Reviewerly/exHarmony huggingface dataset:
Reversible Data Hiding Using an Improved Pixel Value Ordering and Complementary Strategy

Reversible data hiding (RDH) schemes based on pixel value ordering have gained significant popularity due to their unique capability of providing high-quality marked images with a decent embedding capacity, while also enabling secret information extraction and the lossless recovery of the original images at the receiving side. However, the marked image quality may be distorted severely when the pixel value ordering (PVO) method is employed in a layer-wise manner to increase the embedding capacity. In this paper, a new high-capacity RDH scheme using a complementary strategy is introduced to overcome the limitation of the image quality in the case of layer-wise embedding. The proposed RDH scheme first divides the cover image into non-overlapping blocks of 2 × 2 pixels uniformly and then sorts the pixels of each block according to their intensity values. The secret data are then embedded into two layers. In the first layer, the minimum value of the block is decreased and the maximum value is increased by either 1 or 2 to embed the secret data bits. The second layer is used as a complement to the first layer and is in symmetry with the first layer. In the second layer, the proposed RDH scheme increases the minimum valued pixel and decreases the maximum valued pixel in order to minimize the distortion resulting from the first layer embedding and to embed an additional amount of the secret data. As a result, the proposed RDH scheme significantly increases the embedding capacity, which is clearly evident from the conducted experimental results.
The influence of cognitive level on the guaranteed behavioral response of landless farmers in the context of rural revitalization–An empirical study based on partial least squares structural equation modeling

With the continuous acceleration of urbanization and agricultural modernization in China, the trend of concentration of rural land transfer is irreversible. For landless farmers, the absence of land guaranteed function inevitably gives rise to the substitution effect of other guaranteed methods. And the subjective preferences exhibited by farmers in making guaranteed behavior decisions can be quantitatively described as guaranteed behavioral responses (GBRs). Based on the analytical framework of distributed cognitive theory, this paper adopts the validated factor analysis method of structural equation modeling to quantitatively study the cognitive basis and behavioral responses of landless farmers' guaranteed behavior by combining the survey data of rural households in typical rural areas of Wuhan urban area. The study shows that the GBRs of landless farmers are significantly influenced by the cognitive level. "Locality power," "cultural power," and "personal power" are the main, important, and effective cognitive levels that influence farmers' GBRs, respectively. Policy-based protection occupies a dominant position in the rural social guaranteed system, savings-based protection still plays an important function in rural areas, and market-based protection has greater development potential.
Time-reversal equivariant neural network potential and Hamiltonian for magnetic materials

This work presents Time-reversal Equivariant Neural Network (TENN) framework. With TENN, the time-reversal symmetry is considered in the equivariant neural network (ENN), which generalizes the ENN to consider physical quantities related to time-reversal symmetry such as spin and velocity of atoms. TENN-e3, as the time-reversal-extension of E(3) equivariant neural network, is developed to keep the Time-reversal E(3) equivariant with consideration of whether to include the spin-orbit effect for both collinear and non-collinear magnetic moments situations for magnetic material. TENN-e3 can construct spin neural network potential and the Hamiltonian of magnetic material from ab-initio calculations. Time-reversal-E(3)-equivariant convolutions for interactions of spinor and geometric tensors are employed in TENN-e3. Compared to the popular ENN, TENN-e3 can describe the complex spin-lattice coupling with high accuracy and keep time-reversal symmetry which is not preserved in the existing E(3)-equivariant model. Also, the Hamiltonian of magnetic material with time-reversal symmetry can be built with TENN-e3. TENN paves a new way to spin-lattice dynamics simulations over long-time scales and electronic structure calculations of large-scale magnetic materials.
Do Users Ever Draw a Line? Offensiveness and Content Moderation Preferences on Social Media

When is content on social media offensive enough to warrant content moderation? While social media platforms impose limits to what can be posted, we know little about where users draw the line when it comes to offensive language, and what measures they wish to see implemented when content crosses the boundary of what is deemed acceptable. Conducting randomized experiments with over 5,000 participants we study how different types of offensive language causally affect users' content moderation preferences. We quantify causal effects of uncivil, intolerant, and threatening language by randomly introducing these aspects into fictitious social media posts targeting various social groups. While overall there is limited demand for action against offensive behavior, the severity of the attack matters to the average participant. Amongst our treatments, violent threats cause the greatest support for content moderation of various types, including punishments that would be viewed as censorship in some contexts, such as taking down content or suspending accounts.
A short utterance speaker recognition method with improved cepstrum–CNN

Abstract In this study, an improved cepstrum-convolutional neural network is proposed, which can solve the problem of low recognition accuracy of 1-s short utterance in speaker recognition technology. The audio feature Mel frequency cepstrum coefficient is extracted by using the improved cepstrum algorithm and the data of the two-dimensional acoustic feature vector matrix is preprocessed to convert the two-dimensional feature matrix into a three-dimensional tensor as the input data of the two-dimensional convolutional neural network model. Experiments are carried out on an Arabic digital English pronunciation dataset with an audio duration of less than one second in a specific experimental environment. Moreover, the performance of this model is evaluated by accuracy and F1-score. The simulation results show that the accuracy of our proposed model for speech recognition is as high as 100% and 99.60% on the training and test sets, respectively, as well as the F1- score, is 0.9985. It can be seen that the recognition method of this model solves the problem of accuracy degradation of short utterance speaker recognition due to the short duration of the corpus and improves the accuracy of short speech voice recognition. The model is simple but effective, generalization, superior, and has higher practical application value. Article Highlights. It is interesting to study how to improve the accuracy of 1-s short utterance speaker recognition. The improved cepstrum algorithm can solve the problem of not extracting enough discernible acoustic features. This paper proposed model obtained 100% accuracy on a spoken Arabic digit dataset with an audio duration about 0.3 s.
Experimental Detection of Initial System–Environment Entanglement in Open Systems

We experimentally investigate how initial entanglement between the system and environment can be detected in an open system by using some prior knowledge of the joint evolutions. The protocol we employed requires classical optimization on the results after performing measurements on the system state. Such an approach does not require a full 2-qubit QST, and works in scenarios where one has access to the system only. We demonstrate the protocol on both pure entangled states and mixed entangled states. The obtained results show the experimental accessibility and validity of the protocol. Compared with the previous methods, which also assume access only to the system, this protocol is less demanding in terms of measurement and state preparation. The experimental results also show that, using the knowledge of the interaction, we can fine-tune the protocol, thus showing the potential of the protocol for developing experimentally feasible and practical entanglement detection methods.
A New Simple Chaotic System with One Nonlinear Term

In this research article, a simple four-dimensional (4D) chaotic dynamic system with uncomplicated structure and only one nonlinear term is introduced. The features of the proposed design have been conducted with some standard nonlinear dynamic analysis and mathematical tools which show the chaotic nature. One of the most important indicators for detecting complexity of the chaotic systems is the Kaplan-York dimension of the system. Moreover, one of the main criteria of chaotic systems is its simplicity due to the reduction of operating costs. Therefore, it seems necessary to design a system as simple as possible and with high complexity. In this research, a comparison has been made between the proposed system and similar chaotic systems, which has given noticeable results. For the practical implementation of the proposed design, the circuit analysis using Multisim software has been employed. The proposed scheme has been used in the application of image encryption to show the efficiency of the proposed chaotic system and standard encryption tests have been performed. The rest of the numerical results have been conducted using MATLAB/Simulink software.
A Lightweight Border Patrol Object Detection Network for Edge Devices

Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection.
Collaborative Gold Mining Algorithm: An Optimization Algorithm Based on the Natural Gold Mining Process

In optimization algorithms, there are some challenges, including lack of optimal solution, slow convergence, lack of scalability, partial search space, and high computational demand. Inspired by the process of gold exploration and exploitation, we propose a new meta-heuristic and stochastic optimization algorithm called collaborative gold mining (CGM). The proposed algorithm has several iterations; in each of these, the center of mass of points with the highest amount of gold is calculated for each miner (agent), with this process continuing until the point with the highest amount of gold or when the optimal solution is found. In an n-dimensional geographic space, the CGM algorithm can locate the best position with the highest amount of gold in the entire search space by collaborating with several gold miners. The proposed CGM algorithm was applied to solve several continuous mathematical functions and several practical problems, namely, the optimal placement of resources, the traveling salesman problem, and bag-of-tasks scheduling. In order to evaluate its efficiency, the CGM results were compared with the outputs of some famous optimization algorithms, such as the genetic algorithm, simulated annealing, particle swarm optimization, and invasive weed optimization. In addition to determining the optimal solutions for all the evaluated problems, the experimental results show that the CGM mechanism has an acceptable performance in terms of optimal solution, convergence, scalability, search space, and computational demand for solving continuous and discrete problems.
Building Natural Language Interfaces Using Natural Language Understanding and Generation: A Case Study on Human–Machine Interaction in Agriculture

The human–machine interaction of existing agricultural measurement and control platforms lacks user-friendliness and requires manual operation by trained professionals. The recent development of natural language processing technology may bring some interesting changes. We propose a pipeline for building a natural language human–machine interaction interface to provide a better interaction for agricultural measurement and control platforms. Our construction process uses a new method of collecting training data based on the dynamic tuple language framework to synthesize natural language commands entered by the user into structured AOM statements (Action-Object-Member). To construct a mapping of the human–machine interface from natural language commands to AOM invocations, we propose an end-to-end framework that uses a special mask mechanism to improve the BERT-based Seq2Seq model to capture global sequence relations. Experimental results of data collection methods and NL2AOM demonstrate that our pipeline has good performance and a reasonable response time. Finally, we developed desktop and mobile platform applications based on the proposed model and used them in real agricultural scenarios.
Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition

More than four million people worldwide suffer from hearing loss. Recently, new CNNs and deep ensemble-learning technologies have brought promising opportunities to the image-recognition field, so many studies aiming to recognize American Sign Language (ASL) have been conducted to help these people express their thoughts. This paper proposes an ASL Recognition System using Multiple deep CNNs and accuracy-based weighted voting (ARS-MA) composed of three parts: data preprocessing, feature extraction, and classification. Ensemble learning using multiple deep CNNs based on LeNet, AlexNet, VGGNet, GoogleNet, and ResNet were set up for the feature extraction and their results were used to create three new datasets for classification. The proposed accuracy-based weighted voting (AWV) algorithm and four existing machine algorithms were compared for the classification. Two parameters, α and λ, are introduced to increase the accuracy and reduce the testing time in AWV. The experimental results show that the proposed ARS-MA achieved 98.83% and 98.79% accuracy on the ASL Alphabet and ASLA datasets, respectively.
A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks

The target tracking of unmanned aerial vehicles (UAVs) has attracted significant attention recently with the increasing application of UAVs, yet few studies have made breakthroughs in dynamic dim target detection. How to efficiently and accurately identify dynamic dim targets in complex contexts poses a challenge. To address this issue, we proposed an improved lightweight Siamese network (ILSN) with an optimized feature-extraction network design and similarity measurement.As for the feature-extraction network, we built a position-wise attention module to obtain the target feature’s position information, which enhanced the network’s ability to extract weak targets while reducing the model parameters, thus ensuring the network is lightweight. For the similarity-measurement module, the tracking accuracy was expected to be improved by deeply mining the localization information of the shallow features and the semantic information of the deep features in the feature networks. To evaluate the performance of the proposed method, we established a simulated experimental environment and a physical experimental platform and then carried out comparison experiments on the attention modules, tracking accuracy, and efficiency performance. The experimental results showed that, compared with the five introduced comparison algorithms, the ILSN had apparent advantages in tracking accuracy: the tracking speed reached 108 frames per second, which met the real-time requirements while improving the success rate.
Decoherence Effects in a Three-Level System under Gaussian Process

When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is investigated further in the noiseless and noisy regimes. In particular, we consider fractional Gaussian, Gaussian, Ornstein–Uhlenbeck, and power law noisy regimes. We show that the destructive nature of the Ornstein–Uhlenbeck noise toward the symmetry of the qutrit to preserve encoded purity and coherence remains large. Our findings suggest that properly adjusting the noisy parameters to specifically provided values can facilitate optimal extended purity and coherence survival. Non-vanishing terms appear in the final density matrix of the single qutrit system, indicating that it is in a strong coherence regime. Because of all of the Gaussian noises, monotonic decay with no revivals has been observed in the single qutrit system. In terms of coherence and information preservation, we find that the current qutrit system outperforms systems with multiple qubits or qutrits using purity and von Neumann entropy. A comparison of noisy and noiseless situations shows that the fluctuating nature of the local random fields is ultimately lost when influenced using the classical Gaussian noises.
Explainable Decision-Making for Water Quality Protection

All professional decisions prepared for a specific stakeholder can and must be explained. The primary role of explanation is to defend and reinforce the proposed decision, supporting stakeholder confidence in the validity of the decision. In this paper we present the methodology for explaining results of the evaluation of alternatives for water quality protection for a real-life project, the Upper Neuse Clean Water Initiative in North Carolina. The evaluation and comparison of alternatives is based on the Logic Scoring of Preference (LSP) method. We identify three explainability problems: (1) the explanation of LSP criterion properties, (2) the explanation of evaluation results for each alternative, and (3) the explanation of the comparison and ranking of alternatives. To solve these problems, we introduce a set of explainability indicators that characterize properties that are necessary for verbal explanations that humans can understand. In addition, we use this project to show the methodology for automatic generation of explainability reports. We recommend the use of explainability reports as standard supplements for evaluation reports containing the results of evaluation projects based on the LSP method.
Influence of Insufficient Dataset Augmentation on IoU and Detection Threshold in CNN Training for Object Detection on Aerial Images

The objects and events detection tasks are being performed progressively often by robotic systems like unmanned aerial vehicles (UAV) or unmanned surface vehicles (USV). Autonomous operations and intelligent sensing are becoming standard in numerous scenarios such as supervision or even search and rescue (SAR) missions. The low cost of autonomous vehicles, vision sensors and portable computers allows the incorporation of the deep learning, mainly convolutional neural networks (CNN) in these solutions. Many systems meant for custom purposes rely on insufficient training datasets, what may cause a decrease of effectiveness. Moreover, the system's accuracy is usually dependent on the returned bounding boxes highlighting the supposed targets. In desktop applications, precise localisation might not be particularly relevant; however, in real situations, with low visibility and non-optimal camera orientation, it becomes crucial. One of the solutions for dataset enhancement is its augmentation. The presented work is an attempt to evaluate the influence of the training images augmentation on the detection parameters important for the effectiveness of neural networks in the context of object detection. In this research, network appraisal relies on the detection confidence and bounding box prediction accuracy (IoU). All the applied image modifications were simple pattern and colour alterations. The obtained results imply that there is a measurable impact of the augmentation process on the localisation accuracy. It was concluded that a positive or negative influence is related to the complexity and variability of the objects classes.
Complement Class Fine-Tuning of Naïve Bayes for Severely Imbalanced Datasets

In this work, we adapt the fine-tuning algorithm of the Naïve Bayesian (FTNB) classifier to make it more suitable for imbalanced datasets. In particular, we boost misclassified instance probability terms by an amount that is disproportional to the harmonic mean of actual and predicted classes. The intuition is that discriminative attributes when the instance is misclassified would have small probability term pair values in both the actual class due to data scarcity and the predicted class due to weak correlation. Conversely, if both values are relatively high, then the attribute has good data coverage (support) and it should not be a cause for misclassification. Since the harmonic average is dominated by the smaller value and we have an imbalanced dataset, we should enact a large update if both or either term probabilities of actual and predicted classes are small. We used several benchmark datasets (60 different balanced and imbalanced datasets) to determine if the poor performance of the NB classifier is due to the scarcity of data and compared the performance of the proposed algorithm with NB, original FTNB, and other relatively new SOTA Ensemble Imbalanced Classifiers. Our empirical results reveal that the new proposed algorithm significantly outperforms all other classifiers.
Bale Collection Path Planning Using an Autonomous Vehicle with Neighborhood Collection Capabilities

This research was mainly focused on the evaluation of path planning approaches as a prerequisite for the automation of bale collection operations. A comparison between a traditional bale collection path planning approach using traditional vehicles such as tractors, and loaders with an optimized path planning approach using a new autonomous articulated concept vehicle with neighborhood reach capabilities (AVN) was carried out. Furthermore, the effects of carrying capacity on reduction in the working distance of the bale collection operation was also studied. It was concluded that the optimized path planning approach using AVN with increased carrying capacity significantly reduced the working distance for the bale collection operation and can thus improve agricultural sustainability, particularly within forage handling.