Reviewerly/exHarmony huggingface dataset:
Efficient publication of distributed and overlapping graph data under differential privacy

Graph data publication has been considered as an important step for data analysis and mining. Graph data, which provide knowledge on interactions among entities, can be locally generated and held by distributed data owners. These data are usually sensitive and private, because they may be related to owners' personal activities and can be hijacked by adversaries to conduct inference attacks. Current solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed manners. Therefore, this work proposes a novel framework for distributed graph publication. In this framework, differential privacy is applied to justify the safety of the published contents. It includes four phases, i.e., graph combination, plan construction sharing, data perturbation, and graph reconstruction. The published graph selection is guided by one data coordinator, and each graph is perturbed carefully with the Laplace mechanism. The problem of graph selection is formulated and proven to be NP-complete. Then, a heuristic algorithm is proposed for selection. The correctness of the combined graph and the differential privacy on all edges are analyzed. This study also discusses a scenario without a data coordinator and proposes some insights into graph publication.
Enhanced Tunicate Swarm Algorithm for Solving Large-Scale Nonlinear Optimization Problems

Abstract Nowadays optimization problems become difficult and complex, traditional methods become inefficient to reach global optimal solutions. Meanwhile, a huge number of meta-heuristic algorithms have been suggested to overcome the shortcomings of traditional methods. Tunicate Swarm Algorithm (TSA) is a new biologically inspired meta-heuristic optimization algorithm which mimics jet propulsion and swarm intelligence during the searching for a food source. In this paper, we suggested an enhancement to TSA, named Enhanced Tunicate Swarm Algorithm (ETSA), based on a novel searching strategy to improve the exploration and exploitation abilities. The proposed ETSA is applied to 20 unimodal, multimodal and fixed dimensional benchmark test functions and compared with other algorithms. The statistical measures, error analysis and the Wilcoxon test have affirmed the robustness and effectiveness of the ETSA. Furthermore, the scalability of the ETSA is confirmed using high dimensions and results exhibited that the ETSA is least affected by increasing the dimensions. Additionally, the CPU time of the proposed algorithms are obtained, the ETSA provides less CPU time than the others for most functions. Finally, the proposed algorithm is applied at one of the important electrical applications, Economic Dispatch Problem, and the results affirmed its applicability to deal with practical optimization tasks.
Histogram Layers for Texture Analysis

An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local, spatial regions. We present a localized histogram layer for artificial neural networks. Instead of computing global histograms as done previously, the proposed histogram layer directly computes the local, spatial distribution of features for texture analysis and parameters for the layer are estimated during backpropagation. We compare our method with state-of-the-art texture encoding methods such as the Deep Encoding Network Pooling, Deep Texture Encoding Network, Fisher Vector convolutional neural network, and Multi-level Texture Encoding and Representation on three material/texture datasets: (1) the Describable Texture Dataset; (2) an extension of the ground terrain in outdoor scenes; (3) and a subset of the Materials in Context dataset. Results indicate that the inclusion of the proposed histogram layer improves performance. The source code for the histogram layer is publicly available: https://github.com/GatorSense/Histogram_Layer.
Computational complexity continuum within Ising formulation of NP problems

Abstract A promising approach to achieve computational supremacy over the classical von Neumann architecture explores classical and quantum hardware as Ising machines. The minimisation of the Ising Hamiltonian is known to be NP-hard problem yet not all problem instances are equivalently hard to optimise. Given that the operational principles of Ising machines are suited to the structure of some problems but not others, we propose to identify computationally simple instances with an ‘optimisation simplicity criterion’. Neuromorphic architectures based on optical, photonic, and electronic systems can naturally operate to optimise instances satisfying this criterion, which are therefore often chosen to illustrate the computational advantages of new Ising machines. As an example, we show that the Ising model on the Möbius ladder graph is ‘easy’ for Ising machines. By rewiring the Möbius ladder graph to random 3-regular graphs, we probe an intermediate computational complexity between P and NP-hard classes with several numerical methods. Significant fractions of polynomially simple instances are further found for a wide range of small size models from spin glasses to maximum cut problems. A compelling approach for distinguishing easy and hard instances within the same NP-hard class of problems can be a starting point in developing a standardised procedure for the performance evaluation of emerging physical simulators and physics-inspired algorithms.
FedE: Embedding Knowledge Graphs in Federated Setting

Knowledge graphs (KGs) become widespread and many organizations construct as well as maintain their own knowledge graphs. Same as the data isolation which has been a long-standing problem, knowledge graph isolation is common in real knowledge graph applications. Since the incompleteness of knowledge graphs obtained by different owners, they need to take advantage of other knowledge graphs to complete their own knowledge graphs, without exposing knowledge graphs explicitly since the consideration of data privacy, commercial interests and so on. Knowledge graph embedding (KGE) methods represent components of a knowledge graph as vectors in continuous vector spaces (i.e., embeddings) and proved to be effective in conducting knowledge graph completion. However, current knowledge graph embedding methods focus on the scenario with only a single knowledge graph. To solve this problem, we introduce the federated setting for knowledge graphs and apply it in knowledge graph embedding. We propose a Federated Knowledge Graph Embedding framework, FedE, focusing on learning knowledge graph embeddings by aggregating locally-computed updates. In this framework, there is a client for each knowledge graph and a server for coordinating embedding aggregation. Specifically, entity embeddings are locally learned in clients and the server is responsible for aggregating entity embeddings from clients. Furthermore, a model fusion procedure blends the capability of learned embeddings based only on one client without using the federated setting and embeddings based on all the clients in the federated setting. Finally, we conduct extensive experiments on datasets derived from KGE benchmark datasets, and results show the effectiveness of our proposed FedE.
Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection

The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart enterprise as for any system largely dependent on technology. To predict this kind of attack, one solution can be to use unsupervised machine learning approaches, as they guarantee the detection of anomalies regardless of their prior knowledge. It is also essential to identify the anomalous and unknown behaviors that occur within a network in near real-time. Three different approaches have been proposed and benchmarked in exactly the same condition: Deep Autoencoding with GMM and Isolation Forest, Deep Autoencoder with Isolation Forest, and Memory Augmented Deep Autoencoder with Isolation Forest. These approaches are thus the result of combining different unsupervised algorithms. The results show that the addition of the Isolation Forest improves the accuracy values and increases the inference time, although this increase does not represent a relevant problematic factor. This paper also explains the features that the various models consider most important for classifying an event as an attack using the explainable artificial intelligence methodology called Shapley Additive Explanations (SHAP). Experiments were conducted on KDD99, NSL-KDD, and CIC-IDS2017 datasets.
C-VTON: Context-Driven Image-Based Virtual Try-On Network

Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art.
Inverted Docking Station: A Conceptual Design for a Battery-Swapping Platform for Quadrotor UAVs

“Flight Time” and the “Scope of the mission” play major roles in using UAVs as they affect most industrial activities. Once the battery has depleted, the UAV has to land on the ground and human interaction is needed to change the battery with a fully charged one. Nowadays, several automatic battery swapping systems are catching interest in research. This research presents the novel concept of an Inverted Docking Station that allows a quadrotor UAV to attach to the ceiling during the automatic battery-swapping process. The proposed design consist of a docking station, a positioning system and gripper mechanisms. The proposed design allows the quadrotor to carry the load under the quadrotor and remain attached throughout the servicing period. A mathematical model and design guideline have been proposed, and a Finite Element Analysis (FEA) was performed to check that the developed platform is strong enough to withstand the above task. A ‘DJI TELLO’ small-scale quadrotor was chosen as a case study to demonstrate the proposed research. Finally the advantages and the limitations of the system are discussed.
Enrolling into exclusion: African blockchain and decolonial ambitions in an evolving finance/security infrastructure

There is growing debate over whether applications of blockchain and other financial technologies ('fintechs') reinforce forms of neo-colonial extraction that perpetuate North–South inequities or help enact decolonial ambitions across the Global South. This paper expands such discussions and contributes to this special issue on 'fintech in Africa' by situating emerging African blockchain techno-experimentation within wider international infrastructural relations. We argue that blockchain-based activities in and across the African continent must be understood within those also unfolding in countries that have been subjected to financial sanctions of varying types (China, Iran, Russia, Venezuela) by the European Union, United States, and United Nations. Our analysis traces how blockchain-based applications by sanctioned countries are extending exclusions in novel and existing socio-technical relations. We conclude that blockchain-based experiments are facilitating rather than displacing a colonial finance/security infrastructure.
STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation

Graph neural network-based recommendation systems are blossoming recently, and its core component is aggregation methods that determine neighbor embedding learning. Prior arts usually focus on how to aggregate information from the perspective of spatial structure information, but temporal information about neighbors is left insufficiently explored.
Artificial Intelligence for 6G Networks: Technology Advancement and Standardization

With the deployment of 5G networks, standards organizations have started working on the design phase for 6G networks. 6G networks will be immensely complex, requiring more deployment time, cost, and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges by providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical layer (PHY) and link layer in cellular networks, where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and a future timeline on the readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks.
Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms

Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, we used screening mammograms of 1366 women. For MO cancer detection, we used screening mammograms of 333 women (97 MO cancer) with dense breasts. We simulated the right mammogram for normal controls and the cancer side for MO cancer cases. We created two RCDT images, one from a real mammogram pair and another from a real-simulated mammogram pair. We finetuned a VGG16 on resulting RCDT images to classify the women with MO cancer. We compared the classification performance of the CNN trained on fused RCDT images, CNNFused to that of trained only on real RCDT images, CNNReal, and to that of trained only on simulated RCDT images, CNNSimulated. The test AUC for CNNFused was 0.77 with a 95% confidence interval (95CI) of [0.71, 0.83], which was statistically better (p-value < 0.02) than the CNNReal AUC of 0.70 with a 95CI of [0.64, 0.77] and CNNSimulated AUC of 0.68 with a 95CI of [0.62, 0.75]. It showed that CGAN simulated mammograms can help MO cancer detection.
Width &amp; Depth Pruning for Vision Transformers

Transformer models have demonstrated their promising potential and achieved excellent performance on a series of computer vision tasks. However, the huge computational cost of vision transformers hinders their deployment and application to edge devices. Recent works have proposed to ﬁnd and remove the unimportant units of vision transformers. Despite achieving remarkable results, these methods take one dimension of network width into consideration and ignore network depth, which is another important dimension for pruning vision transformers. Therefore, we propose a Width &amp; Depth Pruning (WDPruning) framework that reduces both width and depth dimensions simultaneously. Speciﬁcally, for width pruning, a set of learnable pruning-related parameters is used to adaptively adjust the width of transformer. For depth pruning, we introduce several shallow classiﬁers by using the intermediate information of the transformer blocks, which allows images to be classiﬁed by shallow classiﬁers instead of the deeper classiﬁers. In the inference period, all of the blocks after shallow classiﬁers can be dropped so they don’t bring additional parameters and computation. Experimental results on benchmark datasets demonstrate that the proposed method can signiﬁcantly reduce the computational costs of mainstream vision transformers such as DeiT and Swin Transformer with a minor accuracy drop. In particular, on ILSVRC-12, we achieve over 22% pruning ratio of FLOPs by compressing DeiT-Base, even with an increase of 0.14% Top-1 accuracy.
Efficient Image Encryption Based on New Substitution Box Using DNA Coding and Bent Function

This paper contributes to creating an unbreakable S-Box based on strong bent function expanded by DNA sequences and investigates and analyzes the strength of the proposed S-Box against major standard criteria and benchmarks such as interpolation attack, algebraic attack, avalanche effect, nonlinearity, and period.The outcome of the tests shows that the proposed S-box has good security, as well as it is passed all the randomness tests.On average, the results had after the tests applied have been come with SAC =0.50122, NL = 112, BIC =103.40625 and an iterative period with its maximum value = 256.The complexity of the proposed S-Box is increased with the getting algebraic expression of 255 terms which implies that the algebraic attack resistance = 2 160 .Based on the proposed S-Box, a candidate image enciphering scheme is also suggested to prove the strength of the S-Box.The analysis of the experiments that applied on two modes of images, gray images and RGB ones, supports the scheme's robustness against different differential and statical using standard criteria such as correlation coefficient analysis, information entropy, histogram analysis, unified average change intensity, number of pixels change rate and many others.This enforces the capability of using it in modern-day cryptosystems that utilized in multimedia data exchange.
Unsupervised concept drift detection for multi-label data streams

Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an unsupervised concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 15 datasets and a baseline classifier. The results show that LD3 provides between 16.9 and 56% better predictive performance than comparable detectors on both real-world and synthetic data streams.
Representation Learning in Multi-view Clustering: A Literature Review

Abstract Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.
Subpopulation-specific machine learning prognosis for underrepresented patients with double prioritized bias correction

Abstract Background Many clinical datasets are intrinsically imbalanced, dominated by overwhelming majority groups. Off-the-shelf machine learning models that optimize the prognosis of majority patient types (e.g., healthy class) may cause substantial errors on the minority prediction class (e.g., disease class) and demographic subgroups (e.g., Black or young patients). In the typical one-machine-learning-model-fits-all paradigm, racial and age disparities are likely to exist, but unreported. In addition, some widely used whole-population metrics give misleading results. Methods We design a double prioritized (DP) bias correction technique to mitigate representational biases in machine learning-based prognosis. Our method trains customized machine learning models for specific ethnicity or age groups, a substantial departure from the one-model-predicts-all convention. We compare with other sampling and reweighting techniques in mortality and cancer survivability prediction tasks. Results We first provide empirical evidence showing various prediction deficiencies in a typical machine learning setting without bias correction. For example, missed death cases are 3.14 times higher than missed survival cases for mortality prediction. Then, we show DP consistently boosts the minority class recall for underrepresented groups, by up to 38.0%. DP also reduces relative disparities across race and age groups, e.g., up to 88.0% better than the 8 existing sampling solutions in terms of the relative disparity of minority class recall. Cross-race and cross-age-group evaluation also suggests the need for subpopulation-specific machine learning models. Conclusions Biases exist in the widely accepted one-machine-learning-model-fits-all-population approach. We invent a bias correction method that produces specialized machine learning prognostication models for underrepresented racial and age groups. This technique may reduce potentially life-threatening prediction mistakes for minority populations.