Reviewerly/exHarmony huggingface dataset:
Insights into DoH: Traffic Classification for DNS over HTTPS in an Encrypted Network

In the past few years there has been a growing desire to provide more built in functionality to protect user communications from eavesdropping. An example of this is DNS over HTTPS (DoH) which can be used to protect user privacy, confidentiality and against spoofing attacks. Since its first popularity in 2018 as used in browsers, there is much further study to test the effectiveness of DoH in protection schemes and whether it is possible to detect the protocol over the web. Detecting DoH traffic among normal web traffic is also a major challenge for network admins to allow filtering of malicious traffic flows. In this paper, we investigate machine learning classification to study the detection of DoH traffic and further analyze the key feature characteristics in the protocol behavior to help researchers build credibility in the DoH protocol detection. Our study reveals key features and statistical relationships among DoH test runs on the Alexa-recommended 100 most-used websites using three different DoH servers, showing up to 98% test accuracy in our built classifier.
On a Fast Hough/Radon Transform as a Compact Summation Scheme over Digital Straight Line Segments

The Hough transform, interpreted as the discretization of the Radon transform, is a widely used tool in image processing and machine vision. The primary way to speed it up is to employ the Brady–Yong algorithm. However, the accuracy of the straight line discretization utilized in this algorithm is limited. In this study, we propose a novel algorithm called ASD2 that offers fast computation of the Hough transform for images of arbitrary sizes. Our approach adopts a computation scheme similar to the Brady–Yong algorithm but incorporates the best possible line discretization for improved accuracy. By employing the Method of Four Russians, we demonstrate that for an image of size n×n where n=8q and q∈N, the computational complexity of the ASD2 algorithm is O(n8/3) when summing over O(n2) digital straight line segments.
Qudit surface codes and hypermap codes

In this article, we define homological quantum codes in arbitrary qudit dimensions $$D\ge 2$$ by directly defining CSS operators on a 2-Complex $$\Sigma $$ . If the 2-Complex is constructed from a surface, we obtain a qudit surface code. We then prove that the dimension of the code we define always equals the size of the first homology group of $$\Sigma $$ . We also define the distance of the codes in this setting, finding that they share similar properties with their qubit counterpart. Additionally, we generalize the hypermap-homology quantum code proposed by Martin Leslie to the qudit case. For every such hypermap code, we construct an abstract 2-Complex whose homological quantum code is equivalent to the hypermap code.
Scalable low-latency optical phase sensor array

Optical phase measurement is critical for many applications, and traditional approaches often suffer from mechanical instability, temporal latency, and computational complexity. In this paper, we describe compact phase sensor arrays based on integrated photonics, which enable accurate and scalable reference-free phase sensing in a few measurement steps. This is achieved by connecting multiple two-port phase sensors into a graph to measure relative phases between neighboring and distant spatial locations. We propose an efficient post-processing algorithm, as well as circuit design rules to reduce random and biased error accumulations. We demonstrate the effectiveness of our system in both simulations and experiments with photonics integrated circuits. The proposed system measures the optical phase directly without the need for external references or spatial light modulators, thus providing significant benefits for applications including microscope imaging and optical phased arrays.
Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mining areas is challenging. Therefore, this study proposes a network model based on transfer learning theory and a back propagation (BP) network optimized by a genetic algorithm (GA), taking the Daxigou mining area in Shaanxi Province, China, as a case study. Firstly, visible and near-infrared spectrum data from Landsat8 satellite images, digital elevation models, and geochemical data from field-collected soil samples were used to extract environmental factor candidates indicating the content and spatial distribution of certain heavy metals, including copper (Cu) and lead (Pb). Secondly, each element was correlated with environmental factors and a multicollinearity test was performed to determine the optimal factor set. Then, the BP network optimized by GA was pre-trained with sample data collected in 2017 and retrained with minimal sample data from 2019 using the parameter transfer learning method, allowing spatial distribution mapping of the Cu and Pb content in topsoil of the Daxigou mining area in 2019. From the validation results using field-collected data, the root mean square error (RMSE) and mean relative error (MRE) values using the proposed model, respectively, reduced by 4.688 mg/kg and 1.533 mg/kg for Cu and reduced by 1.586 mg/kg and 1.232 mg/kg for Pb compared to the traditional GA-BP model. Thus, conclusions can be drawn that our proposed Tr-GA-BP network performs well, requiring 16 training samples collected in 2019. In addition, the content of Cu is the highest; Pb is the second highest in the study area. Both of them were spatially distributed mainly in the exploitation, slag stacking, roadside, etc., consistent with field investigation results.
Managing Healthcare Infodemic by deep learning in providing healthcare services

Digital Health care data acquisition and processing is performed by Artificial Intelligence and Internet of Things technologies and digitization of data and information affects the patients’ behavior. News about COVID-19, a global pandemic, is circulating on social media worldwide providing a collection of big data. Awareness about the pandemic is spreading drastically in the form of messages, social media posts, tweets, and videos. It is, therefore, significant to assess the early flow of information on social media during the pandemic to prevent alarmism. This study aims to perform sentiment analysis of social media big data about COVID-19 by deep learning on a dataset provided by IEEE Data Port. The goal is to assist healthcare professionals in developing social media policies that can be used to change public opinion. The Dataset used consists of 11,858 COVID-19-related tweets collected on May 30, 2020. Data are labeled as positive or negative in the first step using TextBlob and VADER. In step II, various machine learning models are compared using three feature extraction techniques in combination with VADER and TextBlob. The results show that Extra Tree Classifier using TF-IDF features outperforms with an accuracy of 0.9474.
3D smooth path planning of AUV based on improved ant colony optimization considering heading switching pressure

Abstract A smooth and secure spatial path planning algorithm that integrates the improved ant colony optimization with the corrective connected spatial search strategy is proposed, aiming at heavy heading switching pressure of autonomous underwater vehicles sailing in complex marine environment. On the one hand, to overcome the low-dimensional search domain and inaccurate spatial communication information in traditional spatial path planning, the spatial connectivity adjacency domain search strategy is designed based on grid environment model. On the other hand, to alleviate heading switching pressure due to large path steering angles and redundant path turning points, the heuristic functions and pheromone update criterion based on ant colony optimization are introduced to improve the solution quality of smooth paths. The simulation results show that the space search strategy can improve the success probability of safe path planning without reducing the scope of explorable free space. Additionally, the simulations demonstrate that the improved ant colony optimization using the spatial search strategy can guarantee the shortest path with lowest tortuous degree and fewest turning times in the same grid environment.
Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model

In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshitai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance.
Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets.
Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems

Spacecraft systems collect health-related data continuously, which can give an indication of the systems’ health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any kind of anomalous behaviour. Typically this is performed by the use of simple thresholds or statistical techniques. Over the past few years, however, data-driven anomaly detection methods have been further developed and improved. They can help to automate the process of anomaly detection. However, it usually is time intensive and requires expertise to identify and implement suitable anomaly detection methods for specific systems, which is often not feasible for application at scale, for instance, when considering a satellite consisting of numerous systems and many more subsystems. To address this limitation, a generic diagnostic framework is proposed that identifies optimal anomaly detection techniques and data pre-processing and thresholding methods. The framework is applied to two publicly available spacecraft datasets and a real-life satellite dataset provided by the European Space Agency. The results show that the framework is robust and adaptive to different system data, providing a quick way to assess anomaly detection for the underlying system. It was found that including thresholding techniques significantly influences the quality of resulting anomaly detection models. With this, the framework can provide both a way forward in developing data-driven anomaly detection methods for spacecraft systems and guidance relative to the direction of anomaly detection method selection and implementation for specific use cases.
My Health Advisor is a Robot: Understanding Intentions to Adopt a Robotic Health Advisor

AbstractRobots and artificial intelligence (AI) have seen increased adoption in healthcare. These health technologies have the capability of providing tailored messages and feedback to each individual. Thus, a robot can potentially serve as a personal health advisor, particularly for health issues that could be benefited through regular guidance and instructions. However, there is a limited understanding of how people might respond to the idea that their health advisor could be a robot. Thus, the present study employs the technology acceptance model (TAM) to examine intentions to adopt a robotic health advisor. Findings demonstrate that perceived ease of communication with and perceived usefulness of a robotic health advisor positively predict favorable attitudes toward a robotic health advisor, which subsequently leads to strong intentions to adopt it. The present investigation also finds that perceived usefulness of a robotic health advisor directly leads to an individual’s intentions to adopt it. Overall, the present study provides important implications for perceptions of a robotic health advisor.Keywords: Robotic advisorhuman-machine communicationsocial robotsartificial intelligencehealth technology Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsJihyun KimJihyun Kim is an Associate Professor in the Nicholson School of Communication and Media at the University of Central Florida. Her primary research focuses on the effects and implications of new media/communication technologies for meaningful outcomes. Her research also examines human-machine communication and how humans perceive machine agents (e.g., AI, robots).Kelly MerrillKelly Merrill Jr. is an Assistant Professor in the School of Communication, Film, and Media Studies at the University of Cincinnati. His research investigates responses to stigmatization and discrimination in health and across technologies. He also studies the use of communication technologies for health benefits.Kun XuKun Xu is an Assistant Professor of Emerging Media at the College of Journalism and Communications, University of Florida. His research focuses on human-computer interaction, human-robot interaction, and computer-mediated communication. His work investigates how people perceive, interact with, and evaluate technologies such as social robots, virtual humans, and chatbots.Chad CollinsChad Collins is a communication instructor and writer based in Florida. He currently teaches at St. Johns River State College and is a teaching assistant at Arizona State University, while also serving as the Claremont Lincoln University Capstone advisor.
Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults' handwriting, children's handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children's handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children's handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.
Blockchain-Enabled Supply Chain Internal and External Finance Model

This study applies Stackelberg game theory to analyze and compare optimal operational strategies in four supply chain finance scenarios: traditional trade financing (TI), trade financing through the blockchain platform (BI), traditional external financing (TE), and external financing through the blockchain platform (BE). The main findings are as follows: First, the adoption of the blockchain platform reduces the interest rate threshold, making external financing more advantageous for retailers with higher capital constraint. Further, financing through the blockchain platform leads to higher wholesale prices, retail prices, and order quantities compared to traditional financing scenarios. Second, internal trade financing and the use of blockchain technology are preferred over external bank financing. However, conducting external bank financing through the blockchain platform yields greater profit growth for manufacturers and retailers. Accessing the blockchain platform is the optimal strategy for retailers and banks, leading to a favorable “multi-win” situation when the manufacturer’s platform fees are reasonable. Third, the manufacturer’s risk guarantee ratio plays a crucial role in determining the choice of financing mode, particularly when the retailer faces the risk of debt default. This study contributes to the literature by quantifying the impacts of blockchain technology deployment for three aspects that have been overlooked in previous studies: the set-up cost and access fee of the blockchain platform, the service level provided by the platform, and the demand increase resulting from blockchain technology adoption.
The mPOC Framework: An Autonomous Outbreak Prediction and Monitoring Platform Based on Wearable IoMT Approach

This paper presents the mHealth Predictive Outbreak for COVID-19 (mPOC) framework, an autonomous platform based on wearable Internet of Medical Things (IoMT) devices for outbreak prediction and monitoring. It utilizes real-time physiological and environmental data to assess user risk. The framework incorporates the analysis of psychological and user-centric data, adopting a combination of top-down and bottom-up approaches. The mPOC mechanism utilizes the bidirectional Mobile Health (mHealth) Disaster Recovery System (mDRS) and employs an intelligent algorithm to calculate the Predictive Exposure Index (PEI) and Deterioration Risk Index (DRI). These indices trigger warnings to users based on adaptive threshold criteria and provide updates to the Outbreak Tracking Center (OTC). This paper provides a comprehensive description and analysis of the framework’s mechanisms and algorithms, complemented by the performance accuracy evaluation. By leveraging wearable IoMT devices, the mPOC framework showcases its potential in disease prevention and control during pandemics, offering timely alerts and vital information to healthcare professionals and individuals to mitigate outbreaks’ impact.
Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach

The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.
Inequalities and content moderation

Abstract As the harms of hate speech, mis/disinformation and incitement to violence on social media have become increasingly apparent, calls for regulation have accelerated. Most of these debates have centred around the needs and concerns of large markets such as the EU and the United States, or the aggressive approach countries such as Russia and China adopt to regulate online content. Our focus in this article is with the rest, the smaller markets at the periphery of the advertising industry, and the deep inequalities that current approaches to content moderation perpetuate. We outline the depth of the unequal practice of moderation, particularly across Africa, and explore the underlying political and economic factors driving this gap. While recognizing content moderation has many limitations, we conclude by underlining potential approaches to increase oversight in content moderation.