Reviewerly/exHarmony huggingface dataset:
BACKM-EHA: A Novel Blockchain-enabled Security Solution for IoMT-based E-healthcare Applications

E-health is the use of information and communication technology (ICT) for the healthcare-related services. It uses various types of digital technologies and telecommunications, such as computers, sensing devices, Internet, and mobile devices to deliver medical services. Internet of Medical Things (IoMT) is a communication environment optimized for low-power devices (for example, health sensors and actuators) and operation on, in, or around the human body (i.e., a patient). It can be used in various applications that are related to healthcare, such as “body automation,” “healthcare,” “medical monitoring,” “body interaction,” and “medical implants (i.e., pacemaker).” Most of the communications happen in IoMT-based e-healthcare system are wireless in nature. This may cause severe threats to the security of the system. Various information security-related attacks, i.e., replay, man-in-the-middle attack (MiTM), impersonation, privileged insider, unauthorised session key computation, credentials leakage, stolen verifier, malware injection are possible in IoMT-based e-healthcare system. These threats and attacks can create serious problems in the social life of an individual, as this may reveal their confidential healthcare information to other unauthorised parties. Therefore, it is essential to propose an access control and key management scheme to secure the communication of a IoMT-based e-healthcare system. Moreover, the security of such kind of scheme can also be enhanced through the deployment of a blockchain mechanism. Therefore, in this article, we propose a blockchain-enabled access control and key management protocol for IoMT-based e-healthcare system that is named as “BACKM-EHA” in short. The security analysis of proposed BACKM-EHA is also provided through the standard, i.e., “Real-Or-Random model.” The various conducted security analyses prove the security of BACKM-EHA against the different types of potential attacks. The performance of BACKM-EHA is better than the other existing schemes, as it requires less communication cost, computation cost, and provides more “security and functionality features.”
Distinguishing Malicious Drones Using Vision Transformer

Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides in autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads. However, malicious drones that carry harmful objects are often adversely used to intrude restricted areas and attack critical public places. Thus, the timely detection of malicious drones can prevent potential harm. This article proposes a vision transformer (ViT) based framework to distinguish between drones and malicious drones. In the proposed ViT based model, drone images are split into fixed-size patches; then, linearly embeddings and position embeddings are applied, and the resulting sequence of vectors is finally fed to a standard ViT encoder. During classification, an additional learnable classification token associated to the sequence is used. The proposed framework is compared with several handcrafted and deep convolutional neural networks (D-CNN), which reveal that the proposed model has achieved an accuracy of 98.3%, outperforming various handcrafted and D-CNNs models. Additionally, the superiority of the proposed model is illustrated by comparing it with the existing state-of-the-art drone-detection methods.
Detecting DNS over HTTPS based data exfiltration

DNS is often used by attackers as a covert channel for data exfiltration, also known as DNS tunneling. Since the plaintext DNS lookup leads to privacy issues, DNS over HTTPS (DoH) has recently been standardized and deployed. DoH encapsulates DNS in HTTPS to encrypt DNS traffic between clients and recursive resolvers. Attackers can also use DoH for subtle data exfiltration. However, existing DNS tunneling detection methods for plaintext DNS are usually ineffective for DoH tunneling. In this paper, we propose a method to detect DoH-based data exfiltration. We analyze TLS fingerprints of DoH clients and build classifiers with flow-based features to detect DoH tunneling. Our experiment discusses the influence of various factors on the detection results in detail, including adversarial considerations by exploring the potential evasion. Experimental results demonstrate that the proposed method is effective, and it is very difficult to evade detection due to the difficulty of feature imitation. Besides, our method can still provide the defender with helpful information for attack investigation even if the attacker evades detection.
The Statistics of<i>k</i>-mers from a Sequence Undergoing a Simple Mutation Process Without Spurious Matches

k-mer-based methods are widely used in bioinformatics, but there are many gaps in our understanding of their statistical properties. Here, we consider the simple model where a sequence S (e.g., a genome or a read) undergoes a simple mutation process through which each nucleotide is mutated independently with some probability r, under the assumption that there are no spurious k-mer matches. How does this process affect the k-mers of S? We derive the expectation and variance of the number of mutated k-mers and of the number of islands (a maximal interval of mutated k-mers) and oceans (a maximal interval of nonmutated k-mers). We then derive hypothesis tests and confidence intervals (CIs) for r given an observed number of mutated k-mers, or, alternatively, given the Jaccard similarity (with or without MinHash). We demonstrate the usefulness of our results using a few select applications: obtaining a CI to supplement the Mash distance point estimate, filtering out reads during alignment by Minimap2, and rating long-read alignments to a de Bruijn graph by Jabba.
Blockchain-based reporting protocols as a collective monitoring mechanism in DAOs

As a recent decentralized business model, Distributed Autonomous Organization (DAO) challenges the management paradigms of conventional organizations and represents the future of democracy-based systems. However, DAOs are facing challenges that can be divided into two categories, including policy-based and technical challenges. Detecting misbehaving nodes is a crucial challenge that is considered in both categories. Designing mechanisms to report malicious behaviors while maintaining anonymity is essential for the survival and efficiency of DAOs. In this paper, we clarify the problem, and introduce Blockchain-Based Anonymous Reporting (BBAR) as a collective monitoring mechanism in DAOs with its requirements and implementation methods.
Multidirection and Multiscale Pyramid in Transformer for Video-Based Pedestrian Retrieval

In video surveillance, pedestrian retrieval (also called person re-identification) is a critical task. This task aims to retrieve the pedestrian of interest from non-overlapping cameras. Recently, transformer-based models have achieved significant progress for this task. However, these models still suffer from ignoring fine-grained, part-informed information. This paper proposes a multi-direction and multi-scale Pyramid in Transformer (PiT) to solve this problem. In transformer-based architecture, each pedestrian image is split into many patches. Then, these patches are fed to transformer layers to obtain the feature representation of this image. To explore the fine-grained information, this paper proposes to apply vertical division and horizontal division on these patches to generate different-direction human parts. These parts provide more fine-grained information. To fuse multi-scale feature representation, this paper presents a pyramid structure containing global-level information and many pieces of local-level information from different scales. The feature pyramids of all the pedestrian images from the same video are fused to form the final multi-direction and multi-scale feature representation. Experimental results on two challenging video-based benchmarks, MARS and iLIDS-VID, show the proposed PiT achieves state-of-the-art performance. Extensive ablation studies demonstrate the superiority of the proposed pyramid structure. The code is available at https://git.openi.org.cn/zangxh/PiT.git.
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks

Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent Internet of Things applications while protecting client data privacy and security. Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot. Most personalized federated learning research, however, focuses on data heterogeneity while ignoring the need for model architecture heterogeneity. Most existing federated learning methods uniformly set the model architecture of all clients participating in federated learning, which is inconvenient for each client’s individual model and local data distribution requirements, and also increases the risk of client model leakage. This article proposes a federated learning method based on co-training and generative adversarial networks (GANs) that allows each client to design its own model to participate in federated learning training independently without sharing any model architecture or parameter information with other clients or a center. In our experiments, the proposed method outperforms the existing methods in mean test accuracy by 42% when the client’s model architecture and data distribution vary significantly.
Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images

Existing CNN-based salient object detection (SOD) models rely heavily on large-scale pixel-level annotations, which are labor-intensive, time-consuming, and expensive. In contrast, sparse annotations ( e.g. , image-level or scribble) are gradually entering the SOD community. However, few efforts have been devoted to studying SOD from sparse annotations, especially in remote sensing. Moreover, sparse annotations usually contain a small amount of information, making it challenging to train a well-performing model, thereby causing its performance to lag largely behind fully-supervised models. Although some SOD methods adopt prior cues ( e.g. , edges) to improve performance, they usually lack targeted discrimination of object boundaries and thus provide saliency maps with poor boundary localization. To this end, in this paper, we propose a novel weakly-supervised SOD framework to predict the saliency of remote sensing images from sparse scribble annotations. To achieve it, we first construct the scribble-based remote sensing saliency dataset by relabeling an existing large-scale SOD dataset with scribbles, namely S-EOR dataset. After that, we present a novel scribble-based boundary-aware network (SBA-Net) for remote sensing saliency detection. Specifically, we design a boundary-aware module (BAM) to explore object boundary semantics, which is explicitly supervised by high-confidence object boundary (pseudo) labels generated by the boundary label generation (BLG) module, forcing the model to learn features that highlight object structures and thus boosting object boundary localization. Extensive quantitative and qualitative comparisons of two public remote sensing SOD datasets show that the proposed method outperforms current weakly supervised and unsupervised SOD methods and is highly competitive with existing fully supervised methods. Numerous ablation experiments demonstrate the effectiveness and generalization of the proposed model. The dataset and code will be publicly available at: https://github.com/ZhouHuang23/SBA-Net .
ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

During the last two years, several deep learning-based methods for face mask detection have been proposed by researchers. However, most of the proposed methods struggle with the detection of face masks that are too small an object to detect and further achieve low detection accuracy. Considering the issues of the existing methods, in this work, we have proposed ETL-YOLO v4 with a modified and improved feature extraction and prediction network for tiny YOLO v4 which surpasses all its predecessors and other related work in the literature. To develop ETL-YOLO v4, we have improved the backbone architecture of tiny YOLO v4 by adding a modified-dense SPP network, two additional detection layers with modified and optimized CNN layers that aid in accurate prediction, used Mish as the activation function, and utilized modified anchor boxes. Furthermore, to obtain detection results in images of varied viewpoints, we have added Mosaic and CutMix data augmentation at training time. The proposed ETL-YOLO v4 achieved 9.93% higher mAP, 5.75% higher average precision (AP) for faces with masks, and 16.6% higher average precision (AP) for the face mask region as compared to its original base-line variant.
Pairwise quantum criteria and teleportation in a spin square complex

Thermal non-classical correlations quantified by concurrence entanglement, local quantum uncertainty, and quantum coherence in a four-qubit square chain are exactly examined. The influences of the Hamiltonian parameters on the mentioned pairwise quantum criteria and fidelity of teleportation are studied, and the most interesting findings are discussed in detail. It is found that the tuning anisotropy results in enhancing the thermal quantum correlations and coherence as well as average fidelity until achieving maximum values. We persuasively deduce that quantum coherence is a more efficient criterion than that of concurrence and local quantum uncertainty to detect the quantumness of a thermal state.
Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning

Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.
Breaking down the Barrier between Teachers and Students by Using Metaverse Technology in Education

In recent years, people have steadily grown more aware of a metaverse. Besides, the enormous business potential hidden in metaverse has drawn a significant amount of funding to conduct a study on it. Technology companies such as Facebook, Microsoft, Nvidia, and others have set out the expansion of metaverse on a global scale. As a result, metaverse research has gradually increased in various countries, becoming a heated topic in China. The reason behind the metaverse's explosion is that people recognize the enormous future business value it holds. Still, they overlook the reality that education is the largest potential field of metaverse application. The metaverse and education have a natural parallel and overlap in terms of usability. In contrast to traditional education, the shared virtual space in the metaverse allows students to better participate in education and even become teachers of teachers. However, a substantial number of people are pessimistic about the metaverse's development and implementation, believing it to be a hoax. Therefore, a questionnaire scale is designed in terms of knowledge, exposure, current technology, and confidence to count people's faith in applying metaverse in education and teaching in the near future. The obtained survey data survey is analyzed using a combination of online and field visits, using both quantitative and qualitative analysis. The lack of understanding of metaverse, lack of faith in existing technology, and lack of awareness of the application of metaverse in education were determined to be the key variables affecting people's confidence in the future use of metaverse in education. It is concluded that popularizing metaverse information, existing technological levels, and current technological development speed can notably increase people's confidence.
AutoField: Automating Feature Selection in Deep Recommender Systems

Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most existing deep recommender systems, however, focus on designing sophisticated neural networks, while neglecting the feature selection process. Typically, they just feed all possible features into their proposed deep architectures, or select important features manually by human experts. The former leads to non-trivial embedding parameters and extra inference time, while the latter requires plenty of expert knowledge and human labor effort. In this work, we propose an AutoML framework that can adaptively select the essential feature fields in an automatic manner. Specifically, we first design a differentiable controller network, which is capable of automatically adjusting the probability of selecting a particular feature field; then, only selected feature fields are utilized to retrain the deep recommendation model. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our framework. We conduct further experiments to investigate its properties, including the transferability, key components, and parameter sensitivity.
A systematic review of virtual brainstorming from the perspective of creativity: affordances, framework, and outlook

This study explores virtual reality (VR) as an emerging tool integrated into brainstorming activities to enhance creativity, and systematically reviews existing studies to provide an advanced literature review on this subject. Moreover, we propose a framework for enhancing creativity in virtual brainstorming (VB) based on the 4Ps (person, process, product, and press) of creativity, which provides guidance on leveraging VB to enhance creativity. Furthermore, eight affordances of VB are identified to enhance creativity: anonymity, appraisal, avatars, immersion, multiple communication, recording, simulated objects, and tracing. The potential perspectives of VB to enhance creativity based on this study's framework are also explored, which deserves further investigation.
Do Knowledge Economy Indicators Affect Economic Growth? Evidence from Developing Countries

The competitiveness of national economies is increasingly dependent on their ability to produce and use knowledge, as knowledge, education, and innovation are the main indicators of economic growth in a globalizing world. Many countries have adopted policies related to the production of knowledge and its transformation into wealth that stimulates the growth and competitiveness of their economies. Through our study, we measured some knowledge economy (KE) variables for a sample of 20 developing countries. During the period (1996–2020), using panel data, the estimate was made using three models: the cumulative regression model, the fixed-effects model, and the random-effects model. The results of the statistical tests indicated that the fixed-effects model is the appropriate model, and that the estimates of the proposed model parameters do not contradict the assumptions of economic theory, nor do they contradict the practical reality. In addition, the results showed that 93% one of the changes that occur in economic growth in the developing countries under study is due to the dependence on the knowledge economy. It became clear through the study that the use of cross-sectional time-series models increases the accuracy of statistical forecasting, because it considers the information with a time dimension in the time series, as well as the cross-sectional dimension in different units. Our study found a positive impact on economic growth of the internet and mobile phone users, control of corruption, political stability, foreign direct investment, and the total value of international trade. Additionally, spending on education, patents for residents, and trade openness had a negative impact on economic growth. Accordingly, the knowledge economy in developing countries contributed greatly to their economic growth and had a prominent role in maintaining high rates of growth for decades. Our study contributes by expanding the scope of developing countries in deciding to focus on the important indicators in the knowledge economy (KE), so that they can increase the added value of their economic growth.
An efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic system and Otsu threshold segmentation

This paper proposes an efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic systems and Otsu threshold segmentation. In our method, first, the stereoscopic medical image is divided into the image top, middle, and lower parts. Moreover, each part is divided into background areas and regions of interest utilizing Otsu threshold segmentation, increasing about 40% the encryption efficiency when the background area is discarded. Second, compared with existing chaotic systems, the proposed coupled chaotic system has better ergodicity and randomness, with all NIST SP800-22 test data exceeding 0.01. Third, we develop a robust watermarking algorithm based on forwarding Meyer wavelet transform and singular value decomposition. Furthermore, the watermark algorithm embedded the two-dimensional code doctor-patient information in the region of interest. Finally, the experimental results demonstrate that the proposed algorithm has appealing encryption and watermark performance, the histogram and scatter graphs are governed by approximately uniform distribution, the NPCR and UACI of plaintext sensitivity and the key sensitivity are close to 99.6094% and 33.4635%, affording robustness to noise and clipping attacks.
Contextual Adapters for Personalized Speech Recognition in Neural Transducers

Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is with shallow fusion methods at inference time. However, due to their dependence on external language models and the deterministic approach to weight boosting, their performance is limited. In this paper, we propose training neural contextual adapters for personalization in neural transducer based ASR models. Our approach can not only bias towards user-defined words, but also has the flexibility to work with pretrained ASR models. Using an in-house dataset, we demonstrate that contextual adapters can be applied to any general purpose pretrained ASR model to improve personalization. Our method outperforms shallow fusion, while retaining functionality of the pretrained models by not altering any of the model weights. We further show that the adapter style training is superior to full-fine-tuning of the ASR models on datasets with user-defined content.