Reviewerly/exHarmony huggingface dataset:
A Systematic Literature Review on Latest Keystroke Dynamics Based Models

The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion (&#x201C;keystroke dynamics&#x201D; OR &#x201C;typing pattern&#x201D;) AND (&#x201C;authentication&#x201D; OR &#x201C;verification&#x201D; OR &#x201C;identification&#x201D;). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twenty-seven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.
Recognition of the Agricultural Named Entities With Multifeature Fusion Based on ALBERT

High quality agricultural named entity recognition (NER) model can provide effective support for agricultural information extraction, semantic retrieval and other tasks. However, the existing models ignore the potential characteristics of Chinese characters, resulting in the lack of internal semantics. Moreover, the agricultural text sequence is long, which leads to the lack of long-distance dependence of model capture. In order to solve the above problems, a self-attention mechanism RSA-CANER agricultural named entity recognition model is proposed which incorporating the potential characteristics of Chinese characters. First of all, the model takes character features and potential features of Chinese characters as input to enrich semantic information. Among them, character features are obtained based on ALBERT pre training tool, radical features are extracted based on convolutional neural network (CNN), and stroke features are extracted based on bidirectional long short-term memory model (BiLSTM). Then, based on the BiLSTM, the sequence characteristic matrix is obtained, and the self-attention mechanism is used to further enhance the ability of the model to capture long-distance dependence. Finally, the global optimal sequence is generated based on conditional random field (CRF) model. It obtains an F1-score of 95.56. The experimental results show that the model learns semantic information at multiple fine-grained levels of radicals and strokes, enriches the vector expression of target words, and its recognition accuracy is better than other models, improving the generalization ability of the model.
Fair-Weather Near-Surface Atmospheric Electric Field Measurements at the Zhongshan Chinese Station in Antarctica

The variability in the atmospheric electric field is modulated by a combination of solar activities, meteorological activities, and geological conditions. A foundational dataset of the daily variations in the fair-weather atmospheric electric field is established in this study, and several examinations are conducted into the details of these variations in particular regions. This paper is organized as follows. First, the researchers count the datasets of daily variations to the atmospheric electric field on fair-weather days from 1 March–1 August 2022 at Zhongshan Chinese Station in Antarctica and Changping Station in Beijing. Then, the average daily variation in the atmospheric electric field on 40 fair-weather days in Antarctica is shown and the variations are compared with the average curve of 37 fair-weather days in Beijing during the same time period. Finally, the three main differences between these variations, their possible reasons, and a reasonable error analysis are clarified and discussed at the end of this paper.
Improved exploiting modification direction steganography for hexagonal image processing

Steganography has made significant advances in the Square-pixel-based Image Processing (SIP) domain, but to our knowledge, no work has yet been done in Hexel (Hexagonal Pixel)-based Image Processing (HIP). This paper presents a HIP-domain data hiding method that exploits and improves the SIP-domain Exploiting Modification Direction (EMD) embedding scheme. The proposed method, Hexagonal EMD (HexEMD), utilizes a HIP-domain cover image's hexagonal nature and infrastructure to embed the secret message. In standard digital imaging systems, the sensor portion that converts photonic energy into an analog electrical signal and all the subunits that digitize, process, and display this signal are based on square pixel logic, so there is currently no commercial equipment available to produce HIP-domain images. Thus, the image is first transformed into the HIP domain in software using the infrastructure developed in the project. Then the HIP-domain image is partitioned into non-overlapping heptads of the standard size, each containing seven hexels. Rather than embedding segments to the independent pixel pairs as done in SIP-domain EMD, we do the embedding iteratively in each heptad. Experimental results show that the HexEMD outperforms its SIP equivalent, EMD, by improving embedding capacity and achieving low visual quality distortion.
Dense Haze Removal Based on Dynamic Collaborative Inference Learning for Remote Sensing Images

Haze in remote sensing images (RSIs) usually causes serious radiance distortion and image quality degeneration, resulting in difficult remote sensing inversion and interpretation. Under the condition of dense haze, existing dehazing methods still experience problems to be solved: 1) the texture details and spectral characteristics in RSIs cannot be restored well; 2) small-scale objects, such as cars and ships, which often consist of only a few pixels in RSIs, cannot be effectively highlighted in dehazed results. To solve these issues, we propose a novel dynamic collaborative inference learning (DCIL) framework that can significantly restore real surface information from dense hazy RSIs. First, we design a dynamic mutual enhancement (DME) mechanism to reinforce the low-level texture features by integrating primary information and semantic information at different levels. Second, we propose a spectrum-aware aggregation (SAA) strategy to mine the spectrum features among multiscale restored results, which can fully capture spectral characteristics. Third, we build a collaborative criterion by constructing a Siamese network structure in the training stage to improve the robustness and generalization performance of DCIL considering the diversity of the scale range and view change of RSIs. Finally, we propose a phased learning strategy to deduce the implicit haze-relevant features by gradually increasing the concentration of haze which can effectively address small-scale objects obscured by dense haze. To this end, we develop two synthetic remote sensing dehazing datasets to train our model, which can also alleviate the dilemma of hazy RSI datasets shortages. Experimental results on both synthetic datasets and real remote sensing hazy images demonstrate that the proposed DCIL can attain significant progress compared to competing methods. The two synthetic hazy datasets are available at https://github.com/Shan-rs/DCI-Net.
Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China

The Middle–Lower Yangtze River Metallogenic Belt is an important copper and iron polymetallic metallogenic belt in China. Today’s economic development is inseparable from the support of metal mineral resources. With the continuous exploitation of shallow and easily identifiable mines in China, the prospecting work of deep and hidden mines is very important. Mineral prospectivity modeling (MPM) is an important means to improve the efficiency of mineral exploration. With the increase in resource demands and exploration difficulty, the traditional 2DMPM is often difficult to use to reflect the information of deep mineral deposits. More large-scale deposits are needed to carry out 3DMPM research. With the rise of artificial intelligence, the combination of machine learning and geological big data has become a hot issue in the field of 3DMPM. In this paper, a case study of 3DMPM is carried out based on the Xuancheng–Magushan area’s actual data. Two machine learning methods, the random forest and the logistic regression, are selected for comparison. The results show that the 3DMPM based on random forest method performs better than the logistic regression method. It can better characterize the corresponding relationship between the geological structure combination and the metallogenic distribution, and the accuracy in the test set reaches 96.63%. This means that the random forest model could provide more effective and accurate support for integrating predictive data during 3DMPM. Finally, five prospecting targets with good metallogenic potential are delineated in the deep area of the Xuancheng–Magushan area for future exploration.
PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

ABSTRACTAutomatic hate speech identification in unstructured Twitter is significantly more difficult to analyze, posing a significant challenge.Existing models heavily depend on feature engineering, which increases the time complexity of detecting hate speech.This work aims to classify and detect hate speech using a linguistic pattern-based approach as pre-trained transformer language models.As a result, a novel Pattern-based Deep Hate Speech (PDHS) detection model was proposed to detect the presence of hate speech using a cross-attention encoder with a dual-level attention mechanism.Instead of concatenating the features, our model computes dot product attention for better representation by reducing the irrelevant features.The first level of Attention is extracting aspect terms using predefined parts-of-speech tagging.The second level of Attention is extracting the sentiment polarity to form a pattern.Our proposed model trains the extracted patterns with term frequency, parts-of-speech tag, and Sentiment Scores.The experimental results on Twitter Dataset can learn effective features to enhance the performance with minimum training time and attained 88%F1Score.
Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior

The remote sensing imaging environment is complex, in which many factors cause image blur. Thus, without prior knowledge, the restoration model established to obtain clear images can only rely on the observed blurry images. We still build the prior with extreme pixels but no longer traverse all pixels, such as the extreme channels. The features are extracted in units of patches, which are segmented from an image and partially overlap with each other. In this paper, we design a new prior, i.e., overlapped patches' non-linear (OPNL) prior, derived from the ratio of extreme pixels affected by blurring in patches. The analysis of more than 5000 remote sensing images confirms that OPNL prior prefers clear images rather than blurry images in the restoration process. The complexity of the optimization problem is increased due to the introduction of OPNL prior, which makes it impossible to solve it directly. A related solving algorithm is established based on the projected alternating minimization (PAM) algorithm combined with the half-quadratic splitting method, the fast iterative shrinkage-thresholding algorithm (FISTA), fast Fourier transform (FFT), etc. Numerous experiments prove that this algorithm has excellent stability and effectiveness and has obtained competitive processing results in restoring remote sensing images.
Machine learning-based incremental learning in interactive domain modelling

In domain modelling, practitioners manually transform informal requirements written in natural language (problem descriptions) to more concise and analyzable domain models expressed with class diagrams. With automated domain modelling support using existing approaches, manual modifications may still be required in extracted domain models and problem descriptions to make them more accurate and concise. For example, educators teaching software engineering courses at universities usually use an incremental approach to build modelling exercises to restrict students in using intended modelling patterns. These modifications result in the evolution of domain modelling exercises over time. To assist practitioners in this evolution, a synergy between interactive support and automated domain modelling is required. In this paper, we propose a bot-assisted approach to allow practitioners perform domain modelling quickly and interactively. Furthermore, we provide an incremental learning strategy empowered by machine learning to improve the accuracy of the bot's suggestions and extracted domain models by analyzing practitioners' decisions over time. We evaluate the performance of our bot using test problem descriptions which shows that practitioners can expect to get useful support from the bot when applied to exercises of similar size and complexity, with precision, recall, and F2 scores over 85%. Finally, we evaluate our incremental learning strategy where we observe a reduction in the required manual modifications by 70% and an improvement of F2 scores of extracted domain models by 4.2% when using our proposed approach and learning strategy together.
Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge

The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific "wake up" triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the "edge", where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains.
Posterior Regularized Bayesian Neural Network incorporating soft and hard knowledge constraints

Neural Networks (NNs) have been widely used in supervised learning due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as images and text. However, traditional NNs often lack the ability for uncertainty quantification. Bayesian NNs (BNNS) could help measure the uncertainty by considering the distributions of the NN model parameters. Besides, domain knowledge is commonly available and could improve the performance of BNNs if it can be appropriately incorporated. In this work, we propose a novel Posterior-Regularized Bayesian Neural Network (PR-BNN) model by incorporating different types of knowledge constraints, such as the soft and hard constraints, as a posterior regularization term. Furthermore, we propose to combine the augmented Lagrangian method and the existing BNN solvers for efficient inference. The experiments in simulation and two case studies about aviation landing prediction and solar energy output prediction have shown the knowledge constraints and the performance improvement of the proposed model over traditional BNNs without the constraints.
A Fast and Accurate Convolutional Neural Network for LPI Radar Waveform Recognition

Recognizing low-probability-of-intercept (LPI) radar waveforms is essential in modern electronic warfare support systems. Recently, there has been a growing body of research about a convolutional neural network (CNN) to classify modulation schemes of the intercepted LPI radar signal. This paper proposes a CNN model composed of main and sub classifier for recognizing the modulation scheme of the intercepted radar signal. Unlike the existing waveform recognition methods that uses a single CNN classifier, our proposed model exploits a two-step classification technique for effective recognition by considering the modulation scheme recognition complexity. Specifically, the proposed recognition model predicts a modulation scheme through the main classifier in the first step. When the main classifier regards modulation scheme as indistinguishable, the proposed one additionally utilize the sub classifier. Simulation results show that the proposed recognition model has a high classification accuracy while having a low overall computational cost.
Temporal Consistency Learning of Inter-Frames for Video Super-Resolution

Video super-resolution (VSR) is a task that aims to reconstruct high-resolution (HR) frames from the low-resolution (LR) reference frame and multiple neighboring frames. The vital operation is to utilize the relative misaligned frames for the current frame reconstruction and preserve the consistency of the results. Existing methods generally explore information propagation and frame alignment to improve the performance of VSR. However, few studies focus on the temporal consistency of inter-frames. In this paper, we propose a Temporal Consistency learning Network (TCNet) for VSR in an end-to-end manner, to enhance the consistency of the reconstructed videos. A spatio-temporal stability module is designed to learn the self-alignment from inter-frames. Especially, the correlative matching is employed to exploit the spatial dependency from each frame to maintain structural stability. Moreover, a self-attention mechanism is utilized to learn the temporal correspondence to implement an adaptive warping operation for temporal consistency among multi-frames. Besides, a hybrid recurrent architecture is designed to leverage short-term and long-term information. We further present a progressive fusion module to perform a multistage fusion of spatio-temporal features. And the final reconstructed frames are refined by these fused features. Objective and subjective results of various experiments demonstrate that TCNet has superior performance on different benchmark datasets, compared to several state-of-the-art methods.
Real-Time Data-Processing Framework with Model Updating for Digital Twins of Water Treatment Facilities

Machine learning (ML) models are now widely used in digital twins of water treatment facilities. These models are commonly trained based on historical datasets, and their predictions serve various important objectives, such as anomaly detection and optimization. While predictions from the trained models are being made continuously for the digital twin, model updating using newly available real-time data is also necessary so that the twin can mimic the changes in the physical system dynamically. Thus, a synchronicity framework needs to be established in the digital twin, which has not been addressed in the literature so far. In this study, a novel framework with new coverage-based algorithms is proposed to determine the necessity and timing for model updating during real-time data transfers to improve the ML performance over time. The framework is tested in a prototype water treatment facility called the secure water treatment (SWaT) system. The results show that the framework performs well in general to synchronize the model updates and predictions, with a significant reduction in errors of up to 97%. The good performance can be attributed particularly to the coverage-based updating algorithms which control the size of training datasets to accelerate the ML model updating during synchronization.
A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment

This paper focuses on cooperative multi-task assignment and re-assignment problems when multiple unmanned aerial vehicles (UAVs) attack multiple known targets. A unified multi-objective optimization framework for UAV cooperative task assignment and re-assignment is studied in this paper. In order to simultaneously optimize the losses and benefits of the UAVs, we establish a multi-objective optimization model. The amount of tasks that each UAV can perform and the number of attacks on each target are limited according to the ammunition capacity of each UAV and the value of each target. To solve this multi-objective optimization problem, a multi-objective genetic algorithm suitable for UAV cooperative task assignment is constructed based on the NSGA-II algorithm. At the same time, a selection strategy is used to assist decision-makers in choosing one or more solutions from the Pareto-optimal front. Moreover, to deal with emergencies such as UAV damage and to detect of new targets, a task re-assignment algorithm based on the contract network protocol (CNP) is developed. It can be implemented in real-time while only slightly sacrificing the ability to seek the optimal solution. Simulation results demonstrate that the methods developed in this paper are effective.
The Challenges of Blockchain Technology Adoption in the Agro-based Industries

Blockchain is one of the latest innovations in information technology, bringing a digital revolution to many industries by increasing transparency. But this technology needs to be explored a lot as of now. Agriculture supply chain management distributes agro-based products like vegetables, fruits, pulses, and cereals. This research is conducted to identify the agro-based industries' adoption of blockchain in their supply chain for achieving sustainability. The next step towards sustainable agriculture is primarily seen as blockchain-enabled agriculture. By making supply chains transparent, technology can follow products from the point of manufacture and prevent waste and inefficiency. A structured literature review helped determine the barriers to blockchain adoption in agro-based industries. This research is unique as no survey-based research on blockchain in the agriculture supply chain using structural equation modeling has been found. The seven proposed hypotheses support the blockchain challenges for adoption in agro-based industries. The findings of this study suggest that the blockchain can bring transparency and traceability and will remove the agro-industry inefficiencies.