Reviewerly/exHarmony huggingface dataset:
MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images

Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information. This paper proposes a one-stage monocular 3D object detection network (MDS Net), which uses the anchor-free method to detect 3D objects in a per-pixel prediction. Firstly, a novel depth-based stratification structure is developed to improve the network's ability of depth prediction, which exploits the mathematical relationship between the size and the depth in the image of an object based on the pinhole model. Secondly, a new angle loss function is developed to further improve both the accuracy of the angle prediction and the convergence speed of training. An optimized Soft-NMS is finally applied in the post-processing stage to adjust the confidence score of the candidate boxes. Experiment results on the KITTI benchmark demonstrate that the proposed MDS-Net outperforms the existing monocular 3D detection methods in both tasks of 3D detection and BEV detection while fulfilling real-time requirements.
The structure and content of the educational environment in the process of professional training of future pedagogues

Today, the problem of studying the phenomenon of the educational environment is becoming more and more topical, since it is one of the components of the educational process. The article describes the results of a study aimed at analysing the state of the educational environment of the university. The study is based on an online questionnaire survey of three respondent groups – students, teachers, and experts (heads of departments responsible for the educational programmes) of Kostroma State University. Special attention was paid to the components and indicators of the educational environment of the university. According to the results of the study, a number of problems were identified with the main directions of work formulated within the framework of making a unified educational environment of the University for pedagogic areas of training.
An Image Encryption Algorithm Based on an Epidemic Spreading Model

An intrinsic similarity was noted between epidemic spreading models and diffusion operations in cryptography. In this paper, a new image encryption algorithm is proposed based on an epidemic spreading model. The notable feature of the model is the evolution of the network node state. We utilize the evolution of an epidemic spreading model, namely the susceptible-infectious-recovered (SIR) model, to drive the encryption progress by abstracting pixels to the network nodes. In the new cryptosystem, the pixels in plain-text are considered as susceptible individuals, permutation and diffusion are seamlessly integrated rather than having two independent stages, and the encryptions are performed in a two-dimensional and nonlinear manner. Simulation results demonstrate its high encryption speed and excellent encryption security.
3D Human Pose Estimation Based on Transformer Algorithm

Human pose estimation (HPE) is a fundamental problem in computer vision, and it is also the basis of applied research in many fields, which can be used for virtual fitting, fashion analysis, behavior analysis, human-computer interaction, and auxiliary pedestrian detection. The purpose of HPE is to use image processing and machine learning methods to find out the positions and types of joints of people in pictures. There are two main difficulties in HPE. First, the complex human images make the model need to learn a highly nonlinear mapping relationship, and the learning of this mapping relationship is extremely difficult. Second, the highly nonlinear mapping relationship needs to be learned by using a model with high complexity, and a model with high complexity requires a lot of computational overhead. In this context, this paper studies the 3D HPE based on the transformer. We introduce the research status of HPE at home and abroad and provide a theoretical basis for designing the transformer 3D HPE model in this paper. We introduce the technical principle and optimization scheme of CNN and transformer and propose a 3D HPE model based on transformer. We used two datasets, COCO and the MPII datasets, and performed a number of experiments to find the best parameters for model development and then assess the model’s performance. The experimental findings suggest that the strategy described in this study outperforms all other methods on both datasets. The average precision (AP) of our model reaches up to 79% on COCO dataset but a PCKh-0.5 score of 81.5% on the MPII dataset.
Follow the Money, Back to Front

Abstract Hate speech, disinformation, and polarization are underwritten by deeply rooted media market dynamics involving the profitability of stoking anger and providing easy, identity-confirming outrage. Solutions therefore must focus on changing the payoffs to selling outrage and hatred. This essay offers two approaches that aim to make the outrage industry internalize the externalities of its business model. The first is a reconsideration of defamation laws. The second is the creation of a regulatory framework mandating transparency from companies that profit from advertisements in outrage media content. There would be a publicly curated database that makes transparent which companies have advertising associated with hateful content. Civic organizations and citizens would be encouraged to apply economic and social pressure on corporations to divest from this type of content. The essay discusses the benefits and drawbacks of these approaches, offering them as potential short-term solutions to a problem that will require long-term intervention.
In the New Multimedia Era, “Post-90s” Counselors Do a Good Job of Adaptive Education Path Research for “Post-00s” Freshmen

With the rapid development of network technology, all kinds of new media continue to emerge, and the “full-scale” zero-day difference of new media communication breaks through the scale of time and space. The era has come full circle. Today, “post-90s” counselors have gradually become the backbone of college counselors. At the same time, “post-00s” college students have gradually entered the campus and become the main force on campus. Counseling is a traditional profession. In the new era and new starting point, when the “post-00s” college freshmen begin to step into college campuses, how should the new counselors of the “post-90s” get along with them? The author believes that it is necessary to inherit the fine traditions, combine the characteristics of the times and the characteristics of new students, develop and innovate, pay close attention to the style of study, deepen the students, cultivate the backbone, work together at home and school, and study at all times so as to provide the source of living water and motivation for the growth of students. In the first month of college freshmen entering the school, they are still in the stage of exploration and adaptation, their study and living habits are not fixed, their learning methods have changed from supervised passive learning to conscious and active learning, and their lifestyles are also dominated by self-planning and self-management. After the “fresh period,” many freshmen show negative characteristics, such as weak self-discipline, laziness, and slack, and are full of confusion and contradictions. Guilt for not making progress. The decision at this stage is particularly important for freshmen to successfully complete their studies and discover their own value. Therefore, in view of the situation that freshmen cannot adapt to the new environment as soon as possible after being separated from the familiar environment, we should guide students to establish a positive learning and life attitude in the university from multiple angles, all-round and multiple ways, and integrate into the new life as soon as possible. This paper introduces the research status of ant colony algorithm and the existing path planning algorithms and space planning methods. Combined with the relevant knowledge of graph theory, the grid method is used to establish a space model.
Three-Valued Model Checking Smart Contract Systems with Trust Under Uncertainty

Blockchain systems based on smart contracts are critical systems that have to be verified in order to ensure their reliability and efficiency. Verifying these systems is a major challenge that is still an active topic of research in different domains. In this paper, we focus on verifying these systems that we model using trust protocols under uncertainty. Specifically, we address the problem using an effective verification approach called three-valued model checking. We introduce a new logic by extending the recently proposed Computation Tree Logic of Trust (TCTL) to the three-valued case ( $$3v-TCTL$$ ) to reason about trust with uncertainty over smart contract-based systems. We also propose a new transformation approach to reduce the $$3v-TCTL$$ model checking problem to the classical case. We apply our approach to a smart contract-based drug traceability system in the healthcare supply chain. The approach is implemented using a Java toolkit that automatically interacts with the NuSMV model checker. We verify this system against a set of specifications and report the results of our experiments.
Closed Home Physical Education Teaching Model and Response Strategies Based on Big Data Technology

Online teaching is carried out nationwide in the context of the new crown epidemic prevention and control, and physical education, as one of the compulsory courses in schools and universities, is included within the content of online line lessons. Online physical education teaching is a new approach and attempt to switch from an auxiliary teaching tool to the main teaching tool. The subject matter, target audience, teaching methods, and content of physical education classes have changed dramatically. The development of online physical education has played an important role in popularizing the concept of “sports for life” and establishing the concept of lifelong exercise. Through the analysis and research on the characteristics of online physical education, we propose the measures to promote the overall online teaching ability and level of physical education teachers in the postepidemic period, gradually promote the reform of online and offline physical education, and promote the integration of school physical education and social sports, as well as to build a physical education teacher training system that integrates online and offline development and improve and strengthen the management of Internet physical education resources. Since the teaching effect of online teaching is limited by the teaching means and content, which leads to the low accuracy of the evaluation of online sports teaching effect, for this reason, this paper designs a model based on big data technology and artificial intelligence algorithm for enhancing closed home sports teaching, obtains the overall situation of online physical education by using convolutional neural network to process physical education video sequences, and improves the system performance by embedding the algorithm into the big data framework, to avoid the interference of teaching environment and complete the evaluation of high precision online sports teaching effect. The experimental results show that the proposed method evaluation can improve the computing rate based on ensuring the high accuracy evaluation of physical education online teaching effect.
Battle Royale Optimizer with a New Movement Strategy

Gamed-based is a new stochastic metaheuristics optimization category that is inspired by traditional or digital game genres. Unlike SI-based algorithms, individuals do not work together with the goal of defeating other individuals and winning the game. Battle royale optimizer (BRO) is a Gamed-based metaheuristic optimization algorithm that has been recently proposed for the task of continuous problems. This paper proposes a modified BRO (M-BRO) in order to improve balance between exploration and exploitation. For this matter, an additional movement operator has been used in the movement strategy. Moreover, no extra parameters are required for the proposed approach. Furthermore, the complexity of this modified algorithm is the same as the original one. Experiments are performed on a set of 19 (unimodal and multimodal) benchmark functions (CEC 2010). The proposed method has been compared with the original BRO alongside six well-known/recently proposed optimization algorithms. The results show that BRO with additional movement operator performs well to solve complex numerical optimization problems compared to the original BRO and other competitors.
Game Character Generation with Generative Adversarial Networks

Designing visual content and characters for games is a time consuming task even for designers and illustrators with experience. Most of the game companies and developers use procedural methods to automate the design process. The visual content produced by these algorithms is limited in terms of variation. In this paper, we propose to use Generative Adversarial Networks (GANs) for visual content production. Two different rpg and dnd visual image datasets were collected over the internet for training and 6 different GAN models were trained on them. In 3 of 18 experiments, transfer learning methods are used because of the limited datasets. The Frechet Inception Distance metric was used to compare the model results. As a result, SNGAN was the most successful in both datasets. Moreover, the transfer learning method (WGAN-GP, BigGAN) was more successful than the from scratch method.
Two-qubit Fisher information and Jensen–Shannon nonlocality dynamics induced by a coherent cavity under dipole, intensity-dependent, and decoherence couplings

This study investigates two-qubit local quantum Fisher information correlation (LQFI), entanglement of formation (EoF), and two-qubit Jensen–Shannon coherence as a result of cavity–qubit coupling via intensity dependent and qubit–qubit interactions. Initially, the cavity is filled with coherent and even coherent fields, and the two-qubit state is pure. The cavity–qubit intensity dependent interaction, in the presence of intrinsic decoherence, can produce two-qubit local quantum Fisher information, entanglement of formation, and Jensen–Shannon coherence. This ability depends on dipole–qubit interactions, intrinsic decoherence, and initial cavity sates. The phenomena of sudden changes emerge in the dynamics behavior of the local quantum Fisher information, whereas the phenomenon of sudden birth–death occurs during the entanglement of formation dynamics. These phenomena can be enhanced by modifying the initial coherent field parameters, the mean photon number, and the non-classicality. The dipole qubit–qubit interaction improves the generated two-qubit correlation and coherence, while intrinsic decoherence decreases and stabilizes the generated two-qubit correlation and coherence. For a small initial coherent field intensity and dipole qubit–qubit interaction, the LQFI and EoF non-local correlations grow while the two-qubit Jensen–Shannon coherence diminishes. The qubit–qubit interaction coupling and the initial coherent field intensity can improve the produced stationary LQFI and EoF correlations and Jensen–Shannon coherence in the presence of the intrinsic decoherence.
Analysis of the Dilemmas and Countermeasures Brought by Data Analysis Based on Short Online Videos on Civic and Political Work

In the new era of Internet development, short video media has emerged and become the second largest network application in China. While bringing opportunities for the propaganda work of civic and political science, short video also brings difficulties and challenges such as complex network information interfering with the attention of netizens, fragmented communication method not conducive to systematic communication, and algorithmic recommendation technology causing barriers to the reception of information by netizens. In the case that the media short video and the political work need to be constantly updated, we should deal with the short video challenge brought by the media, effectively avoid its negative impact, actively deal with the period of time since the media, and make use of the short video to carry out various forms of educational reform. Facing these challenges, this paper focuses on helping the development of political work in the new era from the aspects of talent training, content optimization, brand building, algorithm standardization, etc.
Evaluation of Pointing Ray Techniques for Distant Object Referencing in Model-Free Outdoor Collaborative Augmented Reality

Referencing objects of interest is a common requirement in many collaborative tasks. Nonetheless, accurate object referencing at a distance can be challenging due to the reduced visibility of the objects or the collaborator and limited communication medium. Augmented Reality (AR) may help address the issues by providing virtual pointing rays to the target of common interest. However, such pointing ray techniques can face critical limitations in large outdoor spaces, especially when the environment model is unavailable. In this work, we evaluated two pointing ray techniques for distant object referencing in model-free AR from the literature: the Double Ray technique enhancing visual matching between rays and targets, and the Parallel Bars technique providing artificial orientation cues. Our experiment in outdoor AR involving participants as pointers and observers partially replicated results from a previous study that only evaluated observers in simulated AR. We found that while the effectiveness of the Double Ray technique is reduced with the additional workload for the pointer and human pointing errors, it is still beneficial for distant object referencing.
Study on Noise Reduction Method of low Illuminance Image Based on Noise Feature

In the field of image processing, it is often necessary to separate the background and foreground of image for the application for image editing, moving-object recognition, machine learning and so on. In image recognition under low illumination, the random noise is so intense relative to the intensity of the signal. So “How to deal with the noise” is important problem. Therefore, in this research, we propose the method of separating target and noise instead of removing noise. It can be seen from the results of the proposed method that the separation of target and noise is possible.
New Authoritative Changes with Blockchain an Emphasis Production Network

The paper means to introduce a deliberate writing survey showing the advantages, difficulties and future examination of blockchain innovation for the store network, likewise recommending how the elements of blockchain innovation can change the hierarchical parts of the store network. This paper appears through 31 factors ordered into positive, negative and future headings of innovation for the supply network. For example, blockchain innovation will diminish tedious of tasks the board and installments utilizing brilliant agreements. Moreover, incorporating blockchain innovation with different advances will permit item following and maintainable creation of the board. The choice of papers is restricted to supply networkopus data set and explicitly to the Administration Diary. Practical implications – blockchain innovation makes cooperative shared and business-to-business markets. The innovation mechanizes a few assignments, for example, request the board, installment for products, squander decrease and interaction control. In this manner, its utilization inside the supply networks will work on the usefulness and benefits of the members. This paper is centered around blockchain innovation for the supply network region with 60 articles examined. Likewise, 13 factors on benefits, eight factors on difficulties and 12 focuses on future examination bearings were investigated. This work will help specialists and business visionaries to extend about the progressions that blockchain innovation offers in supply network.
Analysis of Erhu Performance Effect in Public Health Music Works Based on Artificial Intelligence Technology

With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people’s evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect.
Pragmatic reasoning and semantic convention: A case study on gradable adjectives

Gradable adjectives denote properties that are relativized to contextual thresholds of application: how long an object must be in order to count as long in a context of utterance depends on what the threshold is in that context. But thresholds are variable across contexts and adjectives, and are in general uncertain. This leads to two questions about the meanings of gradable adjectives in particular contexts of utterance: what truth conditions are they understood to introduce, and what information are they taken to communicate? In this paper, we consider two kinds of answers to these questions, one from semantic theory, and one from Bayesian pragmatics, and assess them relative to human judgments about truth and communicated information. Our findings indicate that Bayesian accounts can model human judgments about what is communicated better than they can model human judgments about truth conditions, and their weakness on the latter is mainly because they fall short in accurately predicting the relevant threshold distributions. We explore the possibility that the overall performance of the Bayesian accounts can be potentially improved when they are supplemented with the threshold conventions postulated by semantic theory. EARLY ACCESS