Video deepfake detection using Particle Swarm Optimization improved deep neural networks

As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets

Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection

Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related semantics. In this paper, we propose a novel ``pre-train, adapt and detect" paradigm to detect camouflaged objects. By introducing a large pre-trained model, abundant knowledge learned from massive multi-modal data can be directly transferred to COD. A lightweight parallel adapter is inserted to adjust the features suitable for the downstream COD task. Extensive experiments on four challenging benchmark datasets demonstrate that our method outperforms existing state-of-the-art COD models by large margins. Moreover, we design a multi-task learning scheme for tuning the adapter to exploit the shareable knowledge across different semantic classes. Comprehensive experimental results showed that the generalization ability of our model can be substantially improved with multi-task adapter initialization on source tasks and multi-task adaptation on target tasks

Dose-sparing effect of lapatinib co-administered with a high-fat enteral nutrition emulsion: preclinical pharmacokinetic study

Background Lapatinib is an oral small-molecule tyrosine kinase inhibitor indicated for advanced or metastatic HER2-positive breast cancer. In order to reduce the treatment cost, a high-fat enteral nutrition emulsion TPF-T was selected as a dose-sparing agent for lapatinib-based therapies. This study aimed to investigate the effect of TPF-T on lapatinib pharmacokinetics. Methods First, a simple and rapid liquid chromatography tandem mass spectrometry (LC–MS/MS) method was developed to quantitatively evaluate lapatinib in rabbit plasma. The method was fully validated according to the China Pharmacopoeia 2020 guidance. Rabbits and rats were chosen as the animal models due to their low and high bile flows, respectively. The proposed LC–MS/MS method was applied to pharmacokinetic studies of lapatinib, with or without TPF-T, in rabbit and rat plasma. Results The LC–MS/MS method revealed high sensitivity and excellent efficiency. In the rabbit model, co-administration with TPF-T resulted in a 32.2% increase in lapatinib exposure. In the rat model, TPF-T had minimal influence on the lapatinib exposure. In both models, TPF-T was observed to significantly elevate lapatinib concentration in the absorption phase. Conclusion Co-administration with TPF-T had a moderate effect on increasing exposure to lapatinib. Dose sparing using a high-fat liquid diet is potentially feasible for lapatinib-based therapies

Risk Factors and Medico-Economic Effect of Pancreatic Fistula after Pancreaticoduodenectomy

The study aimed to uncover the risk factors for the new defined pancreatic fistula (PF) and clinical related PF (CR-PF) after pancreaticoduodenectomy (PD) surgery and to evaluate the medico-economic effect of patients. A total of 412 patients were classified into two groups according to different criteria, PF and NOPF according to PF occurrence: CR-PF (grades B and C) and NOCR-PF (grade A) based on PF severity. A total of 28 factors were evaluated by univariate and multivariate logistic regression test. Hospital charges and stays of these patients were assessed. The results showed that more hospital stages and charges are needed for patients in PF and CR-PF groups than in NOPF and NOCR-PF groups (P<0.05). The excessive drinking, soft remnant pancreas, preoperative albumin, and intraoperative blood transfusion are risk factors affecting both PF and CR-PF incidence. More professional surgeons can effectively reduce the PF and CR-PF incidence. Patients with PF and CR-PF need more hospital costs and stages than that in NOPF and NOCR-PF groups. It is critical that surgeons know the risk factors related to PF and CR-PF so as to take corresponding therapeutic regimens for each patient

The Supply and Demand Mechanism of Electric Power Retailers and Cellular Networks Based on Matching Theory

With the rapid increase of wireless network traffic, the energy consumption of mobile network operators (MNOs) continues to increase, and the electricity bill has become an important part of the operating expenses of MNOs. The power grid as the power supplier of cellular networks is also developing rapidly. In this paper, we design two levels of bilateral matching algorithm to solve the energy management of micro-grid connected cellular networks. There are multiple retailers (sellers) and clusters (buyers) in our system model, which determine the transaction price and trading energy respectively and have a certain influence on the balance of energy supply and demand. Retailers make more profits by adjusting the price of electricity in matching algorithm M-1, depending on the energy they capture and the level of storage. At the same time, clusters adjust the electricity consumption through matching algorithm M-2 and power allocation on the basis of ensuring the quality of users&rsquo; service. Finally, the performance of the proposed scheme is evaluated by changing various parameters in the simulation

Characterizing Rockbursts and Analysis on Hilbert-Huang Transform Spectrum of Microseismic Events, Shuangjiangkou Hydropower Station, Based on Microseismic Monitoring

The Shuangjiangkou hydropower station in China has complex geological conditions with high in situ stress. During the tunnel excavation, rockbursts occurred frequently, which seriously affected construction progress. Microseismic (MS) monitoring technology was used to explore rock MS activities to predict rockbursts. The MS monitoring system can capture a large number of MS signals. Based on Hilbert–Huang transform (HHT) instantaneous frequency analysis technology, using MATLAB software (R2022a) to write a program to convert the MS waveform, the frequency and energy characteristics of MS signals at a certain time can be obtained. By analyzing the frequency and energy characteristics of every event, the microseism active areas can be determined, and then rockbursts can be predicted scientifically. This paper selected two different construction sites, which were the main powerhouse and the access tunnel in the main powerhouse, as the research background. Introducing HHT instantaneous time–frequency analysis technology conducted MS event dynamic analysis and predicted rockbursts. The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics at a certain time and reflected the change laws of signal instantaneous energy and local abrupt change information. The results indicated that some parameter anomalies in the event spectrum can predict rockbursts. For complex tunnel construction conditions, the HHT time–frequency analysis technology can realize a new idea of using a single-channel signal to predict rockbursts, which was very meaningful

The Supply and Demand Mechanism of Electric Power Retailers and Cellular Networks Based on Matching Theory

With the rapid increase of wireless network traffic, the energy consumption of mobile network operators (MNOs) continues to increase, and the electricity bill has become an important part of the operating expenses of MNOs. The power grid as the power supplier of cellular networks is also developing rapidly. In this paper, we design two levels of bilateral matching algorithm to solve the energy management of micro-grid connected cellular networks. There are multiple retailers (sellers) and clusters (buyers) in our system model, which determine the transaction price and trading energy respectively and have a certain influence on the balance of energy supply and demand. Retailers make more profits by adjusting the price of electricity in matching algorithm M-1, depending on the energy they capture and the level of storage. At the same time, clusters adjust the electricity consumption through matching algorithm M-2 and power allocation on the basis of ensuring the quality of users&rsquo; service. Finally, the performance of the proposed scheme is evaluated by changing various parameters in the simulation

Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS