduce the ngvb package, where LGMs implemented in R-INLA can be easily extended to LnGMs by adding a single line of code. Supplementary materials for this article are available online.
Variational quantum algorithms for simulation of Lindblad dynamics

Authors:
raw_author_name: Netanel H Lindner
raw_affiliation_string: Department of Physics, Technion Israel Institute of Technology, 3200003 Haifa, Haifa, Haifa, 3200003, ISRAEL

Abstract: Abstract We introduce variational hybrid classical-quantum algorithms to simulate the Lindblad master equation and its adjoint for time-evolving Markovian open quantum systems and quantum observables. Our methods are based on a direct representation of density matrices and quantum observables as quantum superstates. We design and optimize low-depth variational quantum circuits that efficiently capture the unitary and non-unitary dynamics of the solutions. We benchmark and test the algorithms on different models and system sizes, showing their potential for utility with near-future hardware.
An Ensemble Strategy with Gradient Conflict for Multi-Domain Neural Machine Translation

Authors:
raw_author_name: Zhibo Man
raw_affiliation_string: School of Computer and Information Technology, Beijing Jiaotong University, China
raw_author_name: Yujie Zhang
raw_affiliation_string: School of Computer and Information Technology, Beijing Jiaotong University, China
raw_author_name: Yu Li
raw_affiliation_string: School of Computer and Information Technology, Beijing Jiaotong University, China
raw_author_name: Yuanmeng Chen
raw_affiliation_string: School of Computer and Information Technology, Beijing Jiaotong University, China
raw_author_name: Yufeng Chen
raw_affiliation_string: School of Computer and Information Technology, Beijing Jiaotong University, China
raw_author_name: Jinan Xu
raw_affiliation_string: School of Computer and Information Technology, Beijing Jiaotong University, China

Abstract: Multi-domain neural machine translation aims to construct a unified neural machine translation model to translate sentences across various domains. Nevertheless, previous studies have one limitation is the incapacity to acquire both domain-general and domain-specific representations concurrently. To this end, we propose an ensemble strategy with gradient conflict for multi-domain neural machine translation that automatically learns model parameters by identifying both domain-shared and domain-specific features. Specifically, our approach consists of (1) a parameter-sharing framework, where the parameters of all the layers are originally shared and equivalent to each domain, and (2) ensemble strategy, in which we design an Extra Ensemble strategy via a piecewise condition function to learn direction and distance-based gradient conflict. In addition, we give a detailed theoretical analysis of the gradient conflict to further validate the effectiveness of our approach. Experimental results on two multi-domain datasets show the superior performance of our proposed model compared to previous work.
A survey of meta-heuristic algorithms in optimization of space scale expansion

Authors:
raw_author_name: Jinlu Zhang
raw_affiliation_string: Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China; Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, China
raw_author_name: Hao Sun
raw_affiliation_string: Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China; Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, China
raw_author_name: Ziyu Hu
raw_affiliation_string: Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China; Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, China

Abstract: Optimization problem of space scale expansion widely exists in practical applications, such as transportation, logistics, scheduling, social networks, etc. According to different expansion directions, the problem of space scale expansion can be divided into three categories: expansion of decision space, expansion of objective space and simultaneous expansion of decision space and objective space. These three types of problems correspond to large-scale global optimization problem, multi/many-objective optimization problem, and large-scale multi/many-objective optimization problem, respectively. Driven by the above problems, meta-heuristic algorithms (MHAs) with scalable characteristics have received extensive attention in this field. This paper summarizes the research progress of MHAs for space scale expansion optimization from three perspectives. Starting from the key difficulties of the optimization problem, the challenges brought by the expansion of the space scale to the existing MHAs are emphatically analyzed. From the perspective of methodology, the optimization methods of MHAs are divided into three modules: simplify problem structure, improve algorithm performance and expand application fields. Based on the review of performance evaluation benchmark problems, the simulation degree of various test suites for different practical application problems is summarized. In addition, some remaining challenges and future research directions on optimization of space scale expansion are discussed and analyzed.
Intrusion detection based on phishing detection with machine learning

Authors:
raw_author_name: K. Sangeetha
raw_affiliation_string: Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
raw_author_name: T. Kamaleshwar
raw_affiliation_string: Department of Computer Science and Engineering, Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, TN, India
raw_author_name: S. Udhaya Shree
raw_affiliation_string: Department of Computer Science and Engineering, Alpha College of Engineering and Technology, Puducherry, India

Abstract: Machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative Distribution Function gradient (CDF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method.
Capturing natural position relationships: A neural differential equation approach

Authors:
raw_author_name: Changqing Ji
raw_affiliation_string: School of Information Engineering, Dalian University, Dalian, 116622, China; School of Physical Science and Technology, Dalian University, Dalian, 116622, China
raw_author_name: Liyong Wang
raw_affiliation_string: School of Information Engineering, Dalian University, Dalian, 116622, China
raw_author_name: Jing Qin
raw_affiliation_string: School of Software, Dalian University, Dalian, 116622, China
raw_author_name: Xin Kang
raw_affiliation_string: Emergency Medical Department, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510920, China
raw_author_name: Zumin Wang
raw_affiliation_string: School of Information Engineering, Dalian University, Dalian, 116622, China

Abstract: The Transformer has emerged as the predominant model in Natural Language Processing due to its exceptional performance in various sequence modeling tasks, particularly in handling long-term dependencies. However, the traditional absolute and relative position encoding methods, which do not learn from data, tend to ignore the inherent structure of natural language sequences due to the position embedding layer at the input end of the Transformer model. This paper introduces a novel learnable neural Ordinary Differential Equation Position Encoding (ODEPE) method that can implicitly capture the natural position relationships within a sequence without requiring additional position embeddings. ODEPE can model continuous sequences and leverage differential equations to simulate the evolution of position information along the sequence, enabling position information to flow seamlessly between sequences. Additionally, a highly effective recurrent attention framework is proposed, which hybridizes attention with the ODEPE method to improve model performance. Compared to the Transformer-based sequence modeling network, our framework demonstrates a performance improvement of 24.0 points on the WikiText-103 dataset, while also achieving a performance improvement of 1.06 points on the Enwik8 dataset. This corresponds to an improvement of 4.9% and 0.17%, respectively.
Optimisation of classification methods to differentiate morphologically-similar pollen grains from FT-IR spectra

Authors:
raw_author_name: Matthew Craven
raw_affiliation_string: School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
raw_author_name: Ralph M. Fyfe
raw_affiliation_string: School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth PL4 8AA, UK

Abstract: A growing body of research is demonstrating the potential of Fourier-Transform Infrared spectroscopy (FT-IR) to identify and differentiate morphologically similar pollen taxa. The Poaceae (grass) family is a large and complex with morphologically similar pollen grains. It is not possible to use traditional light microscopy to differentiate Poaceae species, or genus, based on pollen morphological characteristics. This research presents a study of five species from the Poaceae family found across a wide variety of different moorland vegetation communities, to test the extent to which FT-IR microspectroscopy can be used to separate and identify these species and develop statistical approaches for the analyses of these data. Moorland grasses are of particular importance to assess conservation status and baselines in fragile and scarce vegetation communities, whose vegetation composition in the past remains cryptic owing to low taxonomic resolution. Non-differentiated and second derivative spectra were combined with Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to determine whether species had different chemical compositions and would cluster. Decision trees and random forest were used to classify each species and demonstrated 100% successful classification rate. This success demonstrates that using FT-IR microspectroscopy alongside spectral pre-processing and multivariate analysis can successfully identify and separate these moorland Poaceae species and has the clear potential to improve taxonomic resolution and classification of fossil pollen records. This will improve our understanding of how past land-use practice has shaped upland communities, provide more detailed ecologically-relevant palaeoecological information, and be utilised for the restoration and conservation of upland habitats.
Adaptive multi-channel contrastive graph convolutional network with graph and feature fusion

Authors:
raw_author_name: Luying Zhong
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
raw_author_name: Zhaoliang Chen
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
raw_author_name: Na Song
raw_affiliation_string: School of Mechanical, Electrical and Information Engineering, Putian University, Putian 351100, China
raw_author_name: Shiping Wang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China; Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen 518172, China

Abstract: The domain of multi-view semi-supervised classification is an appealing topic in real-world applications. Due to the powerful capability of gathering information from neighbors, Graph Convolutional Network (GCN) has become a hotspot in the classification task. However, most of multi-view classification works based on GCN only assign weights for feature fusion, and directly consider the weighted sum of the adjacency matrices, ignoring the interaction and correlation among features. These may be problematic since aggregating the matrices from less relevant views may destroy the original topology space, leading to undesired performance. To tackle the aforementioned challenges, this paper presents an Adaptive Multi-Channel Graph Convolutional Network (AMC-GCN). To extract the interactive information, AMC-GCN designs a deep interactive feature integration network to incorporate consensus and complementary information. To fuse the graph structures, AMC-GCN exploits the relevance between views and imposes an adjacency matrix fusion network on constructing multiple GCN channels, thereby delivering discriminative information on graphs. To enhance the homogeneity of the framework, AMC-GCN applies a contrastive loss to joint learning during the optimization for classification. With these considerations, AMC-GCN exploits relevant and interactive information between views to promote graph and feature fusion. Substantial experimental results on real-world datasets verify the superiority of AMC-GCN.
Deep semi-supervised clustering based on pairwise constraints and sample similarity

Authors:
raw_author_name: Xiao Qin
raw_affiliation_string: Center for Applied Mathematics of Guangxi, Nanning, Guangxi, China; Guangxi Key Lab of Human–machine Interaction and Intelligent Decision, Guangxi Academy of Science, Nanning, Guangxi, China
raw_author_name: Changan Yuan
raw_affiliation_string: Guangxi Key Lab of Human–machine Interaction and Intelligent Decision, Guangxi Academy of Science, Nanning, Guangxi, China
raw_author_name: Jianhui Jiang
raw_affiliation_string: Guangxi Key Lab of Human–machine Interaction and Intelligent Decision, Guangxi Academy of Science, Nanning, Guangxi, China
raw_author_name: Long Chen
raw_affiliation_string: Guangxi Key Lab of Human–machine Interaction and Intelligent Decision, Guangxi Academy of Science, Nanning, Guangxi, China; Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi, China

Abstract: Semi-supervised clustering methods enhance the performance of completely unsupervised clustering tasks by incorporating pairwise relationship information from a subset of samples. However, previous methods based on pairwise constraints have struggled to effectively leverage available prior knowledge and fully exploit the similarity relationships between samples. To address these issues, this paper proposes an advanced approach called Deep Semi-Supervised Clustering based on Pairwise Constraints and Sample Similarity (DSCPS). Specifically, DSCPS consists of two training stages: the coarse clustering stage and the fine clustering stage. In the coarse clustering stage, DSCPS assigns probabilities to each sample indicating its belongingness to each cluster by computing the distance to the cluster centers. In the fine clustering stage, DSCPS introduces a predictor network in the clustering space to predict the class labels of each sample. Simultaneously, the thresholds for similarity and dissimilarity between samples are determined based on pairwise constraints. Finally, the encoder and prediction network are further optimized using sample similarity relationships and pairwise constraints as loss conditions, resulting in the final clustering results. Extensive experiments demonstrate that DSCPS outperforms state-of-the-art methods, achieving the highest performance.