Video Deepfake Classification Using Particle Swarm Optimization-based Evolving Ensemble Models

The recent breakthrough of deep learning based generative models has led to the escalated generation of photo-realistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNN-Recurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyper-parameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller’s method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts

Quality assessment metric of stereo images considering cyclopean integration and visual saliency

In recent years, there has been great progress in the wider use of three-dimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS) studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called “cyclopean saliency”, which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judgment than the state-of-art SIQA metrics. 3D saliency performance is also compared with “cyclopean saliency” in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality

Neural inference search for multiloss segmentation models

Semantic segmentation is vital for many emerging surveillance applications, but current models cannot be relied upon to meet the required tolerance, particularly in complex tasks that involve multiple classes and varied environments. To improve performance, we propose a novel algorithm, neural inference search (NIS), for hyperparameter optimization pertaining to established deep learning segmentation models in conjunction with a new multiloss function. It incorporates three novel search behaviors, i.e., Maximized Standard Deviation Velocity Prediction, Local Best Velocity Prediction, and n -dimensional Whirlpool Search. The first two behaviors are exploratory, leveraging long short-term memory (LSTM)-convolutional neural network (CNN)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions

Quality assessment metric of stereo images considering cyclopean integration and visual saliency

This paper was accepted for publication in the journal Information Sciences and the definitive published version is available at http://dx.doi.org/10.1016/j.ins.2016.09.004.In recent years, there has been great progress in the wider use of three-dimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS) studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called “cyclopean saliency”, which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judgment than the state-of-art SIQA metrics. 3D saliency performance is also compared with “cyclopean saliency” in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality

Carvedilol suppresses migration and invasion of malignant breast cells by inactivating Src involving cAMP/PKA and PKCδ signaling pathway

Context: Carvedilol (CAR) can inhibit cell growth and induce cell apoptosis in breast cancer in vitro. But it is still not known whether CAR affects the migration and invasion of breast cancer cells. Aims: To investigate the effects of CAR on migration and invasion of breast cancer cells and its corresponding signal pathways. Settings and Design: Firstly, the invasive potential of breast cancer cells were investigated after incubation with CAR and/or norepinephrine (NE). If the invasive potential of breast cancer cells were inhibited by CAR, then the signal pathways related to migration and invasion were detected, such as Src, cyclic adenosine monohposphate (cAMP)/protein kinase A (PKA), etc. Subjects and Methods: Membrane invasion culture system (MICS) chamber was used to measure the invasive and migratory potential of breast cancer cells. Western blot analysis and small interfering RNA (siRNA) transfection experiment were employed to determine the signal pathway adopted by CAR in suppressing migration and invasion of MDA-MB-231 and MCF-7 cells. cAMP-Glo and PKCδ kinase activity assay kit were used to measure cAMP and PKCδ activity, respectively, according to the manufacturer′s instructions. Statistical analysis used: Statistical differences between the mean values of control and experimental groups were determined using two-tailed, unpaired Student′s t-tests. Results: CAR significantly decreased the potential of migration and invasion of breast cancer cells. CAR inhibited Src activation in MDA-MB-231 and MCF-7 cells through blocking beta or alpha adrenergic receptor (ADR), respectively. Furthermore, CAR suppressed the Src activation through different signaling pathways. Under treatment of CAR, cAMP/PKA-Src pathway was inhibited in MDA-MB-231 cells; but in MCF-7 cells, CAR mainly inhibited the PKCδ-Src pathway. Conclusions: CAR was an anti-metastatic agent, which targets Src involving cAMP/PKA or PKCδ pathway in malignant breast cells

The effects of combined exercise intervention based on Internet and social media software for postoperative patients with breast cancer: study protocol for a randomized controlled trial

Abstract Background Many randomized controlled trials have investigated the effects of exercise on the rehabilitation of patients with breast cancer. However, the exercise forms used in most previous studies were monotonous. Therefore, we designed a protocol to estimate the effects of combined exercise intervention using Internet and social media software on the rehabilitation of postoperative patients with BC. Methods/Design This study protocol is a randomized control trial with an intervention time of 12 weeks. After completing baseline questionnaire and physical fitness tests, the participants are randomized to the study group or the control group. Procedure contents of exercise intervention in the study group include: via phone step-recording app, ask the individuals to complete the target number of steps within a specified period of exercise, four times per week; face-to-face remote video guidance of individuals on muscle training, three times per week; common knowledge of physical exercise BC rehabilitation will be pushed regularly by social media apps every day. The control group will receive normal treatment and rehabilitation according to daily specifications of the hospital. The primary outcome will be the quality of life. The secondary outcomes are physical fitness and social cognitive indicators. Discussion This study is a clinical trial to estimate the effects of combined exercise intervention based on the Internet and social media software for postoperative patients with breast cancer (BC). If expected results are achieved in this study, measures and methods of BC rehabilitation will be enriched. Trial registration Chinese Clinical Trial Register, ChiCTR-IPR-17012368. Registered on 14 August 2017

Cooperative power management for range extended electric vehicle based on internet of vehicles

The dramatic progress in internet of vehicles (IoVs) inspires further development in electrified transportation, and abundant information exchanged in IoVs can be infused into vehicles to promote the controlling performance of electric vehicles (EVs) via vehicle-environment cooperation. In this paper, a cooperative power management strategy (PMS) is advanced for the range extended electric vehicle (REEV). To this end, the studied REEV is accurately modelled first, laying an efficient platform for strategy design. Based on the advanced framework of IoVs, the cooperative PMS is meticulously developed via incorporating the self-learning explicit equivalent minimization consumption strategy (SL-eECMS) and adaptive neuro-fuzzy inference system (ANFIS) based online charging management within on-board power sources in the REEV. The brand-new SL-eECMS achieves preferable balance between the optimal effect and instant implementation capability through integrating the improved quantum particle swarm optimization (iQPSO), and ANFIS grasps future driving status macroscopically, offering the predicted charging request for online charge management. The substantial simulations and hardware-in-the-loop (HIL) test manifest that the proposed cooperative PSMS can coherently and efficiently manage power flow within power sources in the REEV, highlighting its anticipated preferable performance

Periductal Mastitis: An Inflammatory Disease Related to Bacterial Infection and Consequent Immune Responses?

Periductal mastitis (PDM) is a prolonged inflammatory disease, but the cause of PDM is poorly understood. In the present case control study, 87 PDM and 87 healthy controls were enrolled and the results were evaluated to identify the significant risk factors for PDM. To investigate the roles of bacterial infection and critical cytokines expression, 16S rRNA gene sequencing and bacterial culturing were conducted. We also measured the levels of interferon-γ, interleukin-12A, and interleukin-17A by semiquantitative immunohistochemistry method. In a multivariable logistic regression model, we identified overweight/obesity and late onset of menarche as independent risk factors for PDM. In contrast, age of first birth >27 years had a protective effect. With 16S rRNA gene sequencing, we confirmed bacterial infections were found in all PDM patients, but none of the control patients was positive on the gene expression of 16S rRNA. Our results also demonstrated significant increases of the IFN-γ and IL-12A expression in PDM, but there was no difference in IL-17A expression in these two groups. Taken together, this study suggests that reproductive factors and overweight/obesity are possible predisposing risk factors for PDM. Bacterial infection and the increased expression of some proinflammatory cytokines are associated with the pathogenesis of this disease

Cooperative power management for range extended electric vehicle based on internet of vehicles

The dramatic progress in internet of vehicles (IoVs) inspires further development in electrified transportation, and abundant information exchanged in IoVs can be infused into vehicles to promote the controlling performance of electric vehicles (EVs) via vehicle-environment cooperation. In this paper, a cooperative power management strategy (PMS) is advanced for the range extended electric vehicle (REEV). To this end, the studied REEV is accurately modelled first, laying an efficient platform for strategy design. Based on the advanced framework of IoVs, the cooperative PMS is meticulously developed via incorporating the self-learning explicit equivalent minimization consumption strategy (SL-eECMS) and adaptive neuro-fuzzy inference system (ANFIS) based online charging management within on-board power sources in the REEV. The brand-new SL-eECMS achieves preferable balance between the optimal effect and instant implementation capability through integrating the improved quantum particle swarm optimization (iQPSO), and ANFIS grasps future driving status macroscopically, offering the predicted charging request for online charge management. The substantial simulations and hardware-in-the-loop (HIL) test manifest that the proposed cooperative PSMS can coherently and efficiently manage power flow within power sources in the REEV, highlighting its anticipated preferable performance