BAGEL: Backdoor Attacks against Federated Contrastive Learning

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a global encoder with unlabeled data, and the global encoder could be widely used as a feature extractor to build models for many downstream tasks. However, FCL is also vulnerable to many security threats (e.g., backdoor attacks) due to its distributed nature, which are seldom investigated in existing solutions. In this paper, we study the backdoor attack against FCL as a pioneer research, to illustrate how backdoor attacks on distributed local clients act on downstream tasks. Specifically, in our system, malicious clients can successfully inject a backdoor into the global encoder by uploading poisoned local updates, thus downstream models built with this global encoder will also inherit the backdoor. We also investigate how to inject backdoors into multiple downstream models, in terms of two different backdoor attacks, namely the \textit{centralized attack} and the \textit{decentralized attack}. Experiment results show that both the centralized and the decentralized attacks can inject backdoors into downstream models effectively with high attack success rates. Finally, we evaluate two defense methods against our proposed backdoor attacks in FCL, which indicates that the decentralized backdoor attack is more stealthy and harder to defend

Nanozymes for the Therapeutic Treatment of Diabetic Foot Ulcers

Diabetic foot ulcers (DFU) are chronic, refractory wounds caused by diabetic neuropathy, vascular disease, and bacterial infection, and have become one of the most serious and persistent complications of diabetes mellitus because of their high incidence and difficulty in healing. Its malignancy results from a complex microenvironment that includes a series of unfriendly physiological states secondary to hyperglycemia, such as recurrent infections, excessive oxidative stress, persistent inflammation, and ischemia and hypoxia. However, current common clinical treatments, such as antibiotic therapy, insulin therapy, surgical debridement, and conventional wound dressings all have drawbacks, and suboptimal outcomes exacerbate the financial and physical burdens of diabetic patients. Therefore, development of new, effective and affordable treatments for DFU represents a top priority to improve the quality of life of diabetic patients. In recent years, nanozymes-based diabetic wound therapy systems have been attracting extensive interest by integrating the unique advantages of nanomaterials and natural enzymes. Compared with natural enzymes, nanozymes possess more stable catalytic activity, lower production cost and greater maneuverability. Remarkably, many nanozymes possess multienzyme activities that can cascade multiple enzyme-catalyzed reactions simultaneously throughout the recovery process of DFU. Additionally, their favorable photothermal-acoustic properties can be exploited for further enhancement of the therapeutic effects. In this review we first describe the characteristic pathological microenvironment of DFU, then discuss the therapeutic mechanisms and applications of nanozymes in DFU healing, and finally, highlight the challenges and perspectives of nanozyme development for DFU treatment

Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

Various rehabilitation robots have been employed to recover the motor function of stroke patients. To improve the effect of rehabilitation, robots should promote patient participation and provide compliant assistance. This paper proposes an adaptive admittance control scheme (AACS) consisting of an admittance filter, inner position controller, and electromyography (EMG)-driven musculoskeletal model (EDMM). The admittance filter generates the subject's intended motion according to the joint torque estimated by the EDMM. The inner position controller tracks the intended motion, and its parameters are adjusted according to the estimated joint stiffness. Eight healthy subjects were instructed to wear the ankle exoskeleton robot, and they completed a series of sinusoidal tracking tasks involving ankle dorsiflexion and plantarflexion. The robot was controlled by the AACS and a non-adaptive admittance control scheme (NAACS) at four fixed parameter levels. The tracking performance was evaluated using the jerk value, position error, interaction torque, and EMG levels of the tibialis anterior (TA) and gastrocnemius (GAS). For the NAACS, the jerk value and position error increased with the parameter levels, and the interaction torque and EMG levels of the TA tended to decrease. In contrast, the AACS could maintain a moderate jerk value, position error, interaction torque, and TA EMG level. These results demonstrate that the AACS achieves a good tradeoff between accurate tracking and compliant assistance because it can produce a real-time response to stiffness changes in the ankle joint. The AACS can alleviate the conflict between accurate tracking and compliant assistance and has potential for application in robot-assisted rehabilitation

Deep Learning for Detection of Object-Based Forgery in Advanced Video

Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results

Performance Test and Stability Analysis of Jute Ecological Bag on Subgrade Slope

Ecological bags have been gradually adopted for ecological protection on the subgrade slope because of their good soil fixation effect, strong water retention performance, fast construction and other advantages. Ecological bags made of natural jute have obvious attributes in environmental protection and economic efficiency. In this study, the tensile and the tearing strength of the common-used jute cloth have been tested. The result shows that the strength meet the requirements of the standards. Compared with current frequently-used ecological bag made of non-woven cloth, the jute ecological bag has large apertures, which is suitable for the growth of dicotyledon plants. Moreover, its high friction coefficient with the soil is beneficial to the structure stability on the slope. On the other hand, a stability evaluation method has been established for the jute ecological bags on the subgrade slope under natural and heavy rainfall conditions. Then the steel wire mesh fixed by the anchor rods is used to enhance the stability of the jute ecological bags, which constitute the ecological protection system for the subgrade slope. Also, the stability of the protection system is analyzed and calculated