Reviewerly/exHarmony huggingface dataset:
A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications

The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.
Integration with Visual Perception—Research on the Usability of a Data Visualization Interface Layout in Zero-Carbon Parks Based on Eye-Tracking Technology

With the continued application of data visualization technology in sustainable development, the construction of carbon emission monitoring platforms is becoming increasingly popular in industrial parks. However, there are many kinds of such interfaces, the usability of which remains unclear. Therefore, in order to explore the usability of current carbon emission visualization interfaces in parks and put forward humanized optimization strategies for their subsequent design, this study used eye-tracking technology to analyze the data readability of six types of layouts from three aspects of visual perception features: integrity, understandability, and selectivity. Quantitative data from eye movement experiments and visual perception characteristics were evaluated using a Likert scale in an analysis of different layouts, and the correlation data between three visual perception characteristics and the readability of different layout data were obtained using an SPSS tool. The results show that, compared with a layout containing 3D graphics, the pure data type of interface has a shorter task completion time and higher readability; however, it provides fewer choices for users and is less interesting. In addition, there is a significant negative correlation between integrity and task completion time; the more complete the interface layout, the shorter the task completion time. In summary, a certain correlation was found between visual perception characteristics and the readability of interface layout using this method. At the same time, the advantages and disadvantages of different interface layouts were also analyzed, and more humanized optimization directions and strategies were devised. This is vital for aiding subsequent research on the influence of specific layout elements to optimize visualization interfaces that display carbon emission data.
Blockchain Technology in Various Fields: Applications, Challenges, And Future

Blockchain technology has revolutionized many industries and has great potential outside of finance. This paper explores blockchain's applications and challenges in agriculture, electricity, transportation, healthcare, and finance. Blockchain technology can track agricultural product origin, quality, and safety to improve supply chain transparency in agriculture. Blockchain can also reduce intermediaries, improve payment systems, and expand financing. Blockchain can enable decentralized electricity management, peer-to-peer energy transactions, and lower transaction costs. It could boost renewable energy integration, grid efficiency, and energy access for underserved communities. Blockchain technology can improve transportation supply chain visibility and reduce fraud by providing a shared, tamper-proof ledger to track goods and prevent unauthorized access. Blockchain can secure, interoperable, and improve patient privacy in healthcare. It could let patients share their health data with providers and researchers while protecting their privacy. Blockchain implementation is also tricky. Scalability, interoperability, compliance, and data privacy Blockchain solutions must handle large amounts of data, integrate with existing systems, comply with laws and regulations, and protect sensitive data. Further research and development are necessary to explore blockchain technology's possibilities in these fields fully.
Addressing Posterior Collapse in Variational Autoencoders with β-VAE

Posterior collapse is a pervasive issue in Variational Autoencoders (VAEs) that leads to the learned latent representations becoming trivial and devoid of meaningful information. To address this problem, this paper presents a novel β-VAE approach, which incorporates a hyperparameter β to strike an optimal balance between the reconstruction loss and the KL divergence loss. By conducting a comprehensive series of experiments and drawing comparisons with existing methods, robust evidence is provided that the proposed β-VAE method effectively mitigates posterior collapse and yields more expressive and informative latent representations.The experimental setup involves various architectures and datasets to demonstrate the versatility and efficacy of the β-VAE approach in diverse settings. Additionally, ablation studies are performed to investigate the impact of different β values on the model's performance, elucidating the role of this hyperparameter in controlling the trade-off between reconstruction quality and latent representation expressiveness. Furthermore, the disentanglement properties of the learned latent space are analyzed, which is a crucial aspect of VAEs, especially when applied to complex, real-world data.In-depth analysis of the results offers valuable insights into the underlying mechanisms of β-VAE, thereby contributing to a more profound understanding of VAEs and their inherent limitations. The findings not only establish the effectiveness of the β-VAE method in preventing posterior collapse but also pave the way for future research on improving VAEs' performance in various applications. Potential future work could explore alternative techniques for balancing the competing objectives of reconstruction and latent representation learning or delve into the theoretical properties of β-VAE, providing a more rigorous foundation for this approach.
The applications of chatbot

A chatbot is a program that uses natural language to simulate a human conversation. and can receive questions from users to answer, as well as engage in casual conversation with users in a set format or help them complete a matter. It can free up personnel in an organization that is needed for reasonably stable, repetitive, and demanding tasks or procedures. Natural language processing (NLP), an essential aspect of artificial intelligence, forms the foundation of the system., and neural networks (NNs) that receive natural language and process responses. In recent years, chatbots have grown significantly with the rise of artificial intelligence, with achievements by major Internet companies, various universities, and research institutes. This review provides a detailed study of chatbots in five areas of intelligence in recent years, including education, intelligent question and answer, customer service, entertainment, and personal assistants. The goal is to summarize the cutting-edge and effective technologies in each area. In reviewing these papers, we pay closer attention to the technology in this paper's potential development possibilities and whether relevant experiments or detailed descriptions are done for the proposed future extensions, etc. While we discuss and study the current technologies, we also pay attention to the possibilities and trends in the development of new fields.
A Method for Training-free Person Image Picture Generation

The current state-of-the-art Diffusion model has demonstrated excellent results in generating images. However, the images are monotonous and are mostly the result of the distribution of images of people in the training set, making it challenging to generate multiple images for a fixed number of individuals. This problem can often only be solved by fine-tuning the training of the model. This means that each individual/animated character image must be trained if it is to be drawn, and the hardware and cost of this training is often beyond the reach of the average user, who accounts for the largest number of people. To solve this problem, the Character Image Feature Encoder model proposed in this paper enables the user to use the process by simply providing a picture of the character to make the image of the character in the generated image match the expectation. In addition, various details can be adjusted during the process using prompts. Unlike traditional Image-to-Image models, the Character Image Feature Encoder extracts only the relevant image features, rather than information about the model's composition or movements. In addition, the Character Image Feature Encoder can be adapted to different models after training. The proposed model can be conveniently incorporated into the Stable Diffusion generation process without modifying the model's ontology or used in combination with Stable Diffusion as a joint model.
The Study of Performance for Face Detection Based on Multiple Representative Convolutional Neural Networks

Due to the diversity of deep learning models, choosing the suitable model for a specific task can be rather onerous. In this paper, the performance of three deep convolutional neural networks, namely VGG16, ResNet50, and MobileNetV2 on face detection were compared. Each model was trained on a dataset of 11,900 images from the FDDB dataset that included various face sizes and orientations with multiple augmentations, including color alteration, blurring, and flipping. The final layers of the models were modified into a binary classification model and a regression model indicating face found and coordinates of the facial bounding box. The models were trained on the same basis of 40 epochs with batch size 64 with binary cross entropy loss and DIoU loss and a learning rate of 0.0001 with a learning rate decay of 0.8 per epoch. The experimental results demonstrated that VGG16 outperformed ResNet50 and MobileNetV2 in terms of accuracy, with VGG16 achieving the highest score of 0.9240, followed by ResNet50 with a score of 0.8568, and MobileNetV2 with an accuracy of 0.6028. The results suggest that VGG16 is a more suitable choice for face detection applications than ResNet50 and MobileNetV2, while ResNet50 and MobileNetV2 may provide higher accuracy for other image recognition tasks or real time face detections. The findings in this paper can contribute to the selection of appropriate deep learning models for face detection.
The Study of Performance for Cross-Platform Spam Filtering Based on the Random Forest Algorithm

The objective of this study was to investigate the performance of the Random Forest algorithm in spam detection when generalized from email spam to social media comment spam. The dataset used involved the use of two sources: an email dataset and a YouTube spam comment dataset. Text processing techniques and feature extraction methods were applied to preprocess the datasets using scikit-learn package. Labels were mapped from "spam" and "ham" to "1" and "0" respectively for training and testing the model. The email spam dataset was split into training and testing datasets, and the first 3000 lines were used for training the model. The generalization ability of the model was tested on the YouTube spam comment dataset. Multiple decision trees were created using the Random Forest algorithm and were trained on different subsets of the training data. The results indicated that the accuracy rate of the prediction on the YouTube spam comment dataset was only around 62%, which is comparatively low. This suggests that the Random Forest algorithm, when used for spam detection, may not have good enough generalization ability to be applied in practice. Additionally, as the number of trees increased, the maximum accuracy decreased, indicating the possibility of overfitting. Although the accuracy of the models was modest, possible improvements could be made to the pre-processing of the data so that the features extracted from the text can have greater conformity with social media spams. In conclusion, further work is needed before the model can be used in generalized situations.
Research On Intelligent Classification Trash Can Based on Machine Learning

At present, there are still many difficulties in advancing garbage classification in China. This passage simulates human eyes by camera, core processor simulates human brain, complete human motion by mechanical design, and realizes intelligent garbage bin classification based on machine vision: AI vision module takes K210 as the core processor; The electronic module uses STM32 as the core chip and the mechanical module to realize two main structures: stepper motor - turntable and servo - connecting rod, which can transport the identified garbage and put it into the corresponding trash can. After testing, the identification accuracy of harmful garbage is as high as 99%, and the accuracy of the other three types of garbage is 93.18%, which can effectively identify the types of garbage and realize automatic garbage classification.
The Research on the Forms and Advantages of VR in Physics Teaching

In traditional physics experiments, the process and results of experiments are often presented verbally by teachers and in books, which do not allow students to understand the nature of the experiments and the techniques used in the experiments, based on this , this paper investigates how to combine VR technology with physics teaching so that students can deeply understand their knowledge and improve their learning efficiency, and the development with the characteristics of VR , the application forms and teaching characteristics of VR in physics teaching, the advantages of VR and the design principles of VR in physics teaching are explored separately, and conclusions are drawn. The results finds that the application forms of VR in physics teaching are Demonstration Experiments, Measurement Experiments, and Investigation Experiments, and the design principles of VR in physics experiments include The Constructivist Theory, The Embodied Cognition Theory, The “learning by doing” theory, and there are six aspects of advantages in physics teaching. Through this paper, the research hope can make some contributions to those who do this related research in the future.
Study on preparation of Olefins by Coupling ethanol based on simulated annealing Model and Neural Network algorithm

Ethanol molecules can be prepared by biomass fermentation, with a wide range of sources, green and clean, and the production of high value-added C4 alkenes with ethanol as a platform molecule has great application prospects and economic benefits has attracted wide attention at home and abroad. Meanwhile, optimizing the reaction conditions for the preparation of butene can also appropriately reduce the waste of resources and make a certain contribution to environmental protection. This paper collects relevant response data, uses fit type and Linear model for data fitting and comparative analysis, calculates the average value of each item of data selected from the five experimental groups, and then uses MATLAB two-dimensional broken line drawing for analyzing the changing law of the data more vividly and intuitively. Finally, the simulated annealing algorithm model and the genetic algorithm model are used to optimize the temperature and catalyst combination of the highest C4 olefin yield, the GRGNon-Linear method, the simple simplex method, and the Evolutionary evolution algorithm and Nesterovmethod artificial firefly algorithm are used to analyze and calculate. Thus, the optimal catalyst combination is 200mg1wt%Co/SiO2-200mgHAP ethanol concentration 0.9ml/min, and the temperature of 425℃ is the maximum yield of C4 olefins. Moreover, 350 degrees is the optimum temperature in the temperature range below 350 degrees. Finally, we use the experimental data of the BP (BackPropagation) neural network prediction algorithm to predict and analyze the feasible scheme and explain the detailed reasons for choosing the experimental conditions to determine five groups of better experimental schemes.
Analysis and Application Research of InfoGAN

Generative Adversarial Networks (GAN) have been a revolutionary development in the field of Artificial Intelligence, particularly in the domain of generative models. The information maximizing generative adversarial nets, or infoGAN, is one of the most recent and promising developments in the world of GAN. InfoGAN focuses on maximizing the mutual information between the generator's output and some input variables. This means that the generated images or data can be controlled more easily while maintaining high-quality results.Apart from these applications, infoGAN has also been used in other areas such as natural language processing, anomaly detection, and even in music generation. With its versatility and robust performance, infoGAN looks set to become an increasingly important tool for researchers and practitioners in the field of machine learning.This paper focuses on the principle of infoGAN (information maximizing generative adversarial nets) and tries to put forward several ways to apply infoGAN to solve different kinds of problems in daily life.
The Advance of GPTs and Language Model in Cyber Security

Nature language processing (NLP), one of the most remarkable machine learning techniques currently available, is gaining traction with the public and has achieved great success in many applications. Many companies have developed language models, such as BERT, BART models from Google, and GPT (generative pre-trained transformer) series models from OpenAI. GPT is an unsupervised learning model that generates responses and uses unsupervised pre-training and supervised fine-tuning. GPT-2 is a multitask unsupervised learner that completes tasks using an unsupervised pre-trained model, including a zero-shot setting. GPT-3 extends the few-shot learning approach introduced in GPT, which does not require any gradient updates or fine-tuning for specific tasks. InstructGPT focuses on the alignment that could fit human intention by fine-tuning with human feedback. The outputs of InstructGPT significantly improved in truthfulness and were slightly less toxic than GPT-3, but bias and simple mistakes still existed. This paper aims to provide a detailed overview of the technical advancements utilized in GPT, GPT2, GPT3, and InstructGPT, explore the techniques in different models, and focus on the applications in the cybersecurity aspect. This paper compares the upgrade of GPT models and summarizes the SecureBERT model’s effects on cyber security.
Chatbot in the Service Industry: Challenges and Perspectives

The chatbot is a software application that could initiate and participate in a meaningful conversation with a live human being agent. With proper algorithms and sufficient training, a chatbot could comprehend inquiries and replies in a way that mimics human response. The chatbot allows companies to operate at a low cost and provides more efficient and professional customer service. This article aims to provide a symmetrical overview of chatbot building and comments on frequently used models and mainly focuses on the models that researchers utilize to build chatbots. The models are separately discussed in terms of query understanding and response generation, and the knowledge base, evaluation, and optimization methods are also included. Chatbots building is a combination of Natural Language processes (NLP) and Machine Learning (ML), and there is a trend shifting from rule-based to more complex models involving Neural Networks.
A New Ensemble Model Based on Machine Learning Algorithms for the Spam-Filtering

Due to the rapid increasement about the information in email, it is necessary to improve the technology of spam-filter. Based on the data on website, this study used python and Scikit-Learn library to process the data. Training the data by using Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest, and Decision Tree these five models, and calculate the evaluation measures for the rest test. From the results of the evaluation to infer the possible logical formula between these five models and calculate their evaluation measures. Comparing the final evaluation measures from the single model’s and the logical formula’s to find one possible logical formula that is better than the single model. The experimental results demonstrated that, each models have different efficiencies about the spam-filter, and the logical formula will improve some scores in evaluation measures, which means the logical formula using multiple models will improve the technology of spam-filter.
Information Encryption and Data Storage Security

Nowadays, people increasingly rely on communication software to communicate. Users can use communication software for daily life information communication and involve work, transactions, or some private content. Moreover, time or money can be lost if that information is leaked. Therefore, ensuring communication security becomes a critical issue. Communication security generally involves two aspects: information transmission and data storage. Attackers do more than attack the transmitted information. It can also steal data that is already stored. This paper first introduces the encryption algorithm of information transmission. And then, this paper introduces secure data storage methods for information encryption.Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES) algorithms are mature encryption algorithms currently used. Using RSA to encrypt the AES key in information transmission can make the information more secure and reliable. In addition, adding RSA algorithm-supported digital signature verification technology can ensure the authenticity and integrity of messages. Data storage is just as necessary as information encryption. Data needs to be stored in a database. Besides, this paper introduces how the security mechanism of the MySQL database controls user access to the database.