Reviewerly/exHarmony huggingface dataset:
Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.
Semantically consistent multi-view representation learning

In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL.
A hybrid enhanced attention transformer network for medical ultrasound image segmentation

Ultrasound (US) technology is a widely utilized for clinical screening owing to its cost-effectiveness, painlessness, and convenience. However, the automatic segmentation of lesions or organs in US images remains to be challenging due to speckle artifacts, blurred boundaries, and low contrast. Recently, transformer-based methods have shown to be effective in long-range dependency, which is a good complement to Convolutional Neural Networks (CNN). In this article, we present a novel U-shape segmentation model based on a hybrid CNN-transformer structure that can effectively integrate CNN local features and transformer long-range contextual information of US images. To begin with, we design a coordinate residual block (CdRB) to encode the absolute position information of lesions. Further, we develop a channel enhanced self-attention-based transformer (ECAT) to help enhance the response of extracted global features. Finally, we adopt a comprehensive dual attention module (CDAM) to enhance skip connection features, which can learn feature correlations and capture more accurate edge features in US images. Results based on four public US datasets demonstrate that our method outperforms state-of-the-art segmentation methods, with 0.741 Dice on BUSI for breast lesion segmentation, 0.827 Dice on DDTI for thyroid lesion segmentation, 0.895 Dice on TN3k for thyroid lesion segmentation and 0.940 Dice on CAMUS for left ventricle segmentation. Furthermore, the robustness of our network is further demonstrated by an external validation dataset for breast lesion segmentation. In summary, our method showcases excellent adaptability and robustness in US image segmentation and can potentially be a general US segmentation tool.
Forecasting Cryptocurrency Prices Using a Gated Recurrent Unit Neural Network

This paper investigates the potential of using a gated recurrent unit (GRU) neural network (NN) for forecasting the prices of three popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). A dataset spanning from October 2021 to October 2022 was collected and used to train and evaluate the performance of the proposed model. The proposed GRU model was evaluated using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE) as evaluation metrics. The results of the study show that the GRU model achieved an RMSE of 366.0601 and a MAPE of 1.7268% for BTC, an RMSE of 37.6678 and a MAPE of 2.3342% for ETH, and an RMSE of 1.0902 and a MAPE of 1.7278% for LTC. The results indicate that the GRU model performed well in forecasting cryptocurrency prices and holds promise as an approach for further research in this field.
Generative Adversarial Networks (GANs) Video Framework: A Systematic Literature Review

The content creation industry is rapidly growing in various fields such as entertainment, education, and social media platforms. There has been an increasing trend in recent years to generate content using AI algorithms. Generative Adversarial Networks (GANs) are a powerful method for generating realistic samples to meet the increasing demand for data. Many variations of GANs models have been proposed and are covered in multiple review papers. This paper presents a systematic literature review of GANs video generation models. First, the models are categorized into general GANs, image GANs, Video GANs, and Unconditional and Conditional GANs. Next, the paper summarizes the improvements made in GANs related to image synthesis and identifies areas where video synthesis has not yet been fully explored. A comprehensive systematic review of Video GANs is then presented by categorizing them into unconditional and conditional GANs. The datasets used in video generation are also discussed in the paper. The conditional models are further explained in sections that are categorized as images, audio, and videos. Lastly, the paper concludes with a discussion of the limitations of GANs and the future work needed in this area.
Hierarchical recursive Levenberg–Marquardt algorithm for radial basis function autoregressive models

The recursive Levenberg–Marquardt (L–M) algorithm has been generally implemented for the parameter identification of nonlinear models in online situations. However, owing to the intrinsic structural characteristics (i.e., the global nonlinear local linearity) of the radial basis function autoregressive (RBF-AR) model, the direct application of the recursive L–M (R-LM) algorithm significantly restricts its performance. To improve the comprehensive performance, a hierarchical recursive L–M (H-R-LM) algorithm is proposed in this paper. The concepts of parameter separation and model decomposition are integrated into the algorithm to reduce the model's complexity. Multi-innovation and forgetting factors leverage historical data more reasonably, thus accelerating the convergence. Hierarchical recognition and alternating optimization can improve identification accuracy. Two experiments on analog and actual data sets are used to compare the performance of the RBF-AR model using R-LM and H-R-LM algorithms. Compared with the R-LM algorithm, experiments verify that the new H-R-LM algorithm presents higher identification accuracy, faster convergence rate, improved predictive performance, and less computational effort.
Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques

The rapid proliferation of cyberthreats necessitates a robust understanding of their evolution and associated tactics, as found in this study. A longitudinal analysis of these threats was conducted, utilizing a six-year data set obtained from a deception network, which emphasized its significance in the study’s primary aim: the exhaustive exploration of the tactics and strategies utilized by cybercriminals and how these tactics and techniques evolved in sophistication and target specificity over time. Different cyberattack instances were dissected and interpreted, with the patterns behind target selection shown. The focus was on unveiling patterns behind target selection and highlighting recurring techniques and emerging trends. The study’s methodological design incorporated data preprocessing, exploratory data analysis, clustering and anomaly detection, temporal analysis, and cross-referencing. The validation process underscored the reliability and robustness of the findings, providing evidence of increasingly sophisticated, targeted cyberattacks. The work identified three distinct network traffic behavior clusters and temporal attack patterns. A validated scoring mechanism provided a benchmark for network anomalies, applicable for predictive analysis and facilitating comparative study of network behaviors. This benchmarking aids organizations in proactively identifying and responding to potential threats. The study significantly contributed to the cybersecurity discourse, offering insights that could guide the development of more effective defense strategies. The need for further investigation into the nature of detected anomalies was acknowledged, advocating for continuous research and proactive defense strategies in the face of the constantly evolving landscape of cyberthreats.
Trust-aware Cloudlet Federation Model for Secure Service Selection

The introduction of Edge computing has pushed the horizon to the edge of the network and the user’s proximity. The openness of data on the edge raised many issues about the security of data. Although edge computing is an extension of the cloud, besides this, its solutions to handle security threats are different from cloud computing because of its architectural dissimilarities. There is a need for a comprehensive mechanism to ensure the choice of secure service. For this purpose, the combination of trust would be a better approach to deal with the service selection tasks in edge computing. In this paper, a trust-aware cloudlet federation model has been proposed which ultimately helps in the safe service selection of an edge node. Before using a service ‘A’ of any edge node, the trust value of the edge node for service ‘A’ should be calculated, called Service Trust Value (STV). Then the edge nodes with a higher STV for service ‘A’ are filtered. Lastly, the model will select the edge node with a satisfactory value of trust for service ‘A’. This proposed model will be beneficial in secure service selection.
Computation Placement Orchestrator for Mobile-Edge Computing in Heterogeneous Vehicular Networks

The vision of Heterogeneous Vehicle Networks (HetVNET) embraces various highly dynamic scenarios with urgent requirements for delay-sensitive and reliability-guaranteed computation placement. Incorporating Mobile Edge Computing (MEC) technology into computation placement has a significant potential to reduce computational delay and enhance communication reliability. However, vehicle mobility and resource constraints make the multi-vehicle scramble for communication and computational resources challenging. This paper intends to investigate collaborative computing by comprehensively considering vehicle mobility, channel condition, and computational resources with two goals: High-Reliability Transmission (HRT) and Computational Delay Minimization (CDM). Specifically, we develop a hybird MEC-enabled computation placement orchestrator for HetVNET, where the HRT and CDM are formulated as mixed-integer programming and non-convex optimization problems, respectively. To ensure high-reliability communication, we leverage the conditional value at risk theory to tackle the non-smooth HRT problem. To solve the CDM problem, we transform it into two sub-problems: resource allocation and task offloading problems, aiming at reducing computational delay and improving resource utilization. Furthermore, we construct an iterative optimization algorithm to capture the optimal computation placement scheme in closed form for the HRT and CDM problems. Performance evaluations show that the proposed methods can significantly improve communication reliability and reduce computational delay.
Overcoming Language Priors via Shuffling Language Bias for Robust Visual Question Answering

Recent research has revealed the notorious language prior problem in visual question answering (VQA) tasks based on visual-textual interaction, which indicates that well-developed VQA models rely on learning shortcuts from questions without fully considering visual evidence.To tackle this problem, most existing methods focus on decreasing the incentive to learn prior knowledge by adding a question-only branch and becoming complacent by mechanically improving accuracy.However, these methods overcorrect positive biases useful for generalization, leading to the degradation of performance on the VQA v2 dataset when cumulating their methods into other VQA architecture.In this paper, we propose a robust shuffling language bias (SLB) approach to explicitly balance the prediction distribution, hopefully alleviating the language prior by increasing training opportunities for VQA models.Experiment results demonstrate that our method is cumulative with data augmentation and large-scale pre-training VQA architectures and achieves competitive performance on both the in-domain benchmark VQA v2 and out-of-distribution benchmark VQA-CP v2.
Optimizing Logging and Monitoring in Heterogeneous Cloud Environments for IoT and Edge Applications

As data is becoming more and more important, Internet of Things (IoT) devices are widely used to collect information and process data from various industries such as finance, autonomous driving, and smart factories. To address the limited computational power of IoT devices in processing real-time data, both edge computing, which utilizes nearby computers with greater computation capabilities, and cloud computing with even more processing power, are widely adopted solutions. As these systems have heterogeneous software and hardware configurations, it can be challenging to understand the behavior of the application from the perspective of different resources. In this paper, we propose an efficient logging and monitoring system in large-scale, heterogeneous environments for IoT and edge applications. To do this, our scheme first collects system resource usage data from each compute node using the operating system’s native system analysis tool. Then, it consolidates the system resource usage information from multiple nodes into an integrated database which creates a comprehensive view of the system. Finally, our scheme provides global system resource information in terms of specific jobs and nodes, providing a comprehensive understanding of complex heterogeneous hardware/software stacks. Our evaluation, using IoT and Edge workloads in heterogeneous systems, demonstrates the efficiency of logging and monitoring schemes. The average network usage for Windows and Linux is 0.12KB and 1.29KB per second, respectively, resulting in minimal network overhead. In addition, the proposed scheme shows negligible overhead in terms of both runtime (up to 0.73%) and storage (0.0474%).
GA Approach to Optimize Training Client Set in Federated Learning

Federated learning, where the distribution of distributed data is unknown, is more difficult and costly to train a central model with than traditional machine learning. In this study, we propose Federated Learning with Genetic Algorithm, which enables faster central model training at lower cost by providing an appropriate client selection method. A client can have its own communication cost depending on its data sharing preference, and based on this cost and the result of the client’s local update, we can select the appropriate combination of clients each round with a genetic algorithm. In each round, the client’s combinations are evaluated anew, which are continually explored. To evaluate the algorithm, we distributed the image dataset and communication costs in two ways and conducted federated learning for the image classification model. Experiments showed that the proposed algorithm can find a more efficient client combination and accelerate the training of federated learning.
Grayscale Image Enhancement Using Water Cycle Algorithm

Recent developments in engineering and computer sciences have heightened the need for digital image enhancement. Most of the previously reported works, however, focused on image enhancement using classical methods like mathematical transformations and spatial and frequency-domain methods. Hence, recently, there has been an increasing interest in using nature-inspired optimization techniques for image processing purposes. The water cycle algorithm (WCA) is one of the nature-inspired algorithms (NIAs) that have gotten much attention in optimizing real-world engineering problems due to its appealing performance. However, to the best of the author’s knowledge, little research has been undertaken on the WCA’s image-enhancing capacity. Thus, this work is intended to offer a modified histogram equalization (HE) approach using WCA to enhance the contrast of an image and maintain its brightness. Besides, the proposed WCA-based image enhancement technique was compared to linear contrast stretching (LCS), HE and its modified versions, particle swarm optimization (PSO), and accelerated particle swarm optimization (APSO). In addition to the objective function fitness, 11 full reference (FR) image quality assessment (IQA) metrics were employed to evaluate image quality and compare performance. Experimental results showed that the suggested image enhancement technique exhibited better performance than others in enhancing dark grayscale images in terms of objective function fitness and perceptual visual IQA metrics like multi-scale structural similarity (MS-SSIM), information-weighted structural similarity (IW-SSIM), information-weighted mean squared error (IW-MSE), and information-weighted peak signal-to-noise ratio (IW-PSNR). The proposed method also demonstrated a faster convergence time to an optimum solution.
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs

Previous super-resolution reconstruction (SR) works are always designed on the assumption that the degradation operation is fixed, such as bicubic downsampling. However, as for remote sensing images, some unexpected factors can cause the blurred visual performance, like weather factors, orbit altitude, etc. Blind SR methods are proposed to deal with various degradations. There are two main challenges of blind SR in RSIs: 1) the accurate estimation of degradation kernels; 2) the realistic image generation in the ill-posed problem. To rise to the challenge, we propose a novel blind SR framework based on dual conditional denoising diffusion probabilistic models (DDSR). In our work, we introduce conditional denoising diffusion probabilistic models (DDPM) from two aspects: kernel estimation progress and reconstruction progress, named as the dual-diffusion. As for kernel estimation progress, conditioned on low-resolution (LR) images, a new DDPM-based kernel predictor is constructed by studying the invertible mapping between the kernel distribution and the latent distribution. As for reconstruction progress, regarding the predicted degradation kernels and LR images as conditional information, we construct a DDPM-based reconstructor to learning the mapping from the LR images to HR images. Comprehensive experiments show the priority of our proposal compared with SOTA blind SR methods. Source Code and supplementary materials are available at https://github.com/Lincoln20030413/DDSR.
Resisting DNN-Based Website Fingerprinting Attacks Enhanced by Adversarial Training

Deep neural network (DNN) based website fingerprinting (WF) attacks pose a severe threat to the privacy of Tor users. To overcome this challenge, adversarial perturbation based WF defenses have been recently proposed to fool the classifiers of attackers, through purposefully perturbing the user’s traffic traces. Unfortunately, these defenses significantly deteriorate once the WF attacks are enhanced with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial training</i> (AT). AT endows the WF attacks with more powerful website recognition capability, through learning the perturbed traffic traces generated by attackers. To resist the WF attacks enhanced by AT, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">black-box</i> WF defense, called Acup3. First, Acup3 leverages <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">many-to-one website imitation</i> to make the traffic traces associated with different websites look more like each other, increasing the difficulty of website classification. Second, Acup3 generates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trace-agnostic</i> perturbations without accessing traffic traces, making it suitable for practical deployment. Third, Acup3 employs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">perturbation variation</i> to diversify the traffic traces of different users visiting the same website, making the knowledge learnt from AT less helpful for WF attacks. Therefore, Acup3 is more robust against AT. Experiments demonstrate Acup3 markedly surpasses four representative WF defenses (e.g., Mockingbird and AWA) in defense capability and bandwidth overhead. Facing the state-of-the-art (SOTA) attack Var-CNN enhanced with AT, Acup3 depresses its attack success rate (ASR) from 98% to 24.29% with only 13.95% bandwidth overhead. Compared to the SOTA defense AWA, Acup3 causes a 24.5% larger decrement in ASR of WF attacks, and achieves a more than 100 times faster speed of perturbation generation.