Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Influence of Preoperative Variables on Patient Satisfaction: A Machine Learning Approach in Rotator Cuff Repair Surgeries

Orthopedic interventions, including rotator cuff repair surgeries, play a pivotal role in addressing musculoskeletal conditions and restoring patients’ functional abilities. While clinical outcomes serve as important benchmarks, patient satisfaction has emerged as a crucial measure of success in evaluating the effectiveness of these surgical procedures. This study delves into the intricate relationship between preoperative variables and patient satisfaction outcomes specifically in the context of rotator cuff repair surgeries. By employing an array of 14 distinct machine learning models, featuring the Multi-Layer Perceptron (MLP) as one of the primary models.we aim to explore the effects of preoperative variables on the outcomes of patient satisfaction.These research outcomes underline the significance of correcting class imbalances in healthcare data and employing accurate predictive modeling techniques to elevate patient-centered care. Our analysis is based on a comprehensive cohort of 189 patients with shoulder pathology, utilizing a combination of stratified sampling and the Borderline Synthetic Minority Oversampling Technique (BSMOTE) to ensure a balanced dataset, facilitating a comprehensive assessment of satisfaction factors. In summary, the baseline models Extra Trees and CatBoost exhibited comparable validation performance with closely aligned F1-scores for both target classes. However, on the test set, Random Forest outperformed XGBoost in terms of achieving higher F1-scores and precision. Turning to feature selection, FS_Extra Trees, FS_XGBoost, and FS_Neural Network excelled on the validation set, demonstrating the highest Kappa values and significant F1 scores. Remarkably, on the test set, the FS_XGBoost model maintained consistent performance with a Kappa coefficient of 0.54, showcasing remarkable F1-scores of 0.87 for the "Satisfied" class and 0.67 for the "Not Satisfied" class. The study’s outcomes demonstrate the substantial impact of machine learning in orthopedic surgery, signifying a significant advancement in data-informed patient care and the enhancement of outcomes in Rotator cuff repair surgeries (RCR).
A Data Protection Method for Short-Term Traffic Prediction with Applications to Network Active Mode Operations

Accurate prediction of active mode traffic is imperative for optimizing traffic operations in Intelligent Trans-portation Systems. However, existing data-driven approaches heavily rely on extensive datasets to achieve reliable traffic prediction. This dependence poses a challenge when it comes to data sharing, particularly when collecting information from multiple local clients, such as institutions, organizations, and mobile devices, and transmitting it to a central server for model training and application. To overcome this challenge and enhance data security, we introduce the FedASTGNN model for active mode traffic prediction. This approach combines the federated averaging (FedAvg) algorithm with an attention-based spatial-temporal graph neural network (ASTGNN) model. Subsequently, we conduct an evaluation to determine the performance gap between the centralized ASTGNN model and the proposed distributed FedASTGNN model. This evaluation takes into account the model's performance across different time aggregation intervals and prediction horizons. Moreover, considering the unique attributes and intricacies of active mode data, we create three scenarios to demonstrate the influence of diverse active mode data from different local clients (subnet-works) on the FedASTGNN model. The findings of our study illustrate that the FedASTGNN model effectively preserves the advantages of the ASTGNN model while ensuring data confidentiality in active mode traffic prediction. Furthermore, we observe that the performance of the FedASTGNN model is significantly affected by the varying degrees of imbalanced data distribution among subnetworks. The insights shed light on the potential and challenges presented by the FedASTGNN model as an efficient and secure solution for predicting active mode traffic in Intelligent Transportation Systems.
SE-DO: Navigating the 6G Frontier with Scalable and Efficient DevOps for Intelligent Agents Optimization

In the imminent era of 6G dominance and the expanding realm of interconnected IoT devices, ensuring seamless connectivity is vital. This paper introduces the Scalable and Efficient DevOps (SE-DO) methodology—a scalable multi-agent system designed for next-gen networks. SE-DO’s avant-garde architecture dynamically integrates new agents, mirroring evolving IoT ecosystems. Two sets of agents manage vast IoT data in urban matrices: extraction agents collect, and preprocessing agents refine for computational tasks. Anticipating 6G’s prowess, training agents optimize machine learning models for real-time traffic analytics. Augmented by predictor agents, SE-DO prioritizes accuracy, efficiency, and security, validated empirically. SE-DO’s adaptability ensures scalable proficiency amid IoT integrations, facilitating a smooth transition to the 6G era. Addressing IoT challenges, SE-DO, rooted in a robust multi-agent system, optimizes agent performance in resource-constrained environments. Real-world experiments highlight SE-DO’s high accuracy and efficiency, making it pivotal for future network optimization. Access SE-DO’s future on GitHub—where innovation meets accessibility. As next-gen networks demand reliable solutions, SE-DO emerges as an indispensable tool for navigating emergent network paradigms.
A 12/7 Segmented Outer Rotor FSPM Motor with Improved Performance

This paper presents a new 12/7 flux switching permanent magnet motor with segmented outer rotor (SOR-FSPM). The proposed topology contains seven magnets which are inserted in the middle of rotor segments. Using a rotor-PM structure increases winding space as well as torque density compared to the conventional FSPMs which magnets are placed on the stator. In addition, utilizing a segmented structure reduces the weight of the machine. In order to improve electromagnetic performance of the proposed machine, original geometry is modified through the stator tooth cutting. To study the effect of stator tooth cutting and find the optimal model, sensitivity analysis and Taguchi method are applied. Finally, the finite element method (FEM) results of the optimal model are discussed and compared to the initial design. The FEM results shows the effectiveness of the optimization procedure.
Data Trading System Using Blockchain with Oracle Network

Data is a crucial asset for Industry 4.0 serving to enhance operational processes, understand customer requirements, assist market decision-making, etc. Data trading systems have sprung up as an answer to a demand for Industry 4.0. However, they operate with systematic issues such as data reselling, inherent lack of trust between buyers and sellers, and botnet attacks. This paper proposes a blockchain-based data trading system that (i) operates under primarily an aggregate transaction mode where the system will act only as an intermediary, (ii) works in four main phases e.g., data-sale phase, contention phase, random arbitrator selection phase, and out-of-chain phase, of which the foremost is mandatory and the remaining are conditional, (iii) uses oracle network for automation and incorporating randomness into the process to fortify against adversary conditions, and (iv) incentivizes the fair behaviour through rewards. The experimental evaluations, analyses, comparisons with state-of-the-art works, etc., imply the efficacy of the proposed data trading system.
Enhancing Scene Simulation for Autonomous Driving with Neural Point Rendering

Simulation plays a critical role in the development and testing of autonomous driving, which encounters significant challenges when synthesizing complex driving scenarios and realistic sensor information. Existing scene simulation methods either fail to capture intricate physical characteristics of the 3D world or struggle to extend to autonomous driving datasets with uneven distribution of viewpoints. This paper proposes a point-based neural rendering approach to reconstruct and extend scenes, thereby generating real-world test data for autonomous driving systems from various views. By utilizing collected LiDAR data and filling in sparse regions in the point cloud, accurate depth and position references are provided. Additionally, the neural descriptor is enhanced by incorporating supplementary features relying on the observation views and sampling frequency, while rendering multi-scale descriptions to capture comprehensive information about the scene's appearance. Experimental results demonstrate that our method achieves high-quality rendering for large-scale autonomous driving scenes and enables scene editing to synthesize more diverse and adaptable testing scenes.
A Dynamic Object Removal and Reconstruction Algorithm for Point Clouds

Autonomous vehicles (AV) are one of the greatest technological advancements of this decade and a giant leap in the transportation industry and mobile robotics. Autonomous vehicles face several major challenges in achieving higher levels of autonomy. One of these is to find a fast and reliable algorithm to process the sensor data so that the simultaneous localization and mapping (SLAM) algorithms run in real-time to achieve autonomous navigation. The major limitation of the SLAM algorithm, especially while building a map is to have static environmental features, i.e. without any dynamic or moving objects. To achieve this, our paper introduces a novel algorithm to remove dynamic objects from point cloud data. The algorithm focuses on identifying and removing dynamic objects from sensor data, thereby creating a static scene suitable for traditional SLAM algorithms. Simulations conducted on the benchmark dataset demonstrate the algorithm's efficacy in successfully eliminating dynamic objects and reconstructing a stable static scene.
A Security Verification System Based on Bilinear Maps in the Multi-cloud Environment

As cloud computing continues to grow, an increasing amount of sensitive data is migrated to the cloud for storage and processing. However, sensitive data is usually stored in a single cloud environment, which poses several risks such as data leakage and a single point of failure. To address the above issues, a security verification system based on bilinear maps in the multi-cloud environment (i.e. SVS-BM) is proposed. In the paper, bilinear mapping technology is used to ensure the integrity and authenticity of user data during transmission and processing in the multi-cloud. Moreover, trusted middleware connects the terminal to multiple cloud service providers, which ensures efficient management and security verification of user data by segmenting and merging user data. Experimental results and performance analysis show that the system can effectively guarantee user data integrity and security in the multi-cloud environment.
A Train Operation Strategy Based on Train Convoy for Single-track Freight Railway

Single-track freight railway currently adopts the way of departure with a fixed headway under inter-station blocking system to organize train operation, due to the limitation of line conditions, which can not meet the growing freight transport demand. In order to improve transport capacity, this paper introduces the concept of train convoy into train scheduling problem of single-track freight railway, and proposes a train operation strategy on this basis. Firstly, this paper analyzes the impact of train convoy on the train operation strategy of single-track freight railway. Secondly, on the basis of considering the constraints of the number equality between arriving trains and departing trains at the departure station, the number of arrival and departure tracks at intermediate station and running time in section, a mixed integer programming model is constructed with a goal of maximizing railway freight volume at a fixed time. Thirdly, this paper designs Harris Hawks optimization algorithm (HHO) based on characteristics of the model to solve it. Finally, the experiments based on some single-track freight railway in China are implemented to verify the effectiveness of the model and algorithm. The results show that the train operation strategy proposed in the paper can increase transport capacity by 27.05 %.
Digitization of Traffic Laws: Methodologies and Usage for Monitoring Driving Compliance

Autonomous Vehicles (AVs) must adhere to traffic laws designed for human-driven vehicles. However, since current traffic laws are expressed in natural language and are inherently ambiguous, AVs encounter challenges in comprehending these laws. Therefore, digitizing traffic laws into a format that AVs can understand is crucial for safe and efficient driving. In this paper, a process for digitizing regulations is proposed, where each regulation is digitized into a temporal logic expression composed of computable atomic propositions. Based on this process, a vehicle-side online violation monitoring architecture is established. These works make AVs understand traffic laws easily. Several common but important regulations are used as examples to illustrate our work and are deployed on an AV for verification. The results demonstrate that the proposed monitoring architecture can monitor ego vehicle's illegal behavior in real-time and provide compliance suggestions, thereby helping AVs operate more safely.
Interactive-Teb: Effective Trajectory Planning in Mixed Traffic Scenarios Considering Kinodynamic Constraints

Trajectory planning in mixed traffic scenarios is a critical and demanding task in autonomous driving, since it necessitates addressing intricate interactions among traffic agents while conforming to kinodynamic constraints of the vehicle. Most existing methods struggle to simultaneously meet the above-mentioned requirements, resulting in unsafe driving behavior. To take this issue, we propose Interactive-TEB algorithm, which utilizes a time-optimal optimization framework to generate a safe and comfortable motion trajectory, considering kinodynamic constraints of the vehicle and obstacle avoidance. Moreover, Interactive-TEB incorporates interactive predicted trajectories of other traffic agents into trajectory optimization to consider the inevitable interactivity in mixed traffic scenarios. Extensive experimental evaluations across diverse mixed traffic scenarios demonstrate that the proposed algorithm outperforms existing methods in terms of safety, comfort, and effectiveness.
Investigating Perceived Digital Switching Cost and Customer Satisfaction-Loyalty Model on Video on Demand

Recently, the number of video-on-demand (VOD) subscribers increased as subscribers enjoyed watching various content during COVID-19. VOD platforms give captivating, inspiring, and commercial-free binge-watching experiences. As 75% of VOD growth was contributed by Indonesia and Thailand, VOD’s marketing practices and programming decisions using network brand identity approaches in Indonesia. Therefore, the competition of network provider companies is becoming more competitive. After examining VOD’s approach to network provider companies, this study considers the brand loyalty of VOD’s subscribers to Indonesian network providers. We use an online poll (survey) with 117 respondents to determine the factors influencing brand loyalty, customer satisfaction, perceived digital value, and perceived digital switching cost. The Structural Equation Modeling-Partial Least Square (SEM-PLS) approach and the Bootstrap parameter estimation method are utilized to ascertain the link between these latent variables. The study’s findings indicate that perceived digital value and perceived digital switching significantly affect brand loyalty. Based on the findings, we suggest implications to improve and maintain the value and price of the services.
Exploring Compact and Efficient Neural Networks for Real-Time Semantic Segmentation

Real-time semantic segmentation has profound implications in modern automated driving systems. For safe control, automated driving systems usually need accurate and real-time traffic information such as the positions of pedestrians, traffic signs, other cars, etc., which are mainly provided by semantic segmentation neural networks. However, it is challenging to build an efficient network that can achieve the optimal trade-off between inference speed and accuracy, due to the fact that high prediction accuracy neural network models are usually over-parameterized, their sheer sizes prevent them from deploying in resource-constrained systems. On the contrary, small-scale models face the problem of lacking accuracy. To solve this problem, we presented a novel method to build an efficient semantic segmentation network that provides a better balance between inference speed and prediction accuracy than the existing methods. Our model is built upon a two-branch network architecture, which allows our model to capture spatial details and contextual features efficiently. Moreover, we introduce a compact block to further compress and accelerate our model. We also apply the pseudo-labeling method to obtain higher prediction accuracy. Finally, we evaluate our method on public datasets and compare our method with the current state-of-the-art methods. Specifically, our method achieves 79.8% and 78.1% mean intersection over union (mIOU) on Cityscapes validation and test sets respectively, at 104FPS on an NVIDIA RTX 2080Ti.
Intelligent Diagnosis Method of Bearing Fault Based on SDP Image Fusion

Aiming at the problem that the vibration signal obtained by a single sensor is difficult to fully characterize the bearing fault characteristics, this paper proposes a bearing fault diagnosis method based on multi-sensor fusion SDP image and deep residual network (ResNet). The method firstly converts the vibration signals obtained from the acceleration sensors arranged at the drive end and the fan end respectively into snowflake images by the symmetrized dot pattern (SDP), then fuses the snowflake images corresponding to the different acceleration sensors as the different channels of the RGB images, and finally uses the fused SDP images as the input to the deep residual network (ResNet) to realise the fault diagnosis of the bearings. After experimental verification, the fault diagnosis accuracy of the proposed method can reach 98.8%, which is much higher than the fault diagnosis accuracy using a single sensor. Therefore, compared with the traditional single-sensor fault diagnosis, the multi-sensor SDP image fusion method proposed in this paper can more comprehensively characterize the bearing fault characteristics, and has better fault diagnosis effect.
A mobile crowdsensing task allocation method in the scenario of environmental protection

In mobile crowdsensing (MCS) environment, total carbon emission based on various travel mode is an important factor that affects the design of incentive mechanism. Aiming at the goal of maximizing task complete rate and minimizing total carbon emission, a task allocation method named TAEP is proposed in the paper. The proposed method contains two stages: preliminary participant selection stage and task allocation stage. In the first phase, based on the joint probability of task acceptance rate and experience of participants, a rough participant selection algorithm is designed to maximize task completion rate. Besides, geographical indistinguishability is adopted to protect participants’ location privacy. In the second phase, a task allocation algorithm based on dynamic programming is given according to the result of the first stage. Candidate participants who can minimize carbon emission within the budget limit are selected as winners. Experimental results on real dataset validate the effectiveness of the proposed method.
Vehicle-in-the-Loop Experimental Verification of Real-Time Embedded Computation on Microcontroller for Service-Oriented Energy-Optimal Strategies of Intelligent Electric Vehicles

In this paper, a real-time energy-optimal strategy exploiting preview information resulting from connectivity and autonomous vehicle (CAV) technology is verified by experimental analyses linked with on-board computing methods on a real vehicle-in-the-loop testbed with virtual road and traffic light system. An energy-optimal deceleration planning/following system (EDPS) as a service-oriented technology for electrified vehicles utilizing preview information is applied to a micro-controller, where the data access route and location on the given system architecture are optimized to shorten computing time. Also, two types of multicore strategies are comparatively analyzed to efficiently operate computing resources of the embedded controller as well as to distribute computing loads, and the strategy comparisons indicate that a function-level task partition considering target cores in advance can practically reduce computing loads. In the vehicle-in-the-loop simulations (VILS) with a realistic driving on the virtual road and CAV technology-based information, the embedded EDPS planning results illustrate that the energy-optimal speed profiles are properly computed on a commercial automotive microcontroller while stably processing real-time data input/output and optimal planning within the given time constraints.
MALE-A: Stimuli and Cause Prediction for Maneuver Planning via Graph Neural Networks in Autonomous Driving

The driving behavior inherently involves different tasks, one such task corresponding to maneuver planning to navigate safely the road network. Yet, the planned maneuver can not be executed all the time due to the presence of other vehicles, traffic signs or traffic rules. This work proposes a two-layer approach for decision-making, where the first layer involves planning the high-level maneuver, while the second layer predicts the stimuli of maneuver execution. To predict the required stimuli action, the deep learning model introduced in this work utilizes inputs such as the planned maneuver, automotive sensor data, and an interaction graph constructed from detected traffic participants and features of the traffic scene. This model is based on Graph Neural Networks (GNNs) for extracting spatial features from the graph, while also tracking the temporal context of the traffic scene using Recurrent Neural Networks (RNNs). Training and validation of the model are performed using the HDD dataset, with the application of data oversampling and undersampling techniques to stabilize the learning process. Ultimately, successful implementation of the proposed two-layer approach and model for decision-making is demonstrated across various complex traffic scenes.