With the development of location-acquisition technologies, there are a huge number of mobile trajectories generated in a variety of domains. However, due to the constraints of device and environment, many trajectories are recorded at a low sampling rate, which increases the uncertainty between two consecutive sampled points in the trajectories. Our task is to recover a high-sampled trajectory based on the irregular low-sampled trajectory. There are two major problems with traditional solutions. First, many of these methods rely on heuristic search algorithms or simple probabilistic models, which cannot well capture complex sequential dependencies or global correlations. Second, most of the previous studies adopt a common preprocessing to map the trajectory into discrete units, which bring noises. To address the above difficulties, we propose a novel Deep Hybrid Trajectory Recovery model. Our model extends the classic sequence-to-sequence generation framework by implementing a subsequence-to-sequence recovery model tailored for the current task, named subseq2seq. In order to effectively capture spatiotemporal correlations, we adopt both spatial and temporal attentions for enhancing the model performance. Furthermore, we integrate the subseq2seq with a Kalman filter component to calibrate coarse-grained discrete units as fine-grained coordinates. Extensive results on real-world datasets have shown the superiority of the proposed model.

Identity-based remote data auditing schemes can verify data integrity and provide a simple identity authentication and management for multiple users. However, prior works on identity-based remote data auditing lack the support of dynamic operations. In these schemes, tag generation is linked to the index of data block, which is related to update operations such as modification, insertion and deletion. If users perform dynamic operations on a data block, the tags of all subsequent blocks need to be modified. It means that if users want to update data on a big data platform, they have to download the whole file, update the file and send the updated file to the big data platform. Such pattern will bring huge communication overhead. In this paper, we propose an identity-based dynamic data auditing scheme which supports dynamic data operations, including modification, insertion and deletion. As far as we know, there is still no other identity-based data auditing scheme that supports dynamic operations. In particular, to achieve efficient dynamic operations, we use the data structure of Merkle hash tree for block tag authentication, which helps update data with integrity assurance. Analyses of security and performance show that the proposed scheme is efficient and secure.

The rapid proliferation of internet data, particularly through social media, has amplified the need for effective sentiment analysis, including the complex task of sarcasm detection. This paper presents a novel multi-modal sarcasm detection model leveraging cue learning techniques to address the challenges posed by data scarcity, especially in low-resource languages. The proposed model builds upon the CLIP architecture, integrating text and image modalities to co-learn sarcasm cues. The methodology encompasses discrete prompt generation, learnable continuous vectors, and multi-modal fusion to enhance detection accuracy. The multi-modal fusion process demonstrates a symmetric integration of text and image data, leading to improved performance. Experimental results on the Twitter Multi-modal Sarcasm Detection Dataset (MSD) demonstrate significant performance improvements over traditional models, highlighting the model’s robustness and adaptability in small-sample scenarios. This research contributes a practical solution for nuanced sentiment analysis, paving the way for advanced applications in public opinion monitoring and AI-driven decision-making processes.

In recent years, the development of computer technology has promoted the informatization and intelligentization of hospital management systems and thus produced a large amount of medical data. These medical data are valuable resources for research. We can obtain inducers and unknown symptoms that can help discover diseases and make earlier diagnoses. Hypertensive disorder in pregnancy (HDP) is a common obstetric complication in pregnant women, which has severe adverse effects on the life safety of pregnant women and fetuses. However, the early and mid-term symptoms of HDP are not obvious, and there is no effective solution for it except for terminating the pregnancy. Therefore, detecting and preventing HDP is of great importance. This study aims at the preprocessing of pregnancy examination data, which serves as a part of HDP prediction. We found that the problem of missing data has a large impact on HDP prediction. Unlike general data, pregnancy examination data have high dimension and a high missing rate, are in a time series, and often have many non-linear relations. Current methods are not able to process the data effectively. To this end, we propose an improved bi-LSTM-based missing value imputation approach. It combines traditional machine learning and bidirectional LSTM to deal with missing data of pregnancy examination data. Our missing value imputation method obtains a good effect and improves the accuracy of the later prediction of HDP using examination data.

With the development of consumer-grade drone products, drones have been good carriers for many IoT (Internet of Things) applications, especially as a part of edge computing to deliver things, collect data, or monitor objects. This paper develops a Unity3D-based quadrotor dynamics simulation component on the original platform. In order to complete the simulation, a force analysis is performed for the quadrotor to establish a physical model based on Newtonian mechanics and Euler angles, and a PID (Proportion Integral Differential)-based control model is developed based on this physical model, which performs a fixed-point control for the simulation. The component introduces weather impact factors into the physical model through an environmental computational model and presents weather effects to the user interface. In addition, we add a scene customization function and a multi-aircraft formation flight function to the original simulation, and optimize the interface presentation scheme for multi-aircraft formation flight. The quadrotor simulation model in this paper solves the problem that the original simulation system is not realistic enough and significantly improves the usability and stability of quadrotors working in the IoT area.

Gestational diabetes mellitus and hypertension are two common pregnancy complications, which seriously threaten the life safety of pregnant women and adversely affect the growth and development of the fetus. Therefore, it is of great significance to detect and prevent hypertension and diabetes at an early stage of pregnancy. Each pregnant woman will undergo multiple tests at different gestational weeks. This progress produces lots of pregnancy examination data. These data can reflect the dynamic changes of pregnant women’s health indicators during pregnancy. This study aims to establish gestational diabetes and hypertension prediction model with a machine learning method based on real pregnancy examination data from the hospital. We use Logistic Regression, XGBoost, LightGBM, and Neural Network Model based on LSTM to do the prediction, respectively, and compare the performance. We check the prediction accuracy at different stages of pregnancy. We found that with pregnancy examination data at all gestational weeks, the predictive AUCs for diabetes and hypertension can reach 0.92 and 0.87, respectively. At 16th gestational week, the AUCs are 0.68 for diabetes and 0.70 for hypertension. We extract the checking items which are most important and get a simplified model with a modest reduction in predictive accuracy. This study demonstrates that based on several routine pregnancy examination items we can establish a machine learning model to detect and predict gestational diabetes and hypertension. This can be used as a diagnostic aid and is conducive to early prevention and treatment.