Reviewerly/exHarmony huggingface dataset:
The evaluation of innovation ability of China's information technology application innovation enterprises based on cloud modeling

This paper adopts the theoretical method of the cloud model to establish the evaluation index system. It tests it through examples, and the results show that the cloud model has a flexible conversion function in qualitative description and quantitative expression, and the evaluation results obtained can be visualized through graphs. It is concluded that the evaluation of the technology application innovation industry (ITAI) enterprise's innovation power by using the cloud modeling method can reflect the current level of the ITAI enterprise's innovation power more realistically, which is helpful to find out the innovation path of the ITAI enterprise.
Swarm Self-supervised Hypergraph Embedding for Recommendation

The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users’ requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm self-supervised hypergraph embedding (SHE) model to predict users’ interests by hypergraph convolution and self-supervised discrimination. SHE builds a hypergraph with multiple interest clues to alleviate the interaction sparsity issue and performs interest propagation to embed CF signals in hybrid learning on the hypergraph. It follows an auxiliary local view by similar hypergraph construction and interest propagation to restrain unnecessary propagation between user swarms. Besides, interest contrast further inserts self-discrimination to deal with long-tail bias issue and enhance interest modeling, which aid recommendation by a multi-task learning optimization. Experiments on public datasets show that the proposed SHE outperforms the state-of-the-art models demonstrating the effectiveness of hypergraph-based interest propagation and swarm-aware interest contrast to enhance embedding for recommendation.
Distributional Learning for Network Alignment with Global Constraints

Network alignment, pairing corresponding nodes across the source and target networks, plays an important role in many data mining tasks. Extensive studies focus on learning node embeddings across different networks in a unified space. However, these methods have not taken the large structural discrepancy between aligned nodes into account and, thus, are largely confined by the deterministic representations of nodes. In this work, we propose a novel network alignment framework highlighted by distributional learning and globally optimal alignment. By modeling the uncertainty of each node by Gaussian distribution, our framework builds similarity matrices on the Wasserstein distance between distributions and applies Sinkhorn operation, which learns the globally optimal mapping in an end-to-end fashion. We show that each integrated part of the framework contributes to the overall performance. Under a variety of experimental settings, our alignment framework shows superior accuracy and efficiency to the state-of-the-art.
Enhancing 6G network security: GANs for pilot contamination attack detection in massive MIMO systems

Accurate channel estimation at the base station (BS) is crucial for reliable, high-throughput communication in massive multiple input multiple output (MIMO) systems. However, the process of estimating channel matrices in massive MIMO systems is prone to pilot contamination attacks. These attacks not only degrade the efficiency of channel estimation but also increase the probability of information leakage. In this paper, a method is being proposed for detecting pilot contamination signals using a deep learning model, namely a generative adversarial network (GAN), at the BS of a massive MIMO system. GAN comprises two interconnected models, the first model is the generator, which creates synthetic samples, and the second model is the discriminator, which distinguishes between real and generator-generated samples. Residual block-based convolutional neural networks (CNNs) like ResNet and reverse ResNet are implemented as discriminator model of GAN for improving the detection of pilot-contaminated signals. The generator, structured as an autoencoder, generates artificial data resembling real data from noisy signals. The simulation results demonstrate that the proposed model effectively identifies the pilot-contaminated attacks; this effectiveness is evaluated through performance matrices called accuracy and loss function. Furthermore, the performance of the model is observed by varying the number of BS antennas and pilot lengths.
Using Neural and Graph Neural Recommender systems to Overcome Choice Overload: Evidence from a Music Education Platform

The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation technologies on educational platforms, with a particular focus on an educational platform for learning and practicing music. Our research is based on data from Tomplay, a music platform that offers sheet music with professional audio recordings, enabling users to discover and practice music content at varying levels of difficulty. Through our analysis, we emphasize the distinct interaction patterns on educational platforms like Tomplay, which we compare with other commonly used recommendation datasets. We find that interactions are comparatively sparse on educational platforms, with users often focusing on specific content as they learn, rather than interacting with a broader range of material. Therefore, our primary goal is to address the issue of data sparsity. We achieve this through entity resolution principles and propose a neural network (NN)-based recommendation model. Further, we improve this model by utilizing graph neural networks (GNNs), which provide superior predictive accuracy compared to NNs. Notably, our study demonstrates that GNNs are highly effective even for users with little or no historical preferences (cold-start problem). Our cold-start experiments also provide valuable insights into an independent issue, namely, the number of historical interactions needed by a recommendation model to gain a comprehensive understanding of a user. Our findings demonstrate that a platform acquires a solid knowledge of a user’s general preferences and characteristics with 50 past interactions. Overall, our study makes significant contributions to information systems research on business analytics and prescriptive analytics. Moreover, our framework and evaluation results offer implications for various stakeholders, including online educational institutions, education policymakers, and learning platform users.
A fully secure lattice-based signcryption with designated equality test in standard model

In hospital information systems, large volumes of electronic diagnostic records (EDRs) take up most of the storage space. While cloud server providers can reduce the local storage burden on hospitals and provide data-sharing services, the potential threat of sensitive data leakage and non-traceability of diagnoses prevents hospitals from uploading patients' diagnostic records to remote cloud servers directly. Therefore, to address security and traceability issues, we propose a new construction of post-quantum secure signcryption scheme with a designated equality test based on lattices (LB-SCDET) in this paper. Our LB-SCDET scheme allows a designated tester to perform equality tests on signcrypt-ciphertexts of EDRs without leaking what the EDRs actually are. Compared to the recent LB-SCET scheme proposed by Le et al., our LB-SCDET scheme implements a designated mechanism and is secure against offline message recovery attacks (OMRA). The comparison shows that our scheme enjoys a higher level of security, albeit the ciphertext size is slightly larger. Finally, we prove the scheme to be secure under the hardness of the Learning-with-Errors problem and unidirectionality of the Short-Integer-Solution problem. To be of independent interest, we show that the LB-SCET scheme fails to achieve the claimed IND-CCA security.
A dynamic adaptive multi-view fusion graph convolutional network recommendation model with dilated mask convolution mechanism

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

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

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

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.
A Machine Learning–Based Readability Model for Gujarati Texts

This study aims to develop a machine learning–based model to predict the readability of Gujarati texts. The dataset was 50 prose passages from Gujarati literature. Fourteen lexical and syntactic readability text features were extracted from the dataset using a machine learning algorithm of the unigram parts of speech tagger and three Python programming scripts. Two samples of native Gujarati speaking secondary and higher education students rated the Gujarati texts for readability judgment on a 10-point scale of “easy” to “difficult” with the interrater agreement. After dimensionality reduction, seven text features as the independent variables and the mean readability rating as the dependent variable were used to train the readability model. As the students' level of education and gender were related to their readability rating, four readability models for school students, university students, male students, and female students were trained with a backward stepwise multiple linear regression algorithm of supervised machine learning. The trained model is comparable across the raters’ groups. The best model is the university students’ readability rating model. The model is cross-validated. It explains 91% and 88% of the variance in readability ratings at training and cross-validation, respectively, and its effect size and power are large and high.
An Ensemble Strategy with Gradient Conflict for Multi-Domain Neural Machine Translation

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.
Ordering cross-spread gathers

Seismic processing on cross-spread gathers is a valuable but underexploited strategy. To do it properly, sources from a single source line and receivers from a single receiver line must be ordered in a physically sensible way, so that adjacent sources or receivers on a surface diagram are physically near each other. However, determining such an ordering is a challenge on irregularly acquired land data. I develop novel automatic ordering algorithms using tools from graph theory that minimize large gaps and preserve the sequential patterns found even in highly irregular acquisition. Java source code is available.
A survey of meta-heuristic algorithms in optimization of space scale expansion

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

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

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

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

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.