Reviewerly/exHarmony huggingface dataset:
Routing, Channel, Key-Rate, and Time-Slot Assignment for QKD in Optical Networks

Quantum Key Distribution (QKD) is currently being explored as a solution to the threats posed to current cryptographic protocols by the evolution of quantum computers and algorithms.However, single-photon quantum signals used for QKD permit to achieve key rates strongly limited by link performance (e.g., loss and noise) and propagation distance, especially in multinode QKD networks, making it necessary to design a scheme to efficiently and timely distribute keys to the various nodes.In this work, we introduce the new problem of joint Routing, Channel, Key-rate and Time-slot Assignment (RCKTA), which is addressed with four different network settings, i.e., allowing or not the use of optical bypass (OB) and trusted relay (TR).We first prove the NP-hardness of the RCKTA problem for all network settings and formulate it using a Mixed Integer Linear Programming (MILP) model that combines both quantum channels and quantum key pool (QKP) to provide an optimized solution in terms of number of accepted key rate requests and key storing rate.To deal with problem complexity, we also propose a heuristic algorithm based on an auxiliary graph, and show that it is able to obtain nearoptimal solutions in polynomial time.Results show that allowing OB and TR achieves an acceptance ratio of 39% and 14% higher than that of OB and TR, respectively.Remarkably, these acceptance ratios are obtained with up to 46% less QKD modules (transceivers) compared to TR and only few (less than 1 per path) additional QKD modules than OB.
Generative adversarial nets for unsupervised outlier detection

Outlier detection, also known as anomaly detection, has been a persistent and active research area for decades due to its wide range of applications in various fields. Many well-established methods have difficulty fitting the distribution of high-dimensional and complex data, making it difficult to detect outliers that have a low degree of deviation. To address this problem, we combine the distribution fitting capability of generative adversarial nets (GANs) with the specificity of the outlier detection problem and propose a GAN-based unsupervised outlier detection (GUOD) method. In a real dataset mixed with normal objects and outliers, the generator of GANs prefers to fit the distribution of the majority of normal objects to minimize the error; as a result, the generated fake data can be used as an augmentation of normal objects. Next, fake "normal objects" are used to train the autoencoder. Finally, the real data are fed into the autoencoder for one forward propagation, and the reconstruction error of the object is used as its own outlier factor. The top-n objects with the largest reconstruction errors are considered outliers. Extensive experiments are conducted on eight real-world datasets, and the results show that the GUOD method performs better than ten other state-of-the-art algorithms.
A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.
Large Separable Kernel Attention: Rethinking the Large Kernel Attention design in CNN

Visual Attention Networks (VAN) with Large Kernel Attention (LKA) modules have been shown to provide remarkable performance, that surpasses Vision Transformers (ViTs), on a range of vision-based tasks. However, the depth-wise convolutional layer in these LKA modules incurs a quadratic increase in the computational and memory footprints with increasing convolutional kernel size. To mitigate these problems and to enable the use of extremely large convolutional kernels in the attention modules of VAN, we propose a family of Large Separable Kernel Attention modules, termed LSKA. LSKA decomposes the 2D convolutional kernel of the depth-wise convolutional layer into cascaded horizontal and vertical 1-D kernels. In contrast to the standard LKA design, the proposed decomposition enables the direct use of the depth-wise convolutional layer with large kernels in the attention module, without requiring any extra blocks. We demonstrate that the proposed LSKA module in VAN can achieve comparable performance with the standard LKA module and incur lower computational complexity and memory footprints. We also find that the proposed LSKA design biases the VAN more toward the shape of the object than the texture with increasing kernel size. Additionally, we benchmark the robustness of the LKA and LSKA in VAN, ViTs, and the recent ConvNeXt on the five corrupted versions of the ImageNet dataset that are largely unexplored in the previous works. Our extensive experimental results show that the proposed LSKA module in VAN provides a significant reduction in computational complexity and memory footprints with increasing kernel size while outperforming ViTs, ConvNeXt, and providing similar performance compared to the LKA module in VAN on object recognition, object detection, semantic segmentation, and robustness tests.
Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison

The advent of whole slide imaging has brought advanced computer-aided diagnosis via medical imaging and artificial intelligence technologies in digital pathology. The examination of tissue samples through whole slide imaging is commonly used to diagnose cancerous diseases, but the analysis of histopathology images through a decision support system is not always accurate due to variations in color caused by different scanning equipment, staining methods, and tissue reactivity. These variabilities decrease the accuracy of computer-aided diagnosis and affect the diagnosis of pathologists. In this context, an effective stain normalization method has proved as a powerful tool to standardize different color appearances and minimize color variations in histopathology images. This study reviews different stain normalization methods highlighting the main methodologies, contributions, advantages, and limitations of correlated works. The state-of-the-art methods are grouped into four distinct categories. Next, we select ten representative methods from the groups and conduct an experimental comparison to investigate the strengths and weaknesses of different methods and rank them according to selected performance accuracy measures. The quality performances of selected methods are compared in terms of quaternion structure similarity index metric, structural similarity index metric, and pearson correlation coefficient conducting experiments on three histopathology datasets. Our findings conclude that the structure-preserving unified transformation-based methods consistently outperform the state-of-the-art methods by improving robustness against variability and reproducibility. The comparative analysis we conducted in this paper will serve as the basis for future research, which will help to refine existing techniques and develop new approaches to address the complexities of stain normalization in complex histopathology images.
Attribute-weighted outlier detection for mixed data based on parallel mutual information

Outlier detection plays an important role in data mining because it can improve the performance of data analysis. Most outlier detection algorithms focus on numerical or categorical attributes; however, data typically have a mixture of numerical and categorical attributes. We addressed this problem by developing an attribute-weighted outlier detection algorithm, PMIOD, for high-dimensional and massive mixed data. The PMIOD algorithm adopts mutual information to measure attribute correlations and provides an attribute-weighting method for mixed data. Based on this, an attribute-weighted outlier detection method for mixed data was developed. Moreover, to improve the efficiency of mutual information computing for high-dimensional mixed data, the mutual information computing was parallelized on the Spark platform. We evaluated the proposed algorithm using ten UCI datasets and four synthetic datasets in comparison with widely used algorithms. Experiments were conducted to demonstrate the superiority of the results produced by the proposed algorithm.
Towards self-explainable graph convolutional neural network with frequency adaptive inception

Graph convolutional neural networks (GCNs) have demonstrated powerful representing ability of irregular data, e.g., skeletal data and graph-structured data, providing the effective mechanism to fuse the neighbor nodes. However, inheriting from the deep learning, GCN also lacks interpretability, which hinders its application to scenarios that have high demand for transparency. Although, there have been many efforts on the interpretability of deep learning, they mainly concentrate on i.i.d data that is hard to be deployed to GCNs, which involve not only the node feature, but also the graph structure. There are few works that attempt to explain it with post-hoc manner, which can be biased, resulting in mis-representation of the true explanation. Therefore, in this paper, we propose a framework, namely ExpFiGCN, that reveals explainability of the GCNs from the perspective of graph structure and mathematical analysis. Specifically, ExpFiGCN can find the most intrinsically relevant node to the central node and obtain the informative and discriminative signals while performing denoising. For the graph structure, we find K-nearest nodes; for the mathematical analysis, every channel of a node and its neighborhoods contribute dynamically to the final channel signal, which can capture the inherent difference of different channels and neighbor nodes. Meanwhile, it can enhance the representation ability of nodes and ameliorate the over-smoothing problem. On the other hand, our model can dynamically adjust the importance of neighborhoods to the central vertex. We empirically validate the effectiveness of the proposed framework ExpFiGCN on various benchmark datasets. Experimental results show that our method achieves substantial improvements and outperforms the state-of-the-art performance strikingly.
Contrastive sentence representation learning with adaptive false negative cancellation

Contrastive sentence representation learning has made great progress thanks to a range of text augmentation strategies and hard negative sampling techniques. However, most studies directly employ in-batch samples as negative samples, ignoring the semantic relationship between negative samples and anchors, which may lead to negative sampling bias. To address this issue, we propose similarity and relative-similarity strategies for identifying potential false negatives. Moreover, we introduce adaptive false negative elimination and attraction methods to mitigate their adverse effects. Our proposed approaches can also be considered semi-supervised contrastive learning, as the identified false negatives can be viewed as either negative or positive samples for contrastive learning in adaptive false negative elimination and attraction methods. By fusing information from positive and negative pairs, contrastive learning learns rich and discriminative representations that capture the intrinsic characteristics of the sentence. Experimental results indicate that our proposed strategies and methods can bring further significant performance improvements. Specifically, the combination of similarity strategy and adaptive false negative elimination method achieves the best results, yielding an average performance gain of 2.1% compared to SimCSE in semantic textual similarity (STS) tasks. Furthermore, our approach is generalizable and can be applied to different text data augmentation strategies and certain existing contrastive sentence representation learning models. Our code and data are publicly available at the link: https://github.com/Linda230/AFNC.
Mapping and discrimination of the mineralization potential in granitoids from Banyo area (Adamawa, Cameroon), using Landsat 9 OLI, ASTER images and field observations

The Banyo area is situated in the west Cameroon domain of the Central African Fold Belt. In this study, field data and remote sensing conventional mapping techniques including RGB colour combination (red-green-blue), principal component analysis (PCA), Crosta-PCA, band ratios (BR), constrained energy minimization (CEM), minimum noise fraction (MNF), spectral angle mapper (SAM) classification and linear spectral unmixing (LSU) algorithms were used to process Landsat 9 OLI and ASTER data in order to discriminate various rock units such as biotite-amphibole granites, hornblende-biotite orthogneiss and map associated mineral deposits (e.g., iron-oxide, hydroxyl and corundum minerals) in Banyo area. In addition, it was found that RGB combination (743) of OLI bands, PCA of OLI bands (PC123), BR of OLI bands (4/2, 6/7, 6/5), BR of ASTER (4/2, 7/6, 8/9), Crosta-PCA of OLI bands, MNF (123) of OLI bands, SAM, and LSU techniques applied to ASTER bands ascertain potential locations of granitoids and gneisses bearing iron and hydroxyl minerals in several areas of Banyo. On the other hand, this study also revealed that corundum deposits are detected by applying the CEM method to OLI band ratio 6/7 highlighting red tones and processing SAM classification algorithm both using laboratory reference spectra. Automatic (using PCI Geomatica line tool) and manual extractions of lineaments revealed three major trends (N-S, E-W, and WNW-ESE to NW-SE) and an accessory trend (NE-SW to ENE-WSW) that surround hydrothermally altered rocks recognized as containing iron oxide, hydroxyl, and corundum mineral deposits. In the field, these lineaments may group to the tree deformation phases. The D1 phase (NW-SE); the D2 phase (NE-SW to ENE-WSW) and the D3 phase (N-S). MLC and SAM classification methods showed to be more effective in discriminating rocks bearing Iron-oxide, hydroxyl and corundum minerals in Banyo area. This result permits to confirm that, the combination of remote sensing (Landsat 9 OLI and ASTER data in this case) and field data constitute a useful tool for mineral exploration in the shear zone. However, the accuracy assessment was relied on comparing the different maps produced from Landsat 9 OLI and ASTER data with the ground truth sample points and also, with the earliest geological map of Banyo area. Thus, quantitatively the results were accurate at 99%.
A framework for cyber-risk insurance against ransomware: A mixed-method approach

Hackers follow a standard cyber kill chain process and install ransomware payloads using phishing emails on firms belonging to critical industry, resulting in huge losses in revenue, reputation, and customer churn. This study uses a mixed-method explanatory approach to mitigate ransomware attacks. We present the quantitative Ransomware Risk Management Model (R2M2) based on protection motivation theory (PMT). The Ransomware Risk Assessment module based on the threat appraisal component of PMT and the National Institute of Standards and Technology (NIST) guidelines can help the chief information security officer (CISO) to assess the risk using predictive analytics techniques. The Ransomware Risk Quantification module uses collective risk modeling to compute the severity of a ransomware attack on an organization. The Ransomware Risk Mitigation module helps the CISO to minimize the probability and impact of a ransomware attack on their organization by (i) investing in perimeter security technologies and training to ensure prepare, deter, detect, restrain, recover, and review mitigation strategies, (ii) adopting the National Institue of Standards and Technology (NIST) Cybersecurity Framework to ensure business resilience, and (iii) mitigating the residual risk by investing in cyber-risk insurance. We validated the proposed method using a qualitative study by interviewing participants form firms affected by ransomware, managed security service providers, security consultants, and cyber-risk insurers.
Effective image tampering localization with multi-scale ConvNeXt feature fusion

With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low generalization performance and robustness. In this letter, we propose an effective image tampering localization scheme based on ConvNeXt encoder and multi-scale Feature Fusion (ConvNeXtFF). Stacked ConvNeXt blocks are utilized as an encoder to capture hierarchical multi-scale features, which are then fused in decoder for locating tampered pixels accurately. Combined loss function and effective data augmentation strategies are adopted to further improve the model performance. Extensive experimental results show that both localization accuracy and robustness of the ConvNeXtFF scheme outperform other state-of-the-art ones. The source code is available at https://github.com/multimediaFor/ConvNeXtFF.
Hello me, meet the real me: Voice synthesis attacks on voice assistants

The radical advances in telecommunications and computer science have enabled a myriad of applications and novel seamless interactions with computing interfaces. Voice Assistants (VAs) have become the norm for smartphones, and millions of VAs incorporated in smart devices are used to control these devices in the smart home context. Previous research has shown that they are prone to attacks, leading vendors to implement countermeasures. One of these measures is to allow only a specific individual, the device's owner, to perform potentially dangerous tasks that may disclose personal information, involve monetary transactions, etc. To understand the extent to which VAs provide the necessary protection to their users, we experimented with two of the most widely used VAs, which the participants trained. We then utilised voice synthesis, using samples provided by participants, to synthesise commands that were used to trigger the corresponding VA and perform a dangerous task. Our extensive results showed that more than 30% of our audio synthesis attacks were successful and at least one successful attack for more than half of the participants. Moreover, they illustrate statistically significant variation among vendors and, in one case, even gender bias. The outcomes are rather alarming and require the deployment of further countermeasures to prevent exploitation, as the number of VAs in use is currently comparable to the world population.
Chatbot ads with a human touch: A test of anthropomorphism, interactivity, and narrativity

Powered by artificial intelligence (AI), chatbots are increasingly capable of simulating human-like conversations. But, is this desirable for strategic communications? Will chatbots be more persuasive if they are more human-like, not only in their appearance but also in their interaction and delivery of advertising content? We explored these questions with a 2 (chatbot profile: human-like vs. machine-like) x 2 (message interactivity: high vs. low) x 2 (ad type: narrative vs. factual) experiment (N = 414). Data reveal that high message interactivity fosters positive attitudes toward the chatbot and the ad by mitigating violated expectancy. Narrative ads promote chatbot advertising through perceived transportation. A three-way interaction revealed that when a chatbot is machine-like in appearance, higher interactivity and adoption of a narrative style of delivery serve to increase ad persuasiveness by heightening social presence. Theoretical and practical implications for chatbot advertising are discussed.
A noise-resistant graph neural network by semi-supervised contrastive learning

Graph neural networks (GNNs) have been widely applied for representation learning on the graph data in real applications, but few of them are designed to conduct representation learning on the graph data with noisy labels. Its key challenge is that the feature embeddings of nodes with noisy labels (noisy nodes for short) are close to those of unlabeled nodes so that the classifier constructed by GNNs is influenced by noisy nodes. To address this issue, in this paper, we propose a noise-resistant graph neural network with semi-supervised contrastive learning to push noisy nodes far away from unlabeled nodes in the embedding space. To do this, we design a constraint of semi-supervised contrastive learning and put it into the objective function of GNNs. Specifically, the proposed constraint enlarges the distance between noisy nodes and unlabeled nodes by pushing noisy nodes far away from their unlabeled neighbors in the embedding space. As a result, the embeddings of unlabeled nodes are influenced by noisy label less. Moreover, we intuitively analyze the feasibility of our proposed constraint. Comprehensive experiments on real datasets further verify the effectiveness of our proposed method over previous SOTA methods in terms of classification tasks with different ratio levels of noisy labels.
Triple Sequence Learning for Cross-domain Recommendation

Cross-domain recommendation (CDR) aims at leveraging the correlation of users’ behaviors in both the source and target domains to improve the user preference modeling in the target domain. Conventional CDR methods typically explore the dual-relations between the source and target domains’ behaviors. However, this may ignore the informative mixed behaviors that naturally reflect the user’s global preference. To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR. Specifically, Tri-CDR independently models the hidden representations for the triple behavior sequences and proposes a triple cross-domain attention (TCA) method to emphasize the informative knowledge related to both user’s global and target-domain preference. To comprehensively explore the cross-domain correlations, we design a triple contrastive learning (TCL) strategy that simultaneously considers the coarse-grained similarities and fine-grained distinctions among the triple sequences, ensuring the alignment while preserving information diversity in multi-domain. We conduct extensive experiments and analyses on six cross-domain settings. The significant improvements of Tri-CDR with different sequential encoders verify its effectiveness and universality. The source code is available at https://github.com/hulkima/Tri-CDR .