Reviewerly/exHarmony huggingface dataset:
Hash-based cryptography, its security and feasibility in modern cryptosystems

Hash-based signatures are one of the most promising classes of cryptographic schemes considered quantum resistant ones. The strength of cryptographic hash functions is one of the most important aspects of ensuring the security of hash-based schemes.&#x0D; Since classical hash-based signatures require tracking the number of signatures used, they were considered to be stateful for a long time. The SPHINCS scheme overcome this limitation, subsequently refined to SPHINCS+.&#x0D; The paper provides an assessment of the security of ES based on hash functions relative to side channel attacks.It also gives an analysis of recommendations for the use of one of the candidates of the NIST competition, based on hash cryptography - SPHINCS+, and conclusions about the feasibility of its use.
CPML: Category Probability Mask Learning for Fine-Grained Visual Classification

Fine-Grained Visual classification (FGVC) is a fundamental problem in computer vision. FGVC is determined by subtle appearance difference of local parts, which thus inspires many part-based methods. Different from attention-based methods and other part-based methods, we propose a novel Category Probability Mask Learning (CPML) module to discover nuanced local differences and mitigate cluttered backgrounds. Meanwhile, the CPML is a simple and efficient module, which can be applied to both convolution neural networks and vision transformers to enhance the ability of feature representation. In addition, we utilize a Category Consistency Loss (CCL) to promote the robustness and discrimination of learned backbone deep features. It is worth mentioning that we only use global branch at test stage because the global feature is already regularized by the part features with CPML and CCL in training steps. Compared with current state-of-the-art methods, our methods achieve promising performance on three widely used FGVC datasets.
A Coverless Image Steganography Method Based on Feature Matrix Mapping

To address the challenge that the current coverless image steganography (CIS) method requires more cover images and more information, a coverless image steganography method based on feature matrix mapping is proposed. In this paper, the low-frequency component of the Discrete Wavelet Transform (DWT) is used as the image eigenvalue, and a robust feature sequence is obtained by comparing the size of the main and minor diagonal blocks with the eigenvalues of other blocks. This paper proposes a feature matrix mapping rule to obtain the mapping matrix of the cover image and the secret information through the feature matrix mapping. The transmission of secret information is achieved through the cover image and the mapping matrix, which solves the problem of large transmission overhead and the need for more cover images and additional information. Experimental results show that this scheme has certain advantages in terms of the number of mapped images and additional information required, and has better security against most image attacks.
Deep Neural Network-based Reversible Image Steganography Technique using Circle-U-Net

Data communication is made at the ease with the advent of the latest communications medium and tools. The concern over data breaches has increased. The digital media communicated across the network are susceptible to unapproved access. Though numerous image steganography approaches were existing for concealing the secret image into the cover image there are still limitations such as inadequate restoration of image quality and less embedding capacity. To overwhelm such shortcomings recently many image steganography approaches based on deep learning are proposed. In this work, a Circle-U-Net-based reversible image steganography technique is proposed. The model includes a contracting process, which includes residual bottleneck as well as circle connect layers which obtain context; an expanding process, which includes sampling layers as well as merging layers for pixel-wise localization. The reversible image steganography (RIS) is carried out with neural network models such as CNN, U-Net scheme, and Circle-U-Net structure on TinyImageNet-200 and Alzheimer’s MRI dataset. The proposed technique is experimented along with RIS using CNN and RIS using U-Net. The experimental results depict that the RIS using the Circle-U-Net structure performs better among the three models.
Lifelong-MonoDepth: Lifelong Learning for Multidomain Monocular Metric Depth Estimation

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning (LL) models capable of estimating metric (absolute) depth. LL approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to LL scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: 1) developing a model capable of addressing the depth scale variation through scale-aware depth learning; 2) devising an effective learning strategy to handle significant domain gaps; and 3) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this article, we present 1) a lightweight multihead framework that effectively tackles the depth scale imbalance; 2) an uncertainty-aware LL solution that adeptly handles significant domain gaps; and 3) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8%-15%. The code is available at https://github.com/FreeformRobotics/Lifelong-MonoDepth.
The BeMi Stardust: A Structured Ensemble of Binarized Neural Networks

Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices, given the fact that they can be implemented using Boolean operations. The state-of-the-art for training classification BNNs restricted to few-shot learning is based on a Mixed Integer Programming (MIP) approach. This paper proposes the BeMi ensemble, a structured architecture of classification-designed BNNs based on training a single BNN for each possible pair of classes and applying a majority voting scheme to predict the final output. The training of a single BNN discriminating between two classes is achieved by a MIP model that optimizes a lexicographic multi-objective function according to robustness and simplicity principles. This approach results in training networks whose output is not affected by small perturbations on the input and whose number of active weights is as small as possible, while good accuracy is preserved. We computationally validate our model using the MNIST and Fashion-MNIST datasets using up to 40 training images per class. Our structured ensemble outperforms both BNNs trained by stochastic gradient descent and state-of-the-art MIP-based approaches. While the previous approaches achieve an average accuracy of $$ 51.1\%$$ on the MNIST dataset, the BeMi ensemble achieves an average accuracy of $$61.7\%$$ when trained with 10 images per class and $$76.4\%$$ when trained with 40 images per class.
Convex Optimization-Based Trajectory Planning for Quadrotors Landing on Aerial Vehicle Carriers

This paper presents a novel trajectory planning algorithm for quadrotors landing on aerial vehicle carriers (AVCs). The algorithm involves a quadrotor trajectory planning method based on the lossless convexification (LC) theory and a sequential convex programming (SCP) method enabling quadrotors to autonomously land on both static and moving AVCs in a three-dimensional space. By incorporating landing cone constraints, the safety of the quadrotor during landing is ensured. The LC method transforms the original nonconvex optimal control problem (OCP) into a convex optimization problem, enabling the efficient computation of a 3-degree-of-freedom (3-DoF) safe landing trajectory. The designed SCP algorithm utilizes the 3-DoF trajectory as an initial guess and iteratively solves convex subproblems to obtain a safe, agile, and accurate landing trajectory for the complete 6-DoF quadrotor dynamics. Real-world experiments validate the effectiveness and real-time performance of the proposed method.
Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.
Dynamic Police Patrol Scheduling with Multi-Agent Reinforcement Learning

Effective police patrol scheduling is essential in projecting police presence and ensuring readiness in responding to unexpected events in urban environments. However, scheduling patrols can be a challenging task as it requires balancing between two conflicting objectives namely projecting presence (proactive patrol) and incident response (reactive patrol). This task is made even more challenging with the fact that patrol schedules do not remain static as occurrences of dynamic incidents can disrupt the existing schedules. In this paper, we propose a solution to this problem using Multi-Agent Reinforcement Learning (MARL) to address the Dynamic Bi-objective Police Patrol Dispatching and Rescheduling Problem (DPRP). Our solution utilizes an Asynchronous Proximal Policy Optimization-based (APPO) actor-critic method that learns a policy to determine a set of prescribed dispatch rules to dynamically reschedule existing patrol plans. The proposed solution not only reduces computational time required for training, but also improves the solution quality in comparison to an existing RL-based approach that relies on heuristic solver.
Statistical errors in reduced density matrices sampled from quantum circuit simulation and the impact on multireference perturbation theory

In this work, we present a detailed analysis of statistical errors in reduced density matrices (RDMs) of active space wavefunctions sampled from quantum circuit simulation and the impact on results obtained by the multireference theories. From the sampling experiments, it is shown that the errors in sampled RDMs have a larger value for higher-order RDMs, and that the errors in sampled RDMs for excited states are larger than those for the ground state. We analytically derive the expected value of the sum of squared errors between the true distribution and sample distribution of weights of the electron configurations based on a multinomial distribution model, with which we present an assessment of the dependency of RDM errors on the number of shots for the observation (Nshot) and on the character of the target electronic state. With the benchmark calculations of short polyenes, C4H6 and C6H8, we report the statistical errors in CASCI and complete active space second-order perturbation theory (CASPT2) energies obtained with the sampled 1,2-RDMs and 1,2,3,4-RDMs, respectively. It was found that the standard deviation (SD) of the sampled CASCI energies is proportional to as predicted. It was also found that the dependence of the SD of the second-order correction energies are somewhat different but the errors in the reference CASCI energies are dominant as compared with the corrections and the SD of the resulting CASPT2 energies are proportional to . This suggests that the required Nshot for 3,4-RDMs used only in the second-order perturbative corrections is smaller than that for 1,2-RDM used to calculate the reference CASCI energies. It was also suggested from the analysis of the errors in the sampled energies that the required Nshot for 3-RDM, which is used to calculate the perturbative correction energies, can be smaller than that for 2-RDM to calculate the CASCI energies. In fact, it was shown that the potential energy curve along the isomerization reaction of the {[Cu(NH3)3]2O2}2+ complex as an archetype of metalloenzyme, in which static and dynamical electron correlations are both important, can be reasonably reproduced with Nshot = 106 for 1,2-RDMs but Nshot = 105 for 3-RDM by the sampling simulation.
Research on the construction of intelligent dance teaching ecosystem in universities based on multi-data chain network

Abstract In this paper, the Link16 data chain is used to cycle dance movement teaching resources by time and build the function of different nodes of dance teaching to form a distributed network topology without a central node. The network synchronization method integrates and updates the latest dance teaching resources. The MSK demodulation method is used to regulate the multi-data network information, and the network synchronization sequence is constructed by setting the relevant performance of the synchronization code and the judgment threshold. The motion error optimization algorithm reduces motion errors during high-speed dance. The results show that the variance of each classroom in the dance ecological teaching system is within 0.40, with minor fluctuations. In the evaluation results of the three evaluators, the scores of scientific knowledge were all 5, and the scores of ability, attitude and value were slightly different, but the variance of the total score and each index was under 0.10.
Construction of a secure storage and sharing model for medical data under computer network technology

Abstract This paper constructs a secure storage and sharing model of medical data based on the blockchain technology of computer network technology, and the model mainly designs the encryption process, storage process and sharing process of medical data. The model combines asymmetric encryption algorithm and ciphertext access strategy and designs a hybrid encryption method for secure storage and sharing of medical data through three stages: public parameters and key generation, encryption and decryption. The comprehensive performance of the constructed model is tested, and the security performance indexes are 0.998, 0.989, 0.996 and 0.993 and the execution time of the sharing mechanism is 1~16s, the data upload time is 1200ms, and the data download time is only 200ms.
Kinect-based gesture interaction design method for smart terminal APP interface

Abstract In this paper, a static gesture classification model based on a convolutional neural network is proposed to achieve the goal of efficient interaction control and fast response time for gesture recognition, and hand feature extraction is achieved by using alternating convolution and pooling. In the convolutional neural network, image data is obtained by preprocessing operations such as adaptive thresholding and functions such as Gaussian filtering to provide data for the convolutional neural network structure to build the recognition model, which is used to quantify the edge characteristics of the image and construct the image edges with gradient amplitude to build the static gesture recognition algorithm for Kinect. In the accuracy rate for gesture interaction, the average recognition rate for slow movement is 96.5%, while when the interactive gesture is recognized at an accelerated speed, its recognition rate slips to 93.1%, and the total average recognition rate reaches 95.2%.
The cultivation path of vocational talents in rural revitalization industry based on multidimensional data analysis method in the context of industry-education integration

Abstract This paper firstly analyzes the demand for industrial technical talents and industrial vocational talents training specifications for rural revitalization and proposes a vocational talents training model for the rural revitalization industry based on the integration of industry and education. Secondly, a multidimensional talent training evaluation index system is constructed, and then a network hierarchy analysis of the evaluation index system is conducted using algorithms such as evaluation matrix, entropy value method and game theory combination assignment. Finally, quantitative data analysis was carried out on the indicator weights and the evaluation of indicators using County A of City B as an example. The results show that the overall score of the talent cultivation index evaluation system in County A is 79.96, among which the evaluation scores of first-level indicators are 16.73, 10.56, 21.42, 7.89, 17.06 and 6.30.
A deepening study of philosophical cognitive logic based on intelligent interaction

Abstract This paper analyzes the research object of cognitive logic in the context of information technology and philosophical reflection and constructs a model of cognitive logic for contextual dynamics. Contextual recognition of philosophical cognitive logic is achieved through contextual sequence feature extraction, and realistic, intelligent interaction is analyzed through probabilistic potential semantic analysis, while philosophical cognitive logic deepening is achieved using PELP grammar. The analysis of the deepening model showed that the interaction degree of the mental dimension increased from 0.79 to 0.91, and the interaction degree of the thinking dimension increased from 0.88 to 0.97. Applying the model to learning, the difference between learning methods without the model was up to 15 points, and the deepening of philosophical cognitive logic based on intelligent interaction could improve thinking ability.
Research on intelligent soccer teaching and training model integrating virtual reality technology

Abstract This paper utilizes virtual reality technology to enhance players’ skills and tactical awareness in a soccer training system. The article first designs a soccer training system incorporating virtual reality technology, which contains key technologies such as common stance representation of soccer actions, stance solving algorithm and fusion Hausdorff Pearson algorithm. Then, the effect of virtual reality technology on soccer training was analyzed, including the impact on overall performance and players’ tactical awareness, and finally, a comparative analysis of the experimental and control groups was conducted. The results showed that the overall performance of the experimental group in the post-test was 22.08±6.37, and the overall performance of the control group in the post-test was 16.82±5.21. Regarding offensive passing and awareness of running without the ball, there was a significant difference between the experimental and control groups at P&lt;0.05. Regarding co-defense complementary and convergence awareness from offense to defense, there was a highly significant difference between the experimental and control groups, with p&lt;unk&gt;0.01.
Optimization Model Construction of Online Public Opinion Dissemination Based on Behavioral Data Mining

Abstract In this paper, we propose an optimization method based on behavioral data mining for the emergence of negative emotions in network public opinion in unexpected situations leading to problems that endanger social stability and use data mining to confirm the correspondence of the kNN algorithm. In the opinion propagation model, the radius of opinion radiation is assumed to use the network node density and distribution density as features of node exchange information. Then, the kNN algorithm is used to train the comment set for the analysis of user sentiment evolution of network opinion in the social network environment, and the web crawler technology is used to obtain the output interface data APIs of microblogs and WeChat, and the social media user comment data is used as the data for the empirical analysis of network opinion. snowNLP and kNN algorithms are used to analyze the sentiment score of network opinion and the sentiment score less than 0.5, i.e., the sentiment polarity. There were 15 days when the sentiment score tended to be negative and 65 days when the sentiment score was greater than 0.5, i.e., the sentiment polarity tended to be positive.
Exploring the innovation path of daily management mode of college students in the new era based on big data technology

Abstract This paper uses big data technology to focus on the clustering and correlation analysis of students’ daily behavior. The daily management workflow of students is constructed by analyzing the main algorithms of big data technology and the application scope. The association degree rules are combined to calculate students’ associated behaviors’ minimum support and confidence degree. The clustering algorithm was used to classify the daily management patterns of students into learning, closed, and active types. The student daily management clustering process was improved by using Newton interpolation to improve the inheritance and compatibility of node changes. In the correlation analysis, the support of the association label for regular type 1 was 0.441 in support and 0.852 in confidence, and the support of more regular type 2 was 0.425 in support and 0.846 in confidence.
Analysis of the adaptability of constructing a system for helping poor students in colleges and universities in the context of big data

Abstract This paper first analyzes the procedure process and needs of identifying poor students in colleges and universities and constructs a working system to help them. Secondly, it analyzes the evaluation process of the comprehensive system for assisting poor students and constructs an evaluation index system for their aid work. Then, the fuzzy comprehensive evaluation was established to comprehensively evaluate the work of poor students’ aid, and the results were tested using the EDA model. Finally, the quantitative analysis of index weights and satisfaction was conducted with the example of H University. The results showed that, from the index weights, the index weight of the identification of poor students was the largest, 0.3294, and the weights of the construction of rules and regulations and work effectiveness were the smallest, 0.0838 and 0.0178, respectively.