Fast high-fidelity quantum nondemolition readout of a superconducting qubit with tunable transverse couplings

Abstract: Dispersive readout, the current standard for measuring superconducting qubits, relies on relatively easy to engineer, static transverse coupling between a qubit and a detuned cavity mode. However, this method requires circuit design trade-offs between qubit-cavity coupling, cavity-feedline coupling, detuning, and the resultant decoherence introduced by the cavity mode. Additionally, the criteria for performing quantum nondemolition measurements, including reduced cavity photon numbers, further limits speed and fidelity. In this work, we introduce a circuit with pulse-tunable qubit-cavity and cavity-feedline couplings so that the entire qubit-measurement process can be fully optimized. Here, the core measurement process resembles a quantum gate operation between the qubit and the cavity, with the cavity acting like an ancilla, storing the qubit-state information. To fully explore time-dependent effects of the full qubit-cavity dynamics, we go beyond the rotating-wave, Jaynes-Cummings, or dispersive approximations, to develop a pulsed-measurement protocol. In addition, we propose a realistic circuit architecture that can provide a very fast, high-fidelity, QND measurement with near-unit efficiency.


VF-Mask-Net: A Visual Field Noise Reduction Method Using Neural Networks

Abstract: Visual Field (VF) measurements, crucial for diagnosing and treating glaucoma, often contain noise originating from both the instrument and subjects during the response process. This study proposes a neural network-based denoising method for VF data, obviating the need for ground truth labels or paired measurements. Using a mask-imposed VF as an input for the neural network, while the original VF serves as a training label, we evaluated performance metrics such as the accuracy, precision, and sensitivity of denoised VFs. Orthogonal experiments were also employed to assess the impact of mask number, mask structure, and replacement strategy on model accuracy. This study reveals that mask number, replacement strategy, and their interaction significantly affect the accuracy of the denoising model. Under recommended parameters, VF-Mask-Net effectively enhances the accuracy and precision of VF measurements. Furthermore, in deterioration detection tasks, denoised VFs display heightened sensitivity compared to their pre-denoising counterparts.


Research on the development of principles for designing elementary English speaking lessons using artificial intelligence chatbots

Abstract: Abstract The present study was conducted with the aim of developing principles for designing elementary English speaking lessons using artificial intelligence chatbots. To achieve this, design and development research methods were applied, and initial design principles and detailed guidelines were developed through a review of relevant literature. Subsequently, the design principles were modified and refined through two rounds of expert validation and usability evaluation. The research results yielded a total of 10 principles for designing elementary English speaking lessons using artificial intelligence chatbots, including: 1) principle of media selection, 2) principle of creating a learning environment, 3) principle of content restructuring, 4) principle of stimulating and sustaining interest and motivation, 5) principle of providing guidance, 6) principle of scaffolded learning support, 7) principle of individualized feedback provision, 8) principle of fostering a learning environment that supports growth and development, 9) principle of communication and collaboration, and 10) principle of learning management. Additionally, a set of 24 detailed guidelines necessary for implementing each lesson design principle was developed. Based on the research findings, the principles for designing elementary English speaking lessons using artificial intelligence chatbots, as well as the theoretical and practical implications of the study, were discussed. Finally, the limitations of the research were identified, and suggestions for future research were proposed.


DRR: Global Context-Aware Neural Network Using Disease Relationship Reasoning and Attention-Based Feature Fusion

Abstract: The prediction of future disease development based on past diagnosis records has gained significant attention due to the growing health awareness among individuals. Recent deep learning-based methods have successfully predicted disease development by establishing relationships for each diagnosis record and extracting features from a patient’s past diagnoses in chronological order. However, most of these models have ignored the connections between identified diseases and low-risk diseases, leading to bottlenecks and limitations. In addition, the extraction of temporal characteristics is also hindered by the problem of global feature forgetting. To address these issues, we propose a global context-aware net using disease relationship reasoning and attention-based feature fusion, abbreviated as DRR. Our model incorporates a disease relationship reasoning module that enhances the model’s attention to the relationship between confirmed diseases and low-risk diseases, thereby alleviating the current model’s bottlenecks. Moreover, we have established a global graph-based feature fusion module that integrates global graph-based features with temporal features, mitigating the issue of global feature forgetting. Extensive experiments were conducted on two publicly available datasets, and the experiments show that our method achieves advanced performance.


Data-driven criteria for quantum correlations

Abstract: We build a machine learning model to detect correlations in a three-qubit system using a neural network trained in an unsupervised manner on randomly generated states. The network is forced to recognize separable states, and correlated states are detected as anomalies. Quite surprisingly, we find that the proposed detector performs much better at distinguishing a weaker form of quantum correlations, namely, the quantum discord, than entanglement. In fact, it has a tendency to grossly overestimate the set of entangled states even at the optimal threshold for entanglement detection, while it underestimates the set of discordant states to a much lesser extent. In order to illustrate the nature of states classified as quantum correlated, we construct a diagram containing various types of states---entangled, as well as separable, both discordant and nondiscordant. We find that the near-zero value of the recognition loss reproduces the shape of the nondiscordant separable states with high accuracy, especially considering the nontrivial shape of this set on the diagram. The network architecture is designed carefully: it preserves separability, and its output is equivariant with respect to qubit permutations. We show that the choice of architecture is important to get the highest detection accuracy, much better than for a baseline model that just utilizes a partial trace operation.


Verifiably exact solution of the electronic Schrödinger equation on quantum devices

Abstract: Quantum computers have the potential for a significant speedup of molecular computations. However, existing algorithms have limitations; quantum phase estimation (QPE) is intractable on current hardware while variational quantum eigensolvers (VQE) are dependent upon approximate wave functions without guaranteed convergence. In this paper we present an algorithm that yields verifiably exact solutions of the many-electron Schr\"odinger equation. Rather than solve the Schr\"odinger equation directly, we solve its contraction over all electrons except two, known as the contracted Schr\"odinger equation (CSE). The CSE generates a wave-function Ansatz, constructed from an iterative product of nonunitary two-body transformations, whose energy gradient with respect to the two-body operator of the current iteration vanishes if and only if the CSE is satisfied. Because the CSE implies the Schr\"odinger equation, the two-electron Ansatz provides a verifiably exact Ansatz for solving the many-electron Schr\"odinger equation. The exactness property contrasts with that of Ans\"atze built from the product of unitary two-body transformations where the gradient---the residual of the anti-Hermitian part of the CSE (ACSE)---can vanish without implying a solution of the Schr\"odinger equation. We demonstrate the algorithm on both simulators and noisy quantum computers with ${\mathrm{H}}_{2}$ dissociation and the rectangle-to-square transition in ${\mathrm{H}}_{4}$.


Optimized convergence of stochastic gradient descent by weighted averaging

Abstract: Under mild assumptions stochastic gradient methods asymptotically achieve an optimal rate of convergence if the arithmetic mean of all iterates is returned as an approximate optimal solution. However, in the absence of stochastic noise, the arithmetic mean of all iterates converges considerably slower to the optimal solution than the iterates themselves. And also in the presence of noise, when a termination of the stochastic gradient method after a finite number of steps is considered, the arithmetic mean is not necessarily the best possible approximation to the unknown optimal solution. This paper aims at identifying optimal strategies in a particularly simple case, the minimization of a strongly convex function with i. i. d. noise terms and termination after a finite number of steps. Explicit formulas for the stochastic error and the optimality error are derived in dependence of certain parameters of the SGD method. The aim was to choose parameters such that both stochastic error and optimality error are reduced compared to arithmetic averaging. This aim could not be achieved; however, by allowing a slight increase of the stochastic error it was possible to select the parameters such that a significant reduction of the optimality error could be achieved. This reduction of the optimality error has a strong effect on the approximate solution generated by the stochastic gradient method in case that only a moderate number of iterations is used or when the initial error is large. The numerical examples confirm the theoretical results and suggest that a generalization to non-quadratic objective functions may be possible.


Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Abstract: The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.


Entity Management Using RDA and Wikibase: A Case Study at the National Library of Greece

Abstract: The force driving the implementation of Linked Data (LD) technologies in libraries is the aspiration for widespread interoperability of library data and for advanced information services. Toward these goals, libraries change their data models, update their communication formats, and develop prototype applications that use new models and formats and enable entity-based cataloguing. The National Library of Greece (NLG) has been actively adapting its policies, workflows, and tools to make its data LD-ready. Within this context, NLG has developed -in cooperation with Open Knowledge Greece- an entity management prototype using RDA/RDF in a Wikibase instance. The vision is to build a shared entity management system used by all memory institutions in Greece to describe entities of Greek interest. Data is to be stored as a knowledge graph that can be exported in RDF serializations, and in legacy MARC format. This paper first examines related efforts from other National Libraries, and then outlines the steps involved in the publication of the NLG Authority File as linked data; namely dataset selection, modeling, data curation, mapping, and publication. The development of the NLG entity management prototype is also presented. The paper concludes with challenges, lessons learned, and next steps.


An attack on free speech? Examining content moderation, (de-), and (re-) platforming on American right-wing alternative social media

Abstract: Contemporary research on social media looks different than it did in the late 2010s, with users facing a high-choice social media environment as new platforms emerge. Subsequently, alt-right sites have experienced a rise in users—sometimes those who have experienced content moderation by traditional social media sites. As such, scholars have investigated the impact of this content moderation (e.g. de-platforming) on users and the content posted on new alt-right platforms. This work seeks to expand extant research through analyzing a survey of Gab, Parler (now defunct), Truth Social, and Rumble users ( N = 427) who have experienced content moderation on other social media sites. While we find that those temporarily or permanently banned from traditional sites are unlikely to leave the platform altogether for a right-wing alternative social media (RWASM) site, there are active users on these sites worth studying.


Gold and Arsenic in Pyrite and Marcasite: Hydrothermal Experiment and Implications to Natural Ore-Stage Sulfides

Abstract: Hydrothermal synthesis experiments were performed in order to quantify the states of Au and As in pyrite and marcasite. The experiments were performed at 350 °C/500 bar and 490 °C/1000 bar (pyrite–pyrrhotite buffer, C(NaCl) = 15 and 35 wt.%). The synthesis products were studied by EPMA, LA-ICP-MS, and EBSD. The EPMA was applied for simultaneous determinations of Au, As, Fe, and S, with a Au detection limit of 45–48 ppm (3σ). The analyses were performed along profiles across zonal grains. The concentrations of As and Au up to 5 wt.% and 8000 ppm, respectively, were determined in pyrite and up to 6 wt.% and 1300 ppm in marcasite. In pyrite, the Au concentration decreases with fluid salinity and temperature increases. Strong positive Au–As correlation and strong negative Au–Fe and As–S correlation were identified in pyrite. Comparison of the correlations with theoretical lines implies Au–As clustering. The cluster stoichiometry is inferred to be [AuAs10]. Most probably, As in pyrite presents in the form of clusters and in the As→S solid solution. Incorporation of Au in As-rich pyrite can be controlled by the reductive deposition mechanism. In marcasite, the concentrations of Au are not correlated with the As content. The [AuAs10] clusters enrich the {210}, {113}, and {111} pyrite faces, where the former exhibits the highest affinity to Au and As. The affinity of {110} and {100} forms to Au and As is lower. Implication of the experimental results to data for natural auriferous pyrite shows that the increase of Au content at C(As) > 0.5–1 wt.% is caused by the incorporation of the Au-As clusters, but not because of the formation of Au→Fe solid solution. Therefore, the concentration of “invisible” gold in pyrite is dictated solely by the hydrothermal fluid chemistry and subsequent ore transformations.


A Majorization-Minimization Algorithm for Neuroimage Registration

Abstract: .Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.KeywordsMM algorithmimage registrationneuroimagingMSC codes92C5565K10


DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing

Abstract: Abstract In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in Critical Success Index (CSI), 30.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in Probability of Detection (POD), and 3.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data.


Optimization of functional diagnostic test: the effect of kernel method as an estimator of ROC curve

Abstract: Technical development over the last few decades has resulted in the emergence of complex data, in many cases functional data (FD). This type of data can emerge in many medical studies which are geared towards detecting diseases, predicting their course or evaluating the response to a therapy, to name a few. Thus, it is very useful to have statistical methods enabling us to evaluate diagnostic tests based on functional biomarkers. In fact, a diagnostic test that uses functional variables as biomarkers has been developed recently. Their authors proposed a functional version of ROC analysis, resulting in an empirical estimate of the functional ROC curve. In order to improve this methodology, the present paper proposes a procedure to obtain a smooth version of non-parametric estimator of the ROC curve. In addition, a comprehensive simulation study lets to investigate the discriminatory and predictive abilities of the resulting functional diagnostic test. Two examples with real medical data illustrate the approach developed: one deals with gene expression levels for tumoural/normal samples of prostate cancer; the other dataset is about white matter structures in the brain in multiple sclerosis patients.


The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

Abstract: Abstract Introduction Plain language summaries (PLSs) make complex healthcare evidence accessible to patients and the public. Large language models (LLMs) may assist in generating accurate, readable PLSs. This study explored using the LLM Claude 2 to create PLSs of evidence reviews from the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care Program. Methods We selected 10 evidence reviews published from 2021 to 2023, representing a range of methods and topics. We iteratively developed a prompt to guide Claude 2 in creating PLSs which included specifications for plain language, reading level, length, organizational structure, active voice, and inclusive language. PLSs were assessed for adherence to prompt specifications, comprehensiveness, accuracy, readability, and cultural sensitivity. Results All PLSs met the word count. We judged one PLS as fully comprehensive; seven mostly comprehensive. We judged two PLSs as fully capturing the PICO elements; five with minor PICO errors. We judged three PLSs as accurately reporting the results; and four with minor result errors. We judged three PLSs as having major result errors for incorrectly reporting total participants. Five PLSs met the target 6th to 8th grade reading level. Passive voice use averaged 16%. All PLSs used inclusive language. Conclusions LLMs show promise for assisting in PLS creation but likely require human input to ensure accuracy, comprehensiveness, and the appropriate nuances of interpretation. Iterative prompt refinement may improve results and address the needs of specific reviews and audiences. As text‐only summaries, the AI‐generated PLSs could not meet all consumer communication criteria, such as textual design and visual representations. Further testing should include consumer reviewers and explore how to best leverage LLM support in drafting PLS text for complex evidence reviews.


Quran reciter identification using NASNetLarge

Abstract: Abstract Speaker identification has significant advantages for the field of human–computer interaction. Recently, many scholars have made contributions in this field and successfully created deep learning models for automatic speaker identification systems. However, most of the speech signal processing work is limited to English-only applications, despite numerous challenges with Arabic speech, particularly with the recitation of the Holy Quran, which is the Islamic holy book. In the light of these considerations, this study proposes a model for identifying the reciter of the Holy Quran using a dataset of 11,000 audio samples extracted from 20 Quran reciters. To enable feeding the audio samples' visual representation to the pre-trained models, the audio samples are converted from their original audio representation to visual representation using the Mel-Frequency Cepstrum Coefficients. Six pre-trained deep learning models are evaluated separately in the proposed model. The results from the test dataset reveal that the NASNetLarge model achieved the highest accuracy rate of 98.50% among the pre-trained models used in this study.


The role of hyperparameters in machine learning models and how to tune them

Abstract: Abstract Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model's performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31 percent) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.


An elitist whale optimization algorithm with the nonlinear parameter: Algorithm and application

Abstract: Abstract To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with and =0.916619.


Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

Abstract: Parametric understanding for specifying formation characteristics can be perceived through conventional approaches. Significantly, attributes of reservoir lithology are practiced for hydrocarbon exploration. Well logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation time. However, manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for measurement. Therefore, in this study, Deep Neural Network (DNN) has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring lithology. DNN model has been developed for forecasting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons, number of layers, optimizer, learning rate, dropout values, and activation functions. Accuracy of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training, validation and testing. Additionally, an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class weight. It is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification model. The model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracy as well as a high F1-score.


Planning Purposeful Activities Autonomous Intelligent Robot with Knowledge Update in Short-Term Memory

Abstract: The main problems associated with the creation of autonomous intelligent robots capable of performing various complex tasks in a priori undescribed unstable problematic environments, based on the processing of knowledge presented in an abstract way, are outlined. To store typical elements of an abstract knowledge representation model, the article recommends using long-term and short-term memory. Long-term memory with associative search and data retrieval is designed to permanently store information necessary for planning a variety of purposeful activities that provide the robot with the ability to solve various complex behavioral tasks. In short-term memory, submodels of knowledge representation are entered from long-term memory, which are necessary for solving the current task of a certain type in the short term, related to the fulfillment of the task formulated for the autonomous intelligent robot. At the same time, with each change in the type of the current task of behavior being solved by an autonomous intelligent robot, a corresponding update of knowledge stored in short-term memory is simultaneously carried out. Original constructions of typical elements of the model for representing abstract knowledge in the form of various behavioral skills, set regardless of a particular subject area, have been developed. This approach to building a knowledge representation model allows autonomous intelligent robots to adapt to the current operating conditions and, on this basis, organize purposeful activities in complex unstable problematic environments. Various tools and rules for processing abstract knowledge are proposed, which endow autonomous intelligent robots with the ability to eliminate the differences between the current and target situation of the problem environment both in terms of the values of structurally equivalent relations of the same name in them, and in the current states of objects in the environment. This, in turn, makes it possible to create intelligent problem solvers for autonomous intelligent robots for various purposes, capable of performing complex tasks in unstable a priori uncertain conditions of a problematic environment.


An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network

Abstract: Abstract Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets.


Control of a Wheeled Robot on a Plane with Obstacles

Abstract: The work proposes an algorithm for controlling a wheeled robot in an environment with static and dynamic obstacles. A wheeled robot (WR) consists of a platform, two wheels with a differential drive and one roller, which is used solely for the stability of the structure and does not affect the dynamics of the system. The robot’s motion algorithm assumes its movement from the starting point to the final point in an environment with obstacles. The robot’s motion program is specified through servo-constraints, and the algorithm that implements the motion program is based on the potential field method. In the case of a dynamic obstacle, a repulsive field of a shape elongated in the direction of movement of the obstacle is constructed, allowing the robot to safely go around it. It is possible to change the geometric dimensions of the field using the entered numerical parameters. An algorithm for overcoming a potential hole by a robot is presented, according to which the robot is taken out of the potential hole and directed to a global goal by an introduced fictitious point located outside the critical region (local minimum region) and having its own attractive field. The paper presents the results of numerical simulation of the robot’s movement both in an environment with static and dynamic obstacles, as well as the results of a numerical experiment with overcoming the region of a potential well. Graphs of the required mechanical parameters are presented. The results of numerical simulation confirm the effectiveness of the proposed algorithms.


Multidimensional Multi-Connected Fuzzy Interval-Logic Controller

Abstract: The key features and the principle of operation of a multidimensional fuzzy interval-logic controller with interconnected adjustable parameters developed by the authors are considered. The scheme of interpretation of continuous physical quantities by an equivalent set of terms and the basic version of the block diagram of the regulator are presented. The main elements of the logical output block are described, including the block of production rules, which includes a predefined database of values of control actions on control objects in the form of ordinal numbers of terms of output continuous quantities. The advantages and features of setting up the mutual connections between the input and output continuous values of the regulator are considered, which is expressed in determining the functional dependencies between them. The set of parameters possessed by the terms of input continuous quantities in relation to specific output continuous quantities of the regulator is described. The methods of defuzzification of the values of output continuous quantities within the boundaries of their terms are shown. The possibility of combining input and output continuous quantities into groups when setting up mutual connections between them is described. The mechanism of determining groups of interrelated continuous quantities is illustrated, the total number of which is determined by forming a matrix of relationships, according to the author’s scheme. The algorithm of sequential processing of the contents of the cells of the matrix of relationships, consisting of three steps, is considered. Expressions are given for calculating the basic parameters of a multidimensional fuzzy interval-logic controller with interrelated adjustable parameters. The results of a computational experiment to reduce the system of production rules of the regulator in determining the relationships between input and output continuous quantities are presented.


Optimizing AI Social Chatbots for Relational Outcomes: The Effects of Profile Design, Communication Strategies, and Message Framing

Abstract: With more corporations incorporating artificial intelligence (AI) tools like social chatbots into their public communication practices, we explore optimal chatbot designs for organization-public relational outcomes. Our 2 × 2 × 2 factorial web experiment with eight custom-designed chatbots showed that communication strategies (verbal and non-verbal social cues such as emoji, memes, filler words, and response delay) had a significant impact (Cohen’s d = 0.536, standardized coefficient β = 0.438) on chatbot social conversation, the central antecedent to the investigated relational outcomes such as trust in business. Furthermore, the effect of chatbot social conversation is partially mediated by perceived organizational listening, highlighting the importance of listening and its related theories and practices in automated business communication.


Challenging 21st-Century Competencies for STEM Students: Companies’ Vision in Slovenia and Norway in the Light of Global Initiatives for Competencies Development

Abstract: Rapid technological innovations are changing the jobs that Science, Technology, Engineering, and Mathematics (STEM) students are employed in after completing their education. Therefore, employers are looking for applicants with skills beyond the specialised professional competencies that can be acquired in the chosen subjects during formal education. Higher education institutions (HEIs) have an important role to play in this process, as they endeavour to equip graduates with the skills and knowledge for future jobs. Our study aims to identify the key competencies that Slovenian and Norwegian companies expect from graduates of selected STEM degree programmes in both countries. Based on these findings, the participating HEIs can tailor their degree programmes to the employability requirements of companies. This study first analysed European and international recommendations and initiatives relevant to skills development in STEM employment and education. We selected 30 key competencies and included them in the questionnaires in which companies’ human resources managers (HRMs) defined their attitudes towards employability skills in the future. We focused on companies employing graduates of the Ecotechnologies and Civil and Environmental Engineering programmes of the participating HEIs. The analysis of the results showed that companies in the two countries have different attitudes towards skills. Four competencies were identified as more important in both countries: (i) management and design of processes, technologies, and people’s work, (ii) adaptation to changes, (iii) ability to manage uncertainty, and (iv) innovation. Based on the analysis, we have developed a framework of ten employability skills for STEM students. In addition, this study provides the basis for operating a decision support system (DSS) to monitor the development of students’ competencies. The DSS is being developed as part of the RESPO-VI project supported by the Norway Grants to better match the skills of STEM students as future job seekers with the needs of potential employers. The study’s results will also help other formal and non-formal education adapt their curricula and thus reduce the employability skill gaps between current STEM education and future labour market needs.


Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control

Abstract: Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.


Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

Abstract: Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method.


A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation

Abstract: This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.


40Ar/39Ar and (U-Th)/He constraints on emplacement, exhumation, and weathering of alkaline-carbonatite complexes in the Alto Paranaíba Igneous Province (APIP), Brazil

Abstract: The timing and rate of emplacement, exhumation, and weathering of alkaline-carbonatite complexes associated with failed rift systems in the South Atlantic provide constraints on the thermotectonic conditions of West Gondwana before, during, and after break-up. 40Ar/39Ar geochronology of biotite crystals show that two alkaline-carbonatite complexes – Araxá and Catalão I – from the Alto Paranaíba Igneous Province were shallowly emplaced simultaneously at ∼ 85 Ma. After rapid cooling, the igneous complexes were rapidly exhumed between 85 and 66 Ma at Araxá (∼ 52 m.Ma-1) and 85 and 45 Ma at Catalão (∼ 25 m.Ma-1). Fast exhumation of the complexes occurred under relatively arid to semi-arid climates, conditions that favored physical weathering and erosion. The eroded material was transported in a NE-SW direction and deposited in the adjacent Paraná and São Sanfranciscana basins. After exhumation, wetter climates and more subdued tectonic conditions favored active widespread vegetation cover that favored reduced erosion and deepening of weathering profiles. After 66 Ma, the Araxá complex denuded at rates of 3.4 m.Ma-1; at Catalão I, denudation rates reduced to 4.7 m.Ma-1 after ∼ 45 Ma. Favorable geological and climatic conditions promoted the formation of deep and chemically stratified lateritic weathering profiles. The exhumed and weathered carbonatites underwent hypogene and supergene enrichment in Nb, P, Ti, and REEs, critical metals needed for the electrification of our energy base. The preservation of the lateritic profiles and their mineral wealth in the continental interior contrasts with the younger and shallower weathering profiles formed on similar lithologies and at equivalent elevations near the Brazilain South Atlantic margin, confirming that climatic and tectonic conditions along the margin were less conducive to the preservation of valuable mineralized systems because of enhaced erosion.


Error Analysis of Pretrained Language Models (PLMs) in English-to-Arabic Machine Translation

Abstract: Abstract Advances in neural machine translation utilizing pretrained language models (PLMs) have shown promise in improving the translation quality between diverse languages. However, translation from English to languages with complex morphology, such as Arabic, remains challenging. This study investigated the prevailing error patterns of state-of-the-art PLMs when translating from English to Arabic across different text domains. Through empirical analysis using automatic metrics (chrF, BERTScore, COMET) and manual evaluation with the Multidimensional Quality Metrics (MQM) framework, we compared Google Translate and five PLMs (Helsinki, Marefa, Facebook, GPT-3.5-turbo, and GPT-4). Key findings provide valuable insights into current PLM limitations in handling aspects of Arabic grammar and vocabulary while also informing future improvements for advancing English–Arabic machine translation capabilities and accessibility.


Digital Transformation in Tunneling – A Project Report on TransIT

Abstract: The digital transformation has significantly affected many industries. Recently, it has gained momentum in the Architecture, Engineering, and Construction (AEC) sector in general and tunneling in particular. To support these developments, TransIT represents an inter-university and interdisciplinary research initiative with the goal of advancing digitalization in tunneling. This article discusses the challenges connected with the digital transformation of the tunneling sector. We examine this topic with different use cases demonstrating solutions to those challenges.


Encrypted Video Search with Single/Multiple Writers

Abstract: Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has become a major concern since many videos contain sensitive information. Although retrieving (unencrypted) videos has been extensively investigated, retrieving encrypted multimedia has received relatively rare attention, at best in a limitation of image-based similarity searches. We initiate the study of scalable encrypted video search, enabling clients to query videos similar to an image search. Our modular framework leverages intrinsic attributes of videos, such as semantics and visuals, to effectively capture their contents. We propose a two-step approach whereby lightweight searchable encryption techniques are used for pre-screening, followed by an interactive approach for fine-grained search. Furthermore, we present three instantiations, including one centralized-writer instantiation and two distributed-writer instantiations, to effectively cater to varying needs and scenarios– 1) The centralized one employs forward and backward private searchable encryption [CCS 2017] over deep hashing [CVPR 2020]. 2) Motivated by distributed computing, the multi-writer instantiations building atop HSE [Usenix Security 2022] allows searching the relevant videos contributed by multiple intuitions collaboratively. Our experimental results illustrate their practical performance over multiple real-world datasets, whether in a centralized setting or distributed setting.


The effects of quantum hardware properties on the performances of variational quantum learning algorithms

Abstract: Abstract In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.


Modeling and Verification of Natural Language Requirements based on States and Modes

Abstract: The relationship between states (status of a system) and modes (capabilities of a system) used to describe system requirements is often poorly defined. The unclear relationship could make systems of interest out of control because of the out of boundaries of the systems caused by the newly added modes. Formally modeling and verifying requirements can clarify the relationship, making the system safer. To this end, an innovative approach to analyzing requirements is proposed. The MoSt language (a Domain Specific Language implemented on the Xtext framework) is firstly designed for requirements modeling and a model validator is realized to check requirements statically. A code generator is then provided to realize the automatic model transformation from the MoSt model to a NuSMV model, laying the foundation for the dynamic checks of requirements through symbolic model checking. Next, a NuSMV runner is designed to connect the NuSMV with the validator to automate the whole dynamic checks. The grammar, the model validator, the code generator, and the NuSMV runner are finally integrated into a publicly available Eclipse-based tool. Two case studies have been employed to illustrate the feasibility of our approach. For each case study, we injected 14 errors. The results show that the static and dynamic checks can successfully detect all the errors.


Optimal inexactness schedules for tunable oracle-based methods

Abstract: Several recent works address the impact of inexact oracles in the convergence analysis of modern first-order optimization techniques, e.g. Bregman Proximal Gradient and Prox-Linear methods as well as their accelerated variants, extending their field of applicability. In this paper, we consider situations where the oracle's inexactness can be chosen upon demand, more precision coming at a computational price counterpart. Our main motivations arise from oracles requiring the solving of auxiliary subproblems or the inexact computation of involved quantities, e.g. a mini-batch stochastic gradient as a full-gradient estimate. We propose optimal inexactness schedules according to presumed oracle cost models and patterns of worst-case guarantees, covering among others convergence results of the aforementioned methods under the presence of inexactness. Specifically, we detail how to choose the level of inexactness at each iteration to obtain the best trade-off between convergence and computational investments. Furthermore, we highlight the benefits one can expect by tuning those oracles' accuracy instead of keeping it constant throughout. Finally, we provide extensive numerical experiments that support the practical interest of our approach, both in offline and online settings, applied to the Fast Gradient algorithm.


Fossil hailstone imprints in Mesoproterozoic (1.7 Gyr) quartzite of the Tombador Formation (Chapada Diamantina Group), Brazil

Abstract: Fossil pockmarks, 2.5-4 cm in diameter, 3-8 mm deep, with raised, asymmetrical borders are preserved in Mesoproterozoic quartzite representing fluvial and eolic lithofacies of the Tombador Formation (Chapada Diamantina Group) deposited 1.7 billion years ago in what is now northeastern Brazil. Indentations created by projecting 2-4 cm diameter ice pellets into moist beach sand were of the same type - with raised, asymmetrical borders and equivalent diameters and depths – strongly supporting the hypothesis that the fossil features represent impressions of paleohailstone impacts. These well-defined and well-preserved paleohailstone impressions represent the oldest known features of their type in the world, and the first records from South America.


Simplified Multi-head Mechanism for Few-Shot Remote Sensing Image Classification

Abstract: Abstract The study of few-shot remote sensing image classification has received significant attention. Although meta-learning-based algorithms have been the primary focus of recent examination, feature fusion methods stress feature extraction and representation. Nonetheless, current feature fusion methods, like the multi-head mechanism, are restricted by their complicated network structure and challenging training process. This manuscript presents a simplified multi-head mechanism for obtaining multiple feature representations from a single sample. Furthermore, we perform specific fundamental transformations on remote-sensing images to obtain more suitable features for information representation. Specifically, we reduce multiple feature extractors of the multi-head mechanism to a single one and add an image transformation module before the feature extractor. After transforming the image, the features are extracted resulting in multiple features for each sample. The feature fusion stage is integrated with the classification prediction stage, and multiple linear classifiers are combined for multi-decision fusion to complete feature fusion and classification. By combining image transformation with feature decision fusion, we compare our results with other methods through validation tests and demonstrate that our algorithm simplifies the multi-head mechanism while maintaining or improving classification performance.


Moderately supervised learning: definition, framework and generality

Abstract: Abstract Learning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the labels prepared for the training dataset, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL). SL concerns the situation where the training dataset is assigned with ideal (complete, exact and accurate) labels, while WSL concerns the situation where the training dataset is assigned with non-ideal (incomplete, inexact or inaccurate) labels. However, various solutions for SL tasks under the era of deep learning have shown that the given labels are not always easy to learn, and the transformation from the given labels to easy-to-learn targets can significantly affect the performance of the final SL solutions. Without considering the properties of the transformation from the given labels to easy-to-learn targets, the definition of SL conceals some details that can be critical to building the appropriate solutions for specific SL tasks. Thus, for practitioners in various AI application fields, it is desirable to reveal these details systematically. This article attempts to achieve this goal by expanding the categorization of SL and investigating the sub-type that plays the central role in SL. More specifically, taking into consideration the properties of the transformation from the given labels to easy-to-learn targets, we firstly categorize SL into three narrower sub-types. Then we focus on the moderately supervised learning (MSL) sub-type that concerns the situation where the given labels are ideal, but due to the simplicity in annotation, careful designs are required to transform the given labels into easy-to-learn targets. From the perspectives of the definition, framework and generality, we conceptualize MSL to present a complete fundamental basis to systematically analyse MSL tasks. At meantime, revealing the relation between the conceptualization of MSL and the mathematicians’ vision, this article as well establishes a tutorial for AI application practitioners to refer to viewing a problem to be solved from the mathematicians’ vision.


Digital quantum simulation of non-perturbative dynamics of open systems with orthogonal polynomials

Abstract: Classical non-perturbative simulations of open quantum systems&apos; dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open system. In this work, we propose the use of the Time Evolving Density operator with Orthogonal Polynomials Algorithm (TEDOPA) on a quantum computer, which we term as Quantum TEDOPA (Q-TEDOPA), to simulate non-perturbative dynamics of open quantum systems linearly coupled to a bosonic environment (continuous phonon bath). By performing a change of basis of the Hamiltonian, the TEDOPA yields a chain of harmonic oscillators with only local nearest-neighbour interactions, making this algorithm suitable for implementation on quantum devices with limited qubit connectivity such as superconducting quantum processors. We analyse in detail the implementation of the TEDOPA on a quantum device and show that exponential scalings of computational resources can potentially be avoided for time-evolution simulations of the systems considered in this work. We applied the proposed method to the simulation of the exciton transport between two light-harvesting molecules in the regime of moderate coupling strength to a non-Markovian harmonic oscillator environment on an IBMQ device. Applications of the Q-TEDOPA span problems which can not be solved by perturbation techniques belonging to different areas, such as the dynamics of quantum biological systems and strongly correlated condensed matter systems.


Attention-Based Monocular Depth Estimation Considering Global and Local Information in Remote Sensing Images

Abstract: Monocular depth estimation using a single remote sensing image has emerged as a focal point in both remote sensing and computer vision research, proving crucial in tasks such as 3D reconstruction and target instance segmentation. Monocular depth estimation does not require multiple views as references, leading to significant improvements in both time and efficiency. Due to the complexity, occlusion, and uneven depth distribution of remote sensing images, there are currently few monocular depth estimation methods for remote sensing images. This paper proposes an approach to remote sensing monocular depth estimation that integrates an attention mechanism while considering global and local feature information. Leveraging a single remote sensing image as input, the method outputs end-to-end depth estimation for the corresponding area. In the encoder, the proposed method employs a dense neural network (DenseNet) feature extraction module with efficient channel attention (ECA), enhancing the capture of local information and details in remote sensing images. In the decoder stage, this paper proposes a dense atrous spatial pyramid pooling (DenseASPP) module with channel and spatial attention modules, effectively mitigating information loss and strengthening the relationship between the target’s position and the background in the image. Additionally, weighted global guidance plane modules are introduced to fuse comprehensive features from different scales and receptive fields, finally predicting monocular depth for remote sensing images. Extensive experiments on the publicly available WHU-OMVS dataset demonstrate that our method yields better depth results in both qualitative and quantitative metrics.


Reflective equilibrium in logic

Abstract: Abstract Among the areas of knowledge that the method of reflective equilibrium (RE) has been applied to is that of logical validity. According to RE in logic, we come to be justified in believing a (deductive) logical theory in virtue of establishing some state of equilibrium between our initial judgements over the validity of specific (natural language) arguments and the logical principles which constitute our logical theory. Unfortunately, however, while relatively popular, RE with regards to logical theorizing is underspecified. In particular, it’s unclear what constitute: (1) the relevant logical “data”, (2) logical theories, so that they can be suitably tested, and (3) the mechanisms under which such theories are tested. Considering the various options for how to interpret the position, we argue that in order to be workable the advocate of RE about logic must embrace what we call an operationalised , wide and communal version of RE with a rich understanding of logical theories. Fortunately for the advocate of RE, there is an available account of logic’s epistemology which possesses just these properties, logical predictivism . However, equally unfortunately, logical predictivism commits the advocate of RE to certain further claims that they have historically been weary of holding. Consequently, it is unclear whether RE about logic itself is a viable proposal, or rather a similar but distinct epistemology of logic.


Spatial quantum error correction threshold

Abstract: We consider a spatial analogue of the quantum error correction threshold. Given individual time-independent subsystems in which quantum information is coherent over sufficiently long lengths, we show how the information can be kept coherent for arbitrarily long lengths by forming time-independent composite systems. The subsystem coherence range exhibits threshold behavior. When it exceeds a range ${\ensuremath{\xi}}_{\mathrm{th}}$, meaningful information can be extracted from the ground state of the composite system. Otherwise, the information is garbled. The threshold transition implies that the parent Hamiltonian of the ground state has gone from gapped to gapless. Ramifications of the construction for projected entangled pair states and for adiabatic quantum computation are considered.


No-signalling constrains quantum computation with indefinite causal structure

Abstract: Quantum processes with indefinite causal structure emerge when we wonder which are the most general evolutions, allowed by quantum theory, of a set of local systems which are not assumed to be in any particular causal order. These processes can be described within the framework of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mo>&#x2212;</mml:mo><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:math> quantum theory which, starting from considering maps from quantum transformations to quantum transformations, recursively constructs a hierarchy of quantum maps of increasingly higher order. In this work, we develop a formalism for quantum computation with indefinite causal structures; namely, we characterize the computational structure of higher order quantum maps. Taking an axiomatic approach, the rules of this computation are identified as the most general compositions of higher order maps which are compatible with the mathematical structure of quantum theory. We provide a mathematical characterization of the admissible composition for arbitrary higher order quantum maps. We prove that these rules, which have a computational and information-theoretic nature, are determined by the more physical notion of the signalling relations between the quantum systems of the higher order quantum maps.


Cultural Itineraries Generated by Smart Data on the Web

Abstract: The development of storage standards for databases of different natures and origins makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential for the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has an artistic and cultural heritage unique in the world and the Italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts.


A Universally Composable Linkable Ring Signature Supporting Stealth Addresses

Abstract: The linkable ring signature supporting stealth addresses (SALRS) is a recently proposed cryptographic primitive, which is designed to comprehensively address the soundness and privacy requirements associated with concealing the identities of both the payer and payee in cryptocurrency transactions. However, concerns regarding the scalability of SALRS have been underexplored. This becomes notably pertinent in intricate blockchain systems where multiple cryptographic primitives operate concurrently. To bridge this gap, our work revisited and formalized the ideal functionality of SALRS within the universal composability (UC) model. This encapsulates all correctness, soundness, and privacy considerations. Moreover, we established that the newly proposed UC-security property for SALRS is equivalent to the concurrent satisfaction of signer-unlinkability, signer-non-slanderability, signer-anonymity, and master-public-key-unlinkability. These properties represent the four crucial game-based security aspects of SALRS. This result ensures the ongoing security of previously presented SALRS constructions within the UC framework. It also underscores their adaptability for seamless integration with other UC-secure primitives in complex blockchain systems.


A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Abstract: Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents.


Chernoff Fusion Using Observability Gramian-Centric Weighting

Abstract: In this work, an observability Gramian (OG)-based Chernoff fusion (CF) rule is investigated for dealing with unknown correlated probability density functions (PDFs). Specifically, we introduce a generalised uniform observability (GUO) condition, which ensures that error covariances and estimate errors are bounded under nonlinear settings within the extended Kalman filter (EKF) framework. Leveraging the GUO condition, we develop an OG-centric weighting selection method that optimises fusion weights while guaranteeing non-divergent performance using an approximated Chernoff fusion (ACF) algorithm. The resulting OG-centric weights are then embedded to develop an OG-based approximated Chernoff fusion (OGBACF) algorithm that can compute fusion weights and error covariances in parallel. Finally, we conduct simulations to demonstrate the efficacy of our proposed fusion methodology.


Police Organisational Behaviour and the Trend of Criminal Activities in Rivers State Nigeria

Abstract: This study was carried out to investigate the extent to which the Nigeria Police Force (NPF) organizational behavior has reduced the trend of criminal activities in Rivers State. A descriptive survey research method of correlational research design was employed in this study to provide detailed investigation of the procedure used to establish the relationship between the Nigeria police force organizational behavior and the trend of criminal activities in Rivers State. The population of the study was 193,392,500 persons consisting of residents drawn from the cities and rural communities in Rivers State. A sample size of 400 respondents was determined, using Taro Yamene’s sample size techniques at 0.5 percent level of significance. A purposive sampling technique was adopted using a considerable percentage representation approach to ensure effective selection of the representative sample elements of the population from respondents who have adequate knowledge of the study under investigation. A structured instrument for data collection containing twenty (20) item questions was used for the study. The face and content validation of the instrument was obtained through the judgment of experts. A test-retest method was used to determine the reliability of the instrument and a reliability index of .83 was obtained. The data collected were analyzed using the descriptive statistics involving the mean score test and the percentage test method to answer the research questions, while the Z-score test of the inferential statistics method was used to test the hypothesized variables at .05 level of significance. The results obtained revealed that “Nigeria Police Force organizational behavior does not significantly reduce the trend of criminal activities in Rivers State.” The implication of the finding is that the discovered weakness of the Nigeria police force organizational behavior is that it lacks the needed proactive ethical improvement value to carry out its responsible assignments as an organization. Consequently, increasing the level of police compromise with many related criminal activities is considered mephitic enough to uplift or expand the trend of criminal activities in the state if more punitive measures are not employed by the government. It was therefore advisable that the federal government should employ legal assessor that will be used to form “Police Behavioral Monitoring Task Force (PBMT)” in every local government areas to oversee or monitor unethical and irresponsible behaviors of the Nigeria police officers in each state to curtail or curb police dysfunctional involvement in criminal related practices.


Applying Statistical Analysis and Economics Models to Unscramble the Depositional Signals from Chemical Proxies in Black Shales

Abstract: The complex controls on the accumulation of organic-rich rocks remain elusive, despite their economic importance as source rocks and unconventional reservoirs, partially due to the multitude of factors that may impact production and preservation of organic matter in sediments. The complexity of Earth systems is comparable to the intricacies of Economics, and application of statistical and econometrics methods and models to analyze geological data may assist interpretation of the processes controlling organic burial. Chemical indices calculated for mudrock datasets from modern sediments and the Woodford Formation were used as proxies for detrital input, primary productivity, redox conditions, and upwelling, and a series of statistical analyses were run to test whether these methods were useful to discriminate different depositional conditions and establish the controls on total organic carbon (TOC) in the sediments. Model results showed that chemical proxies reliably predict not only TOC but also indicate correlations between indices. Our results suggest that detrital input, primary productivity and bottom-water anoxia are relevant drivers of organic content in the sediments, but the first two appear to have a more significant role in organic burial, illustrating the usefulness of these methods to assess depositional parameters in organic-rich rocks.


Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

Abstract: In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.


Data management method for building internet of things based on blockchain sharding and DAG

Abstract: Sharding technology can address the throughput and scalability limitations that arise when single-chain blockchain are applied in the Internet of Things (IoT). However, existing sharding solutions focus on addressing issues like malicious nodes clustering and cross-shard transactions. Existing sharding solutions cannot adapt to the performance disparities of edge nodes and the characteristic of three-dimensional data queries in building IoT. This leads to problems such as shard overheating and inefficient data query efficiency. This paper proposes a dual-layer architecture called S-DAG, which combines sharded blockchain and DAG blockchain. The sharded blockchain processes transactions within the building IoT, while the DAG blockchain stores block headers from the sharded network. By designing an Adaptive Balancing Load Algorithm (ABLA) for periodic network sharding, nodes are divided based on their load performance values to prevent the aggregation of low-load performance nodes and the resulting issue of shard overheating. By combining the characteristics of the KD tree and Merkle tree, a block structure known as 3D-Merkle tree is designed to support three-dimensional data queries, enhancing the efficiency of three-dimensional data queries in building IoT. By deploying and conducting simulation experiments on various physical devices, we have verified the effectiveness of the solution proposed in this paper. The results indicate that, compared to other solutions, the proposed solution is better suited for building IoT data management. ABLA is effective in preventing shard overheating issue, and the 3D-Merkle tree significantly enhances data query efficiency.


Sparse least-squares Universum twin bounded support vector machine with adaptive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e21955" altimg="si10.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norms and feature selection

Abstract: In data analysis, when attempting to solve classification problems, we may encounter a large number of features. However, not all features are relevant for the current classification, and including irrelevant features can occasionally degrade learning performance. As a result, selecting the most relevant features is critical, especially for high-dimensional data sets in classification problems. Feature selection is an effective method for resolving this issue. It attempts to represent the original data by extracting relevant features containing useful information. In this research, our aim is to propose a p-norm least-squares Universum twin bounded support vector machine (LSp-UTBSVM) to perform classification and feature selection at the same time. Indeed, the proposed method, which outperforms the traditional least-squares Universum twin bounded support vector machine, can achieve good classification accuracy in a reasonable amount of time while also providing a sparse solution. The model we propose is an adaptive learning procedure with p-norm (0<p<1), where the parameter p can be automatically selected by the data set. The algorithm we use to find the approximate solution of this model involves solving systems of linear equations. Furthermore, we obtain new bounds for the absolute values of non-zero components of a local optimal solution. These bounds allow us to remove the zero components from an arbitrary numerical solution. Setting the parameter p, LSp-UTBSVM improves classification accuracy and selects the relevant features. Numerical experiments on a handwritten digit recognition, University of California Irvine (UCI) benchmark, Normally Distributed Clusters (NDC) and high dimensional data sets confirm the superiority of the proposed method in the accuracy of classification and the selection of relevant features in comparison with some popular methods.


Research on Unmanned Aerial Vehicle Path Planning

Abstract: As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.


An adaptive density clustering approach with multi-granularity fusion

Abstract: The real-world dataset exhibits diversity, incorporating instances with complex shapes and significant differences in density hierarchy, potentially disrupted by noise. However, most clustering algorithms typically rely on single-granularity fusion, requiring the pre-setting of global parameters for the entire dataset. Nevertheless, these global parameters may not adequately adapt to clusters with varying hierarchies or shapes, consequently reducing the clustering effectiveness. Therefore, we propose an adaptive density clustering approach with multi-granularity fusion. This approach characterizes the dataset with multi-granularity, forming natural granular-ball. After processing these natural granular-ball, overlapping ones are fused to yield the final clustering result. The entire approach adeptly identifies datasets with significant differences in shape or density hierarchy and exhibits a certain level of robustness. All codes have been released at https://github.com/xjnine/NGBC.


STEFF: Spatio-temporal EfficientNet for dynamic texture classification in outdoor scenes

Abstract:
Abstract
 In recent years, dynamic texture classification has become an important task for computer vision. This is a challenging task due to the unknown spatial and temporal nature of dynamic texture. To overcome this challenge, we investigate the potential of deep learning approaches and propose a novel spatio-temporal approach (STEFF) for dynamic texture classification that combines the representation power of motion and appearance using the difference and average operators between video sequences. In this work, we extract deep texture features from outdoor scenes and integrate both spatial and temporal features into a pre-trained Convolutional Neural Network model, namely EfficientNet, with a fine-tuning and regularization process. The robustness of the proposed approach is reflected in the promising result when comparing our method to the proposed architectures and other existing models. The experimental results on three datasets demonstrate the effectiveness and efficiency of the proposed approach. The accuracy percentages are 95.95%, 94.09%, and 98.01% on the outdoor scenes of Yupenn, DynTex++, and Yupenn++ datasets, respectively.


Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

Abstract: Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.


Privacy-Preserving Multi-Party Cross-Chain Transaction Protocols

Abstract: Cross-chain transaction technologies have greatly promoted the scalability of cryptocurrencies, which then facilitates the development of Metaverse applications. However, existing solutions rely heavily on centralized middleware (notary) or smart contracts. These schemes lack privacy considerations, and users’ cross-chain transactions are easy to master by other parties. Some signature-based payment schemes have good privacy but do not support multi-party cross-chain protocols or rely heavily on some time assumptions. The uncertainty of user behavior makes it difficult to design a secure multi-party cross-chain protocol. To solve these problems, we investigate how to design a secure multi-party cross-chain transaction protocol with offline tolerance. We propose a new signature algorithm called the pre-adaptor signature scheme, an extension of the adaptor signature scheme. The pre-adaptor signature scheme combines the multi-signature and adaptor signature schemes, which can realize the secret transmission channel between multiple parties. To provide offline tolerance, we encode our protocol into the P2SH script. Our protocol provides better privacy due to no dependence on smart contracts. The performance evaluation was conducted with ten participants. For each participant of our cross-chain protocol, the initialization and execution process can be performed in 3 milliseconds and with 6 k bytes of communication overhead at most. The cost increases linearly with the increase in the number of participants.


The recommender problem with convex hulls

Abstract: We introduce a novel approach for the user-based, collaborative filtering, recommender problem for selecting neighbors and weighting their ratings to make a prediction. The proposed approach is based on computational geometry using convex hulls and linear programming (LP). Computational testing is used to compare our approach with one of the current standard methods based on Pearson correlations using three data sets: Jester, Yahoo! Music User Ratings of Musical Artists (Yahoo!Artists), and MovieLens. The comparisons reveal that the method based on convex hulls and LPs can reduce the RMSE for predicted ratings to up to onethird of that of Pearson's correlations. The results make a new method based on polyhedral geometry available to attack other machine learning problems.


Scattering expansion for localization in one dimension: From disordered wires to quantum walks

Abstract: We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. We apply this expansion to several examples, including the Anderson model, a general class of periodic-on-average-random potentials, and a two-component discrete-time quantum walk, showing analytically in the latter case that the localization length can depend nonmonotonically on the strength of phase disorder (whereas expanding in weak disorder yields monotonic decrease). More generally, we obtain to all orders in the expansion a particular nonseparable form for the joint probability distribution of the transmission coefficient logarithm and reflection phase. Furthermore, we show that for weak local reflection strength, a version of the scaling theory of localization holds: the joint distribution is determined by just three parameters.


“What action should l take?”: Help-seeking behaviours of those targeted by romance fraud

Abstract: Fraud uses deception to obtain a financial reward from its victim. There are strong negative stereotypes and victim blaming discourses towards fraud victims, with research indicating that fraud victims are unlikely to report incidents for many reasons, including a high degree of shame and stigmatisation. In this way, it can be assumed that fraud victims are averse to seeking help in the aftermath of an incident. Drawing from romance fraud reports lodged with Scamwatch (an Australian online reporting portal for fraud) between 1 July 2018 and 31 July 2019, this article examines the help-seeking behaviours of those targeted by romance fraud. The analysis found a variety of explicit and implicit help-seeking behaviours, encompassing both personal justice and altruistic reasons. The analysis demonstrates that despite the victim blaming that exists, this did not always act as a barrier to reporting or requesting help. Overall, this article challenges some of the perceived aversity to help-seeking behaviour of fraud victims. It also provides a foundation for improving responses and support to those who actively engage in help-seeking behaviour.


Ultra-early medical treatment-oriented system identification using High-Dimension Low-Sample-Size data

Abstract: Ultra-early detection of diseases with High-Dimension Low-Sample-Size (HDLSS) data has been effectively addressed by the Dynamical Network Biomarkers (DNBs) theory. After ultra-early detection, it is crucial to consider ultra-early medical treatment for the detected disease. From the viewpoint of control engineering, ultra-early medical treatment is achieved by increasing the system’s stability and preventing the bifurcation, called re-stabilization. To implement effective re-stabilization, the system matrix is necessary. However, the available data in biological systems are often HDLSS, which is insufficient to identify the system matrix. In this paper, to realize HDLSS-based ultra-early medical treatment, we investigate an HDLSS data-based system matrix estimation method. First, HDLSS data is applied to compute the sample covariance matrix of the steady state. By assuming that the system matrix is sparse and the structure of the system matrix is known, it can utilize the Lyapunov equation to estimate the system matrix from the covariance matrix. The Lyapunov equation-based method gives a unique optimal estimation if the covariance matrix is full-rank. Otherwise, the optimal estimation is not unique. The sample covariance matrix computed from the HDLSS data is not full-rank. Thus, we apply shrinkage estimation to overcome the under-determined issue to obtain a well-conditioned covariance matrix with full rank. In addition, we confirm the effectiveness of the proposed method through numerical simulations.


Goal-conditioned offline reinforcement learning through state space partitioning

Abstract: Abstract Offline reinforcement learning (RL) aims to create policies for sequential decision-making using exclusively offline datasets. This presents a significant challenge, especially when attempting to accomplish multiple distinct goals or outcomes within a given scenario while receiving sparse rewards. Prior methods using advantage weighting for offline goal-conditioned learning improve policies monotonically. However, they still face challenges from distribution shift and multi-modality that arise due to conflicting ways to reach a goal. This issue is especially challenging in long-horizon tasks, where the presence of multiple, often conflicting, solutions makes it hard to identify a single optimal policy for transitioning from a state to a desired goal. To address these challenges, we introduce a complementary advantage-based weighting scheme that incorporates an additional source of inductive bias. Given a value-based partitioning of the state space, the contribution of actions expected to lead to target regions that are easier to reach, compared to the final goal, is further increased. Our proposed approach, Dual-Advantage Weighted Offline Goal-conditioned RL, outperforms several competing offline algorithms in widely used benchmarks. Furthermore, we provide a theoretical guarantee that the learned policy will not be inferior to the underlying behavior policy.


Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues

Abstract: The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.


An oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy

Abstract: Hybrid data lead to overfitting in machine learning models, which may reduce the accuracy of classification. Feature selection can not only reduce the computational cost of processing hybrid data but also improve the accuracy of classification. The particle swarm optimization (PSO) algorithm has clear advantages in feature selection. This paper presents an oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy. First, a new distance function on the object set of a hybrid information system (HIS) is built, which yields a tolerance relation on this object set. Then, mutual information entropy is presented to measure the uncertainty of the HIS. On this basis, the maximum-relevance and minimal-redundancy model (MRMR model) for the HIS is proposed. Based on the MRMR model, a feature selection algorithm (denoted as MRMR) for the HIS is naturally designed. As the integration of the MRMR model and PSO can effectively explore all possible feature subsets, an oscillatory particle swarm optimization algorithm based on the MRMR model (denoted as OPSO-MRMR) for the HIS is also designed. Moreover, the MRMR model is utilized to define a fitness function that evaluates the quality of particles. The particle position update process is modified by means of a two-order oscillatory equation. Finally, an experimental analysis is conducted to compare the two designed algorithms with five other algorithms. The statistical analysis of classification accuracy and F1 score shows that OPSO-MRMR improves precision by 5.8% and 10.7% compared to the other six algorithms, respectively.