Reviewerly/exHarmony huggingface dataset:
A neighborhood-aware graph self-attention mechanism-based pre-training model for Knowledge Graph Reasoning

Knowledge Graph Reasoning (KGR) is an effective method to ameliorate incompleteness and sparsity problems, which infers new knowledge based on existing knowledge. The Graph Neural Network (GNN)-based approaches can obtain advanced effectiveness. However, it still suffers from some problems such as obtaining insufficient graph features, introducing noises, ignoring path connectivity, and acquiring incomplete neighborhood information. This paper proposes a neighborhood-aware (NA) graph self-attention mechanism-based pre-training model for KGR, namely NA-KGR. The proposed model is composed of two phases. The first phase is an enhanced graph attention network, which can use the weighted characteristics of its neighbors to represent and encode the entities that are most likely to have a positive effect on reasoning. The second phase is a neighborhood-aware self-attention mechanism, which makes the model more able to obtain information from the neighbor entities for inference by increasing an adaptive entity similarity matrix when calculating the attention score. Moreover, we propose a pre-training pattern based on neighborhood-aware random walk sampling and general subgraph structure sampling to improve NA-KGR's generalization ability. Extensive comparison and ablation experimental results on various benchmarks unambiguously demonstrate that the proposed NA-KGR model can obtain the state-of-the-art results of current GNN-based methods.
A Bayesian approach to (online) transfer learning: Theory and algorithms

Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could help solve a related task, if not executed properly, transfer learning algorithms can impair the learning performance instead of improving it – commonly known as negative transfer. In this paper, we use a parametric statistical model to study transfer learning from a Bayesian perspective. Specifically, we study three variants of transfer learning problems, instantaneous, online, and time-variant transfer learning. We define an appropriate objective function for each problem and provide either exact expressions or upper bounds on the learning performance using information-theoretic quantities, which allow simple and explicit characterizations when the sample size becomes large. Furthermore, examples show that the derived bounds are accurate even for small sample sizes. The obtained bounds give valuable insights into the effect of prior knowledge on transfer learning, at least with respect to our Bayesian formulation of the transfer learning problem. In particular, we formally characterize the conditions under which negative transfer occurs. Lastly, we devise several (online) transfer learning algorithms that are amenable to practical implementations, some of which do not require the parametric assumption. We demonstrate the effectiveness of our algorithms with real data sets, focusing primarily on when the source and target data have strong similarities.
A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering

In this article, we propose and study a general framework for comparing and evaluating soft clusterings, viewed as a form of uncertainty quantification for clustering tasks, with the aim of studying the uncertainty that arises from such a comparison. Our proposal is based on the interpretation of soft clustering as describing uncertain information, represented in the form of a mass function, about an unknown, target hard clustering. We present a general construction to this purpose, called distributional measures, whereby any evaluation measure can be naturally extended to soft clustering. We study the theoretical properties of the proposed approach, both in terms of computational complexity and metric properties. Furthermore, we study its relationship with other existing proposals providing, in particular, necessary and sufficient conditions for the equivalence between distributional measures and the recently proposed framework of transport-theoretic measures. We also propose sampling-based approximation algorithms with convergence guarantees, making it possible to apply the proposed method to real-world datasets. Finally, we apply the distributional measures in a computational experiment in order to illustrate their usefulness.
Image Encryption Through Cellular Automata, S-Box and Tent Chaotic Map

Scientists and engineers face increasing pressure to develop innovative and effective security measures to protect multimedia transmitted through the Internet and insecure communication channels. This paper proposes a 3stage image encryption technique using cellular automata, an S-box, and the Tent chaotic map. Performance analysis of the recommended encryption techniques indicates strong resilience to various attacks at a minimal computational cost. Thus, the proposed image encryption technique is suitable for real-time applications.
Three Layered Image Encryption: An Application of Hyperchaos and Cellular Automata

The effectiveness of image encryption is increasing every day due to the increased demand for online images and concerns about their privacy. In this paper, a 3-stage image encryption algorithm is proposed. This paper utilizes a new 7D hyperchaotic function in the first stage. Then, an extended cellular automaton S-box is used in the second stage. Finally, cellular automata is used as the third layer. The proposed encryption technique exhibits high resistance to a variety of attacks while consuming little computational resources, making it suitable for real-time applications.
A Modular Quantum Compilation Framework for Distributed Quantum Computing

For most practical applications, quantum algorithms require large resources in terms of qubit number, much larger than those available with current NISQ processors.With the network and communication functionalities provided by the Quantum Internet, Distributed Quantum Computing (DQC) is considered as a scalable approach for increasing the number of available qubits for computational tasks.For DQC to be effective and efficient, a quantum compiler must find the best partitioning for the quantum algorithm and then perform smart remote operation scheduling to optimize EPR pair consumption.At the same time, the quantum compiler should also find the best local transformation for each partition.In this paper we present a modular quantum compilation framework for DQC that takes into account both network and device constraints and characteristics.We implemented and tested a quantum compiler based on the proposed framework with some circuits of interest, such as the VQE and QFT ones, considering different network topologies, with quantum processors characterized by heavy-hexagon coupling maps.We also devised a strategy for remote scheduling that can exploit both TeleGate and TeleData operations and tested the impact of using either only TeleGates or both.The evaluation results show that TeleData operations can have a positive impact on the number of consumed EPR pairs, depending on the characteristic of compiled circuit.Meanwhile, choosing a more connected network topology helps reduce the number of layers dedicated to remote operations.
Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level

Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to their dependence on climate and geography. While numerous studies have focused on solar energy time series, few have specifically addressed the critical task of forecasting solar energy production at the national level. Accurate national-level forecasting is crucial for optimizing energy management, informing policy development, and promoting environmental sustainability. This study aims to address the challenges associated with the significant variability in renewable energy production and its impact on grid stability by improving the accuracy of existing forecasting approaches. To achieve this goal, we evaluate the effectiveness of univariate and multivariate approaches for time series forecasting of national solar energy production data from ESIOS (the Spanish System Operator). Our primary focus is on leveraging external solar variables, such as solar irradiance data. To this end, we propose a methodology to integrate solar irradiance forecasts with historical data from solar power plants in Spain to improve the performance of multivariate models. Subsequently, we compare the performance of classical regression techniques and state-of-the-art deep learning algorithms, presenting univariate and multivariate models for three forecast horizons (1 h, 24 h, and 48 h). Finally, we assess the performance of our best univariate and multivariate models by comparing them with the official forecast of ESIOS. Our findings indicate that the best-performing models are deep-learning multivariate approaches, which benefit from incorporating solar irradiance forecasts, particularly for longer forecast horizons (24 h and 48 h), and avoid the detrimental effects of the Hughes Phenomenon, which seems to hamper non-deep-learning forecasters. The top-performing models, based on Convolutional Networks and Convolutional + Recurrent Neural Networks, outperform ESIOS by reducing mean absolute error by 41% and 47.58%, respectively.
Efficient and privacy-preserving tree-based inference via additive homomorphic encryption

Due to the excellent efficiency and high interpretability, coupled with the accuracy comparable to deep learning on tabular data, various tree-based models have been widely concerned and succeeded in many fields like medical diagnosis, credit evaluation, and fault detection. However, the model owner may face dilemmas such as compromised privacy and insufficient machine performance when providing tree-based inference services. Therefore, this paper proposes an efficient and privacy-preserving tree-based inference scheme, through which users can enjoy secure, high-accuracy, and transparent outsourcing inference services from one cloud. Specifically, by adopting additive homomorphic encryption as the underlying privacy-preserving technique, we design a suite of perturbation and recovery methods, processing cipher-based inference while masking the inference path. Meanwhile, without additional interactions, users can directly obtain high-accuracy results comparable to non-privacy inference, so the used privacy-preserving methods seem transparent to users. Moreover, through optimizing algorithm design and precalculating partial ciphertexts, the efficiency of our scheme is improved significantly. Formal security analysis demonstrates that our scheme achieves selective security under the honest-but-curious model. Extensive experimental evaluation shows the lossless accuracy, low complexity, and high efficiency of our scheme, which is practical to real-world scenes, especially for large-scale models.
Real-Time Monocular Skeleton-Based Hand Gesture Recognition Using 3D-Jointsformer

Automatic hand gesture recognition in video sequences has widespread applications, ranging from home automation to sign language interpretation and clinical operations. The primary challenge lies in achieving real-time recognition while managing temporal dependencies that can impact performance. Existing methods employ 3D convolutional or Transformer-based architectures with hand skeleton estimation, but both have limitations. To address these challenges, a hybrid approach that combines 3D Convolutional Neural Networks (3D-CNNs) and Transformers is proposed. The method involves using a 3D-CNN to compute high-level semantic skeleton embeddings, capturing local spatial and temporal characteristics of hand gestures. A Transformer network with a self-attention mechanism is then employed to efficiently capture long-range temporal dependencies in the skeleton sequence. Evaluation of the Briareo and Multimodal Hand Gesture datasets resulted in accuracy scores of 95.49% and 97.25%, respectively. Notably, this approach achieves real-time performance using a standard CPU, distinguishing it from methods that require specialized GPUs. The hybrid approach's real-time efficiency and high accuracy demonstrate its superiority over existing state-of-the-art methods. In summary, the hybrid 3D-CNN and Transformer approach effectively addresses real-time recognition challenges and efficient handling of temporal dependencies, outperforming existing methods in both accuracy and speed.
An improved PBFT consensus algorithm based on grouping and credit grading

Abstract To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.
Parents' View of Graphic Novels in Hong Kong Under the 21st Century Mobile Digital Environment

This study reviews the current parents' perspectives and habits of using graphic novels, including electronic equipment, in Hong Kong to support children's reading and learning. Parents were invited to participate in semi-structured group interviews based on our five research questions. The authors employ a thematic analysis approach to analyze the data and determine the similarities and differences between the literature review and the current mobile digital environment. The findings indicated that respondents were not keen on using graphic novels to support their children's reading and learning. The two main reasons were inadequate understanding or bias on graphic novels and the wide availability of online digital materials for reading and learning support. Scant studies focus on graphic novels in parent-child reading from parents' perspectives, especially in the Asian context, though graphic novels are becoming a worldwide trend. Parents' opinions about this issue are invaluable for educators and librarians in curriculum design and collections development.
Lithium-Rich Pegmatite Detection Integrating High-Resolution and Hyperspectral Satellite Data in Zhawulong Area, Western Sichuan, China

Lithium (Li) has grown to be a strategic key metal due to the enormous demand for the development of new energy industries over the world. As one of the most significant sources of Li resources, pegmatite-type Li deposits hold a large share of the mining market. In recent years, several large and super-large spodumene (Spd)-rich pegmatite deposits have been discovered successively in the Hoh-Xil–Songpan-Garzê (HXSG) orogenic belt of the northern Tibetan Plateau, indicative of the great Li prospecting potential of this belt. Hyperspectral remote sensing (HRS), as a rapidly developing exploration technology, is especially sensitive to the identification of alteration minerals, and has made important breakthroughs in porphyry copper deposit exploration. However, due to the small width of the pegmatite dykes and the lack of typical alteration zones, the ability of HRS in the exploration of Li-rich pegmatite deposits remains to be explored. In this study, Li-rich pegmatite anomalies were directly extracted from ZY1-02D hyperspectral imagery in the Zhawulong (ZWL) area of western Sichuan, China, using target detection techniques including Adaptive Cosine Estimator (ACE), Constrained Energy Minimization (CEM), Spectral Angle Mapper (SAM), and SAM with BandMax (SAMBM). Further, the Li-rich anomalies were superimposed with the distribution of pegmatite dykes delineated based on GF-2 high-resolution imagery. Our final results accurately identified the known range of Spd pegmatite dykes and further predicted two new exploration target areas. The approaches used in this study could be easily extended to other potential mineralization areas to discover new rare metal pegmatite deposits on the Tibetan Plateau.
Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.
Turn a blind eye? E-tailer's blockchain participation considering upstream competition between copycats and brands

E-tailers such as Amazon and JD.com have long been criticized for the co-existence of copycat and genuine products sold on their platforms. Although many brands have developed blockchain-based digital manufacturing systems which can enable the traceability of genuine products, consumers could not identify the genuine products without the e-tailers' participation in the blockchain: A well-behaved blockchain requires the digital connection of the upstream (i.e., the brand) and the downstream (i.e., the e-tailer). We hence build a game-theoretical model to analyze the e-tailers' incentives to participate in the upstream brand's blockchain, which guarantees the consumers' trust but the e-tailer's revenue shared from the copycat sales might be reduced. The e-tailers operate under a hybrid mode where the copycat products are sold in agency stores and the genuine products are sold in the e-tailers' reselling stores. We find that the brand's efficient image investment helps incentivize the e-tailer to connect to the upstream brand's blockchain-based manufacturing system. And the e-tailer's blockchain participation induces the genuine product's larger market share by enhancing its diversified competition with the copycat. Otherwise, the e-tailer still tends to participate in the brand's blockchain if its commission rate from the copycat store is low and the e-tailer's reselling channel advantage is limited.
Understanding and improving adversarial transferability of vision transformers and convolutional neural networks

Convolutional neural networks (CNNs) and visual transformers (ViTs) are both known to be vulnerable to adversarial examples. Recent work has illustrated the existence of transferability between the two, but the experimental performance is generally mediocre. To enhance the transferability of adversarial examples between CNNs and ViTs, we propose a novel attack on the phenomenon that CNNs and ViTs differ significantly in their inductive bias, which not only attacks the same inductive bias between the two classes of models, but also suppresses the unique of ViTs. We evaluate the effectiveness of our approach through extensive experiments on state-of-the-art ViTs, CNNs, and robustly trained CNNs, and demonstrate significant improvements in transferability, both between ViTs and from ViTs to CNNs. The code for our project is available at https://github.com/chenxiaoyupetter/inductive-biase-attack.
Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection

Current methodologies for insulator defect detection are hindered by limitations in real-world applicability, spatial constraints, high computational demand, and segmentation challenges. Addressing these shortcomings, this paper presents a robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface. Firstly, the original backbone network is replaced by the transformer cross-stage partial (Transformer-CSP) networks to enrich the network’s ability by capturing information across different depths of network feature maps. Secondly, an insulator defect segmentation head network is presented to handle the segmentation of defect areas such as insulator losses and flashovers. It facilitates instance-level mask prediction for each insulator object, significantly reducing the influence of intricate backgrounds. Finally, comparative experiment results show that the positioning accuracy and defect segmentation accuracy of the proposed both surpass that of other popular models. It can be concluded that the proposed model not only satisfies the requirements for balance between accuracy and speed in power facility inspection, but also provides fresh perspectives for research in other defect detection domains.