Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Summarization Methods for Skin Care Product Reviews: A Study on BERT, BART, and T5 Models

The growth of the skin care industry is driving consumers to look for product review information. The skincare product currently widely used by women and men is facial wash products. On the Female Daily website, the most frequently purchased facial wash products are Cetaphil products. With so many purchases of this product, potential buyers want to try the product by reading the reviews first. However, the large number of reviews is a barrier to accessing relevant information. Therefore, effectively summarizing reviews can solve this problem by presenting key information more concisely and digestibly. This study aims to evaluate the effectiveness of three different summary methods, namely BERT, BART, and T5, in summarizing skin care product reviews. The stages in these three models are explained in detail. Evaluation results using the ROUGE Score method with the Cetaphil facial wash product review dataset show that the BART model consistently has a higher average ROUGE Score than the BERT and T5 models. Specifically, the BART model achieved an average ROUGE-1 of 0.87718, ROUGE-2 of 0.80689, and ROUGE-L of 0.87688, while the BERT model achieved an average ROUGE-1 of 0.84591, ROUGE-2 of 0.77585, and ROUGE- L is 0.84479, and model T5 has an average ROUGE-1 of 0.74995, ROUGE-2 of 0.70048, and ROUGE-L of 0.73023. These results suggest that the generative approach implemented by BART is more effective in addressing the diversity and complexity of skin care product reviews. Therefore, using the BART model is a better choice for producing skin care product review summaries that are more informative and useful for consumers. In the future, these findings may contribute to developing more sophisticated product review summary systems.
Punisher: A Deep Reinforcement Learning Model Trained by Correcting Bad Actions

Deep Reinforcement Learning (DRL) is a deep learning (DL) network model that uses environmental feedback to train and make decisions. Expected value, as a powerful mathematical tool, is widely used in DRL network training. However, there are often deviations between the expected values and the actual values obtained from the environment. Additionally, accumulative deviations are also present in DRL networks. The accumulation of these deviations can result in a slower training speed and negatively impact the network's stability. To address these issues, this paper proposes a new DRL training method called Punisher. The principle behind Punisher is to identify the bad actions made by the DRL agent and correct only those actions during the training process. By focusing on correcting the bad actions, Punisher aims to improve the overall performance and stability of the DRL network. The experimental results demonstrate that the Punisher method exhibits excellent performance, faster training speeds and greater network stability, making it a promising approach for efficiently training DRL agents in various applications. Experiments presented in the main content of this paper have been posted at GitHub at https://github.com/Jimmyoungyi/Punisher.
Influence of Prior Knowledge on Cyclist's Trajectory Forecast

The ability to predict the movement of other traffic participants is crucial for ensuring safety and comfort in shared traffic spaces. While human drivers can often identify behavioral indicators of other road users to anticipate their actions, automated vehicles lack this ability. This is especially important in interaction situations with vulnerable road users, such as cyclists, which are in the focus of this work. Trajectory predictions through machine learning models are a common method to estimate movements of those traffic participants. This paper investigates the impact of incorporating prior knowledge about cyclists' driving intention on the accuracy of trajectory forecasts. The study builds on previous research that utilized neural networks for trajectory predictions and proposes a method to incorporate prior knowledge as an additional input to an existing model. The influence of prior knowledge in different scenarios has been evaluated using different methods. The results indicate that incorporating prior knowledge yields improvements in the accuracy of trajectory predictions, demonstrating the potential of this approach for enhancing the safety and effectiveness of cyclist-vehicle interactions.
Recent Trends for Text Summarization in Scientific Documents

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required; therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization.
Rebalancing Autonomous Electric Vehicles for Mobility-on-Demand by Data-Driven Model Predictive Control

The main goal of this paper is to present an end-to-end, data-driven framework for the control of Autonomous Electric Vehicles (AEV) for Mobility-on-Demand (MoD). We present a data-driven Model Predictive Control (MPC) algorithm that rebalances (i.e. preemptively repositions) the AEV fleet in order to meet the mobility demand in the near future. The algorithm consists of Mixed Integer Linear Programming (MILP) that leverages the short-term forecast of the mobility demand as well as the charging station availability in order to optimally rebalance the AEV fleet. The proposed algorithm is evaluated by means of simulations with the New York City (NYC) taxi data. The proposed algorithm outperforms other state-of-the-art rebalancing strategies by reducing the mean customer wait time by 82.3% and the number of rejected requests by 94.6% for a given fleet size and a number of charging stations.
A Comparison of Optimization Techniques DeepLearning Models Based on Bell Pepper Leaves Diseases Classification

Deep learning has become a powerful tool for classifying and detecting images. In agriculture, deep learning can help organize and predict various issues, such as disease in leaves and stems, providing accurate information to farmers about various plant diseases, etc. This paper explains different optimization techniques used in deep learning, such as Rms prop, SGM, and Adam, specifically in the context of bell pepperdisease classification using Vgg 19. The study aims to analyze and improve the convergence capability of the Vgg 19 deep-learning CNN model. The study aims to determine the optimal configuration that achieves high accuracy in training, validation, and testing for disease classification in bell peppers. The work includes studying different optimizers and learning rates to find the most suitable optimizer and learning rate for the Vgg 19 CNN model in bell pepper leaf disease classification.Based on this study, the paper concludes that the Adam optimizer performs better with a small learning rate.
An Experimental Evaluation of Electrocardiogram Monitoring System Using Internet of Things and Intelligent Sensors Support

Patients in severe condition are treated at the Intensive Care Unit (ICU). In such life-or-death situations, doctors must have continuous access to vital signs including blood pressure, heart rate, and temperature. What's the deal with asthma, the planet, and salt levels? The manual process is excessively time- consuming, and when applied to a large number of patients, it becomes nearly impossible. This Internet of Things-based technology has the potential to implement automated network communication with medical professionals, keeping them fully informed at all times. The sensors in the ICU monitoring system for the Internet of Things ( IoT ) are controlled by an Arduino board. Networked sensors, either worn by the patient or installed in our homes, alter the inductive data collection of our physiological and mental states. A patient may be tracked around-the-clock with a smart health monitoring system based on the Internet of Things ( IoT ). One of the most noticeable uses of the Internet of Things in the present day is in health monitoring systems. In this research, the AD8232 signal conditioning module was presented as the basis for a wireless ECG monitoring system capable of collecting data in real time. The ESP32 will be linked up to the AD8232 ECG Sensor. Then, we'll get an Electrocardiogram (ECG) reading by attaching the leads to the patient's chest or hand. The measured value exceeded the set limit of the sensor. Users of this module of the healthcare system should expect to get an updated prescription for their current medical condition within minutes of any significant change in their health status. Doctors are informed of their patients' conditions on an LCD screen, which they may then use for further assessment. When a patient requires a hospital's Intensive Care Unit, a smart ICU may be used to keep tabs on vital signs including oxygen levels, heart rates, body temperatures and more.
Knowledge extraction and entity linking model based on attention mechanism

In the field of natural language processing, entity extraction, relation extraction, and entity linking are important tools for processing unstructured data. However, the mainstream methods are to carry out each task alone, ignoring the connection between these tasks. In the paper, we propose the pipeline model (PALC-R, POStag-Attention-LSTM-CRF Relation Extraction) of knowledge extraction to complete the extraction of entities and relations. We also propose a joint learning model of entity extraction and entity linking named PALC-DCA (PALC-Dynamic Context Augmentation), to improve the accuracy by sharing local scores obtained in the entity linking module and learning the description information in the third-party knowledge base. The experiments show that entity extraction can achieve the accuracy of 90.84% on CONLL03 dataset, the accuracy of relation extraction is 81.77%, and the accuracy of entity linking on the AIDA CONLL dataset is 94.18%.
DDRSNet-Rail Surface Defects Classification Based on Depthwise-Dilated Convolution

In the domain of railway safety, effective rail surface defects detection remains a pivotal challenge. This paper presents a novel convolutional architecture named Depthwise-Dilated Convolution (DDC) aimed at efficiently classifying rail surface defects. Leveraging the benefits of both depthwise and dilated convolutions, our DDC model showcases significant advantages in terms of computational efficiency and accuracy. Experiments on a comprehensive dataset of 4,698 rail surface defects images, sourced from passenger railways in Northwest China, resulted in an impressive test accuracy of 94.43%. Comparative benchmarks against state-of-the-art models, including CNN, CNN-SPP, Fractal Convolution, Transpose Convolution, and MobileNetV2, further substantiate the superiority of our approach. Through rigorous feature map evaluations, we illustrate the DDC's enhanced capability in discerning intricate rail defects features. Our findings position the Depthwise-Dilated Convolution as a frontrunner for real-world applications, especially in computational-constrained environments.
Exploring the Impact of Cloud Computing and Edge Computing on Resource Consumption for Mobile Devices with Generative Artificial Intelligence APIs

With the release of OpenAI’s ChatGPT, generative AI has become a hot topic in business. Generative AI APIs can be consumed by mobile devices, which are constrained by resources such as CPU, memory, and battery power. Power can be saved by using both Edge and Cloud Computing. However, there is a penalty to response time implementing these services. There seems to be a greater impact on response time with Cloud Computing than with Edge Computing, however Edge Computing is constrained by limits on payload and maximum response times. Our testing results show that Edge Computing is a good middle ground, providing a balance between savings in CPU time, while somewhat mitigating the penalty in response time. Developers exploring the option to use Cloud and/or Edge Computing for power savings on mobile devices should consider the size of their data sets carefully to determine if an Edge Computing service is a viable option.
Robust Model Predictive Control Strategy for Virtually Coupled Train Sets Using Time-Varying Tubes

Advanced Virtual Coupling (VC) technology can improve rail transit capacity by reducing train spacing. Due to the inevitable time-varying uncertainties of tunnel resistance and other disturbances, it is a great challenge to design an optimal controller for Virtually Coupled Train Set (VCTS), especially considering safety constraints and less conservative control performance. To overcome these problems, this paper proposes a novel robust Model Predictive Control (MPC) strategy for VCTS based on time-varying tubes, which is different from the existing methods based on fixed size tubes. First, the VCTS system is modeled considering complex constraints and bounded disturbances. Secondly, on the basis of analyzing the influence of disturbances, the optimal control problem is constructed by using time-varying tubes for constraints tightening. Furthermore, a distributed robust MPC algorithm is proposed to realize cooperative operation control, and its recursive feasibility and input-to-state stability are proved. Finally, numerical simulations are provided to show that the time-varying tubes can obtain a larger feasible domain of the optimal control problem than the fixed size tubes, which is conducive to reducing the conservativeness of controller, and the proposed algorithm can deal with disturbances effectively.
Interaction-Aware Trajectory Prediction with Point Transformer

To ensure safe and efficient autonomous driving, trajectory prediction system must account for social interactions among road participants. Graph-based models are leading approaches in modeling social interactions for trajectory prediction, but they face the challenges of designing an appropriate graph structure and processing complex interactions. We consider that the participants in a scene are a set of unstructured points, which are similar to point cloud data. Inspired by point cloud learning networks, we view the road participants in a scene as point cloud in a two-dimensional coordinate system, and utilize Point Transformer aggregator to process the interactions on both local and global level. Besides, we present a multiplex fusion of social and temporal information for trajectory prediction. We perform extensive experiments on the Argoverse motion forecasting dataset, and the results demonstrate the superior performance of our model for multiagent trajectory prediction.
Research on network security situation awareness prediction model based on deep learning methods

The model of network security situational awareness allows for accurate prediction of forthcoming network security incidents by analyzing historical network security data, thereby helping to detect and respond to potential network security threats in advance and protect network security. In this paper, a novel approach for network security situational awareness is introduced, which can predict future network security events by analyzing historical network traffic data. The model utilizes the LSTM neural network structure, which can capture long-term dependencies in time series data to better predict the occurrence of cybersecurity incidents. In this model, the network traffic data is firstly preprocessed, including data standardization and so on. Then, feed the preprocessed data into the LSTM model for training. Experimental results show that the proposed method achieves significantly more predictive performance on multiple real test sets. Compared with traditional methods, this model can better realize network security situation awareness. Future research can further refine the design and improvement of the model to adapt to the changing cybersecurity environment, and explore the combination of more features and deep learning models to improve the prediction and response capabilities of cybersecurity.
Predicting Diabetes Risk in Pregnant Women: Leveraging AdaBoost as a Boosting Algorithm

Gestational diabetes is one of the diseases that many pregnant women suffer from. In this modern era, machine learning and data mining have a significant impact on world of health. For experimental purposes, we use the PIMA Indian dataset to predict diabetes. We conducted several experiments using 5 machine learning algorithms, namely Support Vector Machine, Random Forest, Logistic Regression, Gaussian Naive Bayes, and Decision Tree, which were then combined with boosting algorithm, namely AdaBoost to form a more robust model. Comparison of model performance was carried out to find out the most suitable model for predicting diabetes and Random Forest + AdaBoost achieved the highest accuracy of 88.2% and F1-score of 83.4%. The aim of this paper is to develop and evaluate a predictive model using the AdaBoost algorithm to effectively predict the risk of diabetes in pregnant women and to provide valuable insights for medical risk prediction, particularly in diabetes.
A Comparative Study of Traditional and Transformer-based Deep Learning Models for Multi-Class Eye Movement Recognition Using Collected Dataset

The study of eye movement recognition has emerged as a pivotal focus, particularly in fields such as human-computer interaction, healthcare diagnostics, and adaptive technologies, due to its potential to enhance lives, especially for those with physical impairments. However, employing deep learning models that utilize non-intrusive cameras for recognizing and classifying eye movements has been impeded by issues that stem from environmental, physiological, and technical factors. These encompass unpredictable lighting, noise, head movements, and inherent human differences. In response to these challenges, this study presents an in-depth comparison between the performance of the ViT vit-base-patch16-224-in21k model and traditional deep learning models including ResNet18, and AlexNet, all of which were adapted and optimized for our collected dataset that consists of diverse eye movements from eight participants, captured under varied environmental and physiological conditions. The evaluation criteria included accuracy, interference time, and memory footprint. The findings indicate that the ViT model delivers a balanced performance, effectively addressing the intricacies of the multi-class eye movement dataset while maintaining interference time efficiency. This study underscores the importance of considering both performance and computational demands in choosing appropriate models for eye movement recognition and offers insights to guide future research. ViT and ResNet18 were about equally accurate but ViT was faster, while ResNet18 used less memory; AlexNet was less accurate and its speed and memory use were in between the two. We find that ViT showed remarkable efficiency with an average of 0.0588 seconds per image which makes it promising for applications that rely on the interference time.
Improving classification accuracy for motor imagery BCI using DWT based Features

Brain-computer interfaces (BCIs) act as vital bridges connecting human brains to external devices. They help people with disabilities do things on their own, like tasks and talking to others. One of the challenges of the BCI motor imagery electroencephalography (MI-EEG) based is the low accuracy of the imagined movements recognition. This paper introduces new features based on the discrete wavelet transform (DWT) to make MI-EEG signal decoding more accurate. Our system utilizes 18 features extracted from both the time and the frequency domains to feed a pre-trained classifier in order to identify hand movement. Many classifiers were used in this study namely the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (KNN), and linear discriminant analysis (LDA). The effectiveness of the proposed system was evaluated based on the classification accuracy (CA) using the BCI Competition II Dataset III. The results of our experiments demonstrate that the LR classifier outperforms all studied classifiers achieving an impressive CA of 92.14% while using the LR classifier with just 6 features selected by genetic algorithm optimization (GA).
Hybrid AI Road Markings Analysis from a Retroreflectometer

Road markings are essential for driving. Their characteristics are surveyed with dynamic retroreflectometers and are provided in an aggregated form to managers, usually averaged on 100 meter intervals. We have developed and tested a road marking analysis method that represents the measured signal as images and allows renderings at finer scales, such as the marking segment scale. To do this, we used several artificial intelligence methods. First, an architecture derived from U-Net was proposed for the automatic segmentation of marking elements. To check the consistency of the maps produced, we tested several techniques used in natural language processing with architectures such as LSTM and an attention mechanism derived from Transformers. Since there exist no annotated databases for this type of data, a Model-View-Controller software solution was developed to visualize, annotate and enhance the data. This led to the implementation of a processing chain as well as incremental learning methods. This paper presents both the software for processing data and visualizing the results, the methodology implemented and the first results obtained.
AoI-Driven Cluster-Based Spectrum Sharing in Internet of Construction Equipment with Multi-Agent Reinforcement Learning

Tunnel construction is a dynamic excavation process that requires a variety of construction equipment and workers to cooperate according to standard procedures. To enhance construction efficiency and reduce labor costs, intelligent construction equipments have been extensively employed in tunnel construction sites. However, the effectiveness of collaborative operation efficiency in construction equipment is hindered to some extent due to inadequate information sharing and inefficient awareness of the construction situation. To deal with the problem, we propose a cluster-based communication network architecture and define a novel metric named construction procedure coherence degree (CPCD) based on age of information (AoI) to quantify the significance of construction equipment collaborative operation. We formulate radio resource allocation as a stochastic optimization problem, which can be modeled as a decentralized partially observable Markov decision process (Dec-POMDP). Furthermore, a multi-agent reinforcement learning algorithm named QMIX is used to optimize the spectrum-sharing strategies among multiple construction equipment, enabling efficient and reliable communication. Extensive experiment results show that the QMIX outperforms well than independent Dueling DQN (IDDQN).