Reviewerly/exHarmony huggingface dataset:
A Survey of Authentication in Internet of Things-Enabled Healthcare Systems

The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system's security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead.
Rethinking gradient operator for exposing AI-enabled face forgeries

For image forensics, convolutional neural networks (CNNs) tend to learn image content features rather than subtle manipulation traces, which constrains detection performance. Existing works usually address this issue by following a common pipeline, namely subtracting the original pixel value from the predicted pixel value to enforce CNNs to learn more features from the manipulation traces. However, due to the complicated learning mechanism, they might still have some unnecessary performance losses. In this work, we rethink the advantages of image gradient operator in exposing AI-enabled face forgeries, and design two plug-and-play modules, namely tensor pre-processing (TP) and manipulation trace attention (MTA), by combining the gradient operator with CNNs. Specifically, the TP module refines the feature tensor of each channel in the network by the gradient operator to highlight manipulation traces and improve feature representation. Moreover, the MTA module considers two dimensions, namely channel and manipulation traces, to enforce the network to learn the distribution of the manipulation traces. Both modules can be seamlessly integrated into existing CNNs for end-to-end training. Experiments show that the proposed expert system achieves better results than prior works on five public image datasets. The code is available at: https://github.com/EricGzq/GocNet-pytorch.
Scaling Blockchain via Layered Sharding

As a promising solution to blockchain scalability, sharding divides blockchain nodes into small groups called shards, splitting the workload. Existing works for sharding, however, are limited by cross-shard transactions, since they need to split each cross-shard transaction into multiple sub-transactions, each of which costs a consensus round to commit. In this paper, we introduce PYRAMID, a novel sharding system based on the idea of layered sharding. In PYRAMID, the nodes with better hardware are allowed to participate in multiple shards and store the blockchains of these shards thus they can validate and execute the cross-shard transactions without splitting. Next, to commit the cross-shard transactions with consistency among the related shards, we design a cooperative cross-shard consensus based on collective signature-based inter-shard collaboration. Furthermore, we present an optimization framework to compute an optimal layered sharding strategy maximizing the transaction throughput with the constraint of system security and node resource. Finally, we implement a prototype for PYRAMID based on Ethereum and the experimental results reveal the efficiency of PYRAMID in terms of performance and scalability, especially in workloads with a high percentage of cross-shard transactions. PYRAMID improves the throughput by up to 3.2 times compared with the state-of-the-art works and achieves about 3821 transaction per seconds for 20 shards.
Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4

Autonomous driving cars are becoming popular everywhere and the need for a robust traffic sign recognition system that ensures safety by recognizing traffic signs accurately and fast is increasing. In this paper, we build a CNN that can classify 43 different traffic signs from the German Traffic Sign Recognition benchmark dataset. The dataset is made up of 39,186 images for training and 12,630 for testing. Our CNN for classification is light and reached an accuracy of 99.20% with only 0.8 M parameters. It is tested also under severe conditions to prove its generalization ability. We also used Faster R-CNN and YOLOv4 networks to implement a recognition system for traffic signs. The German Traffic Sign Detection benchmark dataset was used. Faster R-CNN obtained a mean average precision (mAP) of 43.26% at 6 Frames Per Second (FPS), which is not suitable for real-time application. YOLOv4 achieved an mAP of 59.88% at 35 FPS, which is the preferred model for real-time traffic sign detection. These mAPs are obtained using Intersect Over Union of 50%. A comparative analysis is also presented between these models.
A bibliometric analysis of the global landscape on STEM education (2004-2021): towards global distribution, subject integration, and research trends

Purpose This study aims to provide a comprehensive review and bibliometric analysis of the literature in the field of science, technology, engineering and mathematics (STEM) education over the past 15 years, with a specific focus on global distribution and research trends. Design/methodology/approach This study collected 1,718 documents from the Web of Science (WOS) database and analyzed their timeline distribution, geographical distribution, research topics, subject areas, learning stages and citation burst using a bibliometric approach with VOSviewer and Citespace. Findings Results indicated that: overall, STEM education has increasingly gained scholarly attention and is developing diversely by emphasizing interdisciplinary, cross-domain and regional collaboration. In terms of global collaboration, a collaborative network with the USA in the center is gradually expanding to a global scope. In terms of research themes, four key topics can be outlined including educational equity, pedagogy, empirical effects and career development. Social, cultural and economic factors influence the way STEM education is implemented across different countries. The developed Western countries highlighted educational equity and disciplinary integration, while the developing countries tend to focus more on pedagogical practices. As for research trends, eastern countries are emphasizing humanistic leadership and cultural integration in STEM education; in terms of teachers’ professional development, teachers’ abilities of interdisciplinary integration, technology adoption and pedagogy application are of the greatest importance. With regards to pedagogy, the main focus is for developing students’ higher-order abilities. In terms of education equity, issues of gender and ethnicity were still the hottest topics, while the unbalanced development of STEM education across regions needs further research. Originality/value This study provides a global landscape of STEM education along the timeline, which illustrates the yearly progressive development of STEM education and indicates the future trends.
A time-aware self-attention based neural network model for sequential recommendation

Sequential recommendation is one of the hot research topics in recent years. Various sequential recommendation models have been proposed, of which Self-Attention (SA)-based models are shown to have state-of-the-art performance. However, most of the existing SA-based sequential recommendation models do not make use of temporal information, i.e., timestamps of user–item interactions, except for an initial attempt (Li et al., 2020). In this paper, we propose a Time-Aware Transformer for Sequential Recommendation (TAT4SRec), an SA-based neural network model which utilizes the temporal information and captures users’ preferences more precisely. TAT4SRec has two salient features: (1) TAT4SRec utilizes an encoder–decoder structure to model timestamps and interacted items separately and this structure appears to be a better way of making use of the temporal information. (2) in the proposed TAT4SRec, two different embedding modules are designed to transform continuous data (timestamps) and discrete data (item IDs) into embedding matrices respectively. Specifically, we propose a window function-based embedding module to preserve the continuous dependency contained in similar timestamps. Finally, extensive experiments demonstrate the effectiveness of the proposed TAT4SRec over various state-of-the-art MC/RNN/SA-based sequential recommendation models under several widely-used metrics. Furthermore, experiments are also performed to show the rationality of the different proposed structures and demonstrate the computation efficiency of TAT4SRec. The promising experimental results make it possible to apply TAT4SRec in various online applications.
A Multistage Transfer Learning Approach for Acute Lymphoblastic Leukemia Classification

Automated medical image analysis driven by artificial intelligence can revolutionize modern healthcare in producing swift and precise diagnostics. Due to doctors’ varying breadths of training and expertise, traditional leukemia screening methods frequently involve considerable subjectivity. Using a 3-stage transfer learning approach and stacks of convolutional neural networks, we constructed an efficient pathway for automatic leukemia identification and classification through various phases. Experimental findings disclosed that our pipeline powered by InceptionResNetV2 architecture decisively affects the accuracy with 99.60% (normal vs. leukemia) and 94.67% (normal to L3). Moreover, it reduces error rates by 1.65% and 6.05%, respectively. A consistent result via the T-test confirms our proposed framework robustness with a significant positive difference of 4.71% over the standard transfer learning mechanism (p-value = 0.0001 & t = 0.85310). This research could aid and support oncologists in early yet reliable prognoses of acute lymphoblastic leukemia types.
Federated Graph Machine Learning

Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many realworld scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.
LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing

Nowadays, many edge computing service providers expect to leverage the computational power and data of edge nodes to improve their models without transmitting data. Federated learning facilitates collaborative training of global models among distributed edge nodes without sharing their training data. Unfortunately, existing privacy-preserving federated learning applied to this scenario still faces three challenges: 1) It typically employs complex cryptographic algorithms, which results in excessive training overhead; 2) It cannot guarantee Byzantine robustness while preserving data privacy; and 3) Edge nodes have limited computing power and may drop out frequently. As a result, the privacy-preserving federated learning cannot be effectively applied to edge computing scenarios. Therefore, we propose a lightweight and secure federated learning scheme LSFL, which combines the features of privacy-preserving and Byzantine-Robustness. Specifically, we design the Lightweight Two-Server Secure Aggregation protocol, which utilizes two servers to enable secure Byzantine robustness and model aggregation. This scheme protects data privacy and prevents Byzantine nodes from influencing model aggregation. We implement and evaluate LSFL in a LAN environment, and the experiment results show that LSFL meets fidelity, security, and efficiency design goals, and maintains model accuracy compared to the popular FedAvg scheme.
CCFNet: Cross-Complementary fusion network for RGB-D scene parsing of clothing images

Schemes to complement context relationships by cross-scale feature fusion have appeared in many RGB-D scene parsing algorithms; however, most of these works conduct multi-scale information interaction after multi-modal feature fusion, which ignores the information loss of the two modes in the original coding. Therefore, a cross-complementary fusion network (CCFNet) is designed in this paper to calibrate the multi-modal information before feature fusion, so as to improve the feature quality of each mode and the information complementarity ability of RGB and the depth map. First, we divided the features into low, middle, and high levels, among which the low-level features contain the global details of the image and the main learning features include texture, edge, and other features. The middle layer features contain not only some global detail features but also some local semantic features. Additionally, the high-level features contain rich local semantic features. Then, the feature information lost in the coding process of low and middle level features is supplemented and extracted through the designed cross feature enhancement module, and the high-level features are extracted through the feature enhancement module. In addition, the cross-modal fusion module is designed to integrate multi-modal features of different levels. The experimental results verify that the proposed CCFNet achieves excellent performance on the RGB-D scene parsing dataset containing clothing images, and the generalization ability of the model is verified by the dataset NYU Depth V2.
A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images

In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.
FL-Defender: Combating targeted attacks in federated learning

Federated learning (FL) enables learning a global machine learning model from data distributed among a set of participating workers. This makes it possible (i) to train more accurate models due to learning from rich, joint training data and (ii) to improve privacy by not sharing the workers’ local private data with others. However, the distributed nature of FL makes it vulnerable to targeted poisoning attacks that negatively impact on the integrity of the learned model while, unfortunately, being difficult to detect. Existing defenses against those attacks are limited by assumptions on the workers’ data distribution and/or are ill-suited to high-dimensional models. In this paper, we analyze targeted attacks against FL, specifically label-flipping and backdoor attacks, and find that the neurons in the last layer of a deep learning (DL) model that are related to these attacks exhibit a different behavior from the unrelated neurons. This makes the last-layer gradients valuable features for attack detection. Accordingly, we propose FL-Defender to combat FL targeted attacks. It consists of (i) engineering robust discriminative features by calculating the worker-wise angle similarity for the workers’ last-layer gradients, (ii) compressing the resulting similarity vectors using PCA to reduce redundant information, and (iii) re-weighting the workers’ updates based on their deviation from the centroid of the compressed similarity vectors. Experiments on three data sets show the effectiveness of our method in defending against label-flipping and backdoor attacks. Compared to several state-of-the-art defenses, FL-Defender achieves the lowest attack success rates while maintaining the main task accuracy.
A hybrid NEQR image encryption cryptosystem using two-dimensional quantum walks and quantum coding

Quantum mechanisms can provide strong security in image processing against the disadvantages of pseudorandom and periodic of traditional chaotic systems. Based on its uncertainty and measurement collapse principle, this study proposes an image encryption system based on two-dimensional quantum walks and quantum coding. First, a pseudorandom number generator (PRNG) based on quantum entanglement and coherence principles is designed to generate a secret code stream inconsistent with the original image. Second, using the new enhanced quantum representation (NEQR) model, a quantum right cyclic shift operator and a quantum XOR operator are designed to obtain the final encrypted image. Experimental results and analysis show that the image encryption scheme has high security performance.
Single-state semiquantum private comparison based on Bell states

In this paper, a novel semiquantum private comparison (SQPC) protocol based on single kind of Bell states is proposed, which allows two classical parties to judge the equality of their private inputs securely and correctly under the help of a semi-honest third party (TP) who possesses complete quantum capabilities. TP is allowed to misbehave on her own but cannot conspire with anyone else. Our protocol needs none of unitary operations, quantum entanglement swapping or the reordering operations. Moreover, our protocol only needs to prepare single kind of Bell states as initial quantum resource. Detailed security analysis turns out that our protocol is secure against various outside and participant attacks. Compared with most of the existing SQPC protocols based on Bell states, our protocol is more feasible in practice.
Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Abstract Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.
Eleven quick tips for data cleaning and feature engineering

Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call "feature" a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries.