Towards practicalization of blockchain-based decentralized applications

Blockchain can be defined as an immutable ledger for recording transactions, maintained in a distributed network of mutually untrusting peers. Blockchain technology has been widely applied to various fields beyond its initial usage of cryptocurrency. However, blockchain itself is insufficient to meet all the desired security or efficiency requirements for diversified application scenarios. This dissertation focuses on two core functionalities that blockchain provides, i.e., robust storage and reliable computation. Three concrete application scenarios including Internet of Things (IoT), cybersecurity management (CSM), and peer-to-peer (P2P) content delivery network (CDN) are utilized to elaborate the general design principles for these two main functionalities. Among them, the IoT and CSM applications involve the design of blockchain-based robust storage and management while the P2P CDN requires reliable computation. Such general design principles derived from disparate application scenarios have the potential to realize practicalization of many other blockchain-enabled decentralized applications. In the IoT application, blockchain-based decentralized data management is capable of handling faulty nodes, as designed in the cybersecurity application. But an important issue lies in the interaction between external network and blockchain network, i.e., external clients must rely on a relay node to communicate with the full nodes in the blockchain. Compromization of such relay nodes may result in a security breach and even a blockage of IoT sensors from the network. Therefore, a censorship-resistant blockchain-based decentralized IoT management system is proposed. Experimental results from proof-of-concept implementation and deployment in a real distributed environment show the feasibility and effectiveness in achieving censorship resistance. The CSM application incorporates blockchain to provide robust storage of historical cybersecurity data so that with a certain level of cyber intelligence, a defender can determine if a network has been compromised and to what extent. The CSM functions can be categorized into three classes: Network-centric (N-CSM), Tools-centric (T-CSM) and Application-centric (A-CSM). The cyber intelligence identifies new attackers, victims, or defense capabilities. Moreover, a decentralized storage network (DSN) is integrated to reduce on-chain storage costs without undermining its robustness. Experiments with the prototype implementation and real-world cyber datasets show that the blockchain-based CSM solution is effective and efficient. The P2P CDN application explores and utilizes the functionality of reliable computation that blockchain empowers. Particularly, P2P CDN is promising to provide benefits including cost-saving and scalable peak-demand handling compared with centralized CDNs. However, reliable P2P delivery requires proper enforcement of delivery fairness. Unfortunately, most existing studies on delivery fairness are based on non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc P2P setting. To address this issue, an expressive security requirement for desired fair P2P content delivery is defined and two efficient approaches based on blockchain for P2P downloading and P2P streaming are proposed. The proposed system guarantees the fairness for each party even when all others collude to arbitrarily misbehave and achieves asymptotically optimal on-chain costs and optimal delivery communication

External characteristic analysis and stator parameter optimization for a torque converter

The performance of a flattened hydraulic torque converter is optimized with orthogonal experiment and response surface method, considering parameters of its stator blade, defined with nonuniform rotational B-splines. The optimization model, with maximum of the stalling torque ratio as an objective, is determined through an external characteristic statistical analysis under the new European Driving Cycle condition. The optimization results show that the stall torque ratio is increased by 10.83%, while the highest efficiency is above 84%

Dynamic changes in enzyme activities and phenolic content during in vitro rooting of tree peony (Paeonia suffruticosa Andr.) plantlets

The dynamic changes of phenolic content and peroxidase (POD), polyphenol oxidase (PPO), indole-3-acetic acid oxidase (IAAO) and phenylalanine ammonia lyase (PAL) activities were assessed during the in vitro rooting process of three cultivars of tree peony (Paeonia suffruticosa Andr.). These changes in enzyme-related activity and phenolic content__observed at the level of the whole plant__differed during the first 20 days of the rooting process in easy-to-root ‘Feng Dan Bai’ cultivar and difficult-to-root ‘Wu Long Peng Sheng’ and ‘Tai Ping Hong’ cultivars, and in most cases they were actually opposite. The ease with which ‘Feng Dan Bai’ was able to root was closely related to the activity of all four enzymes (POD, PPO, IAAO, PAL) as well as to the phenolic content

Knowledge Restore and Transfer for Multi-label Class-Incremental Learning

Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in class-incremental learning, these methods have difficulty in solving the MLCIL problem due to label absence and information dilution. In this paper, we propose a knowledge restore and transfer (KRT) framework for MLCIL, which includes a dynamic pseudo-label (DPL) module to restore the old class knowledge and an incremental cross-attention(ICA) module to save session-specific knowledge and transfer old class knowledge to the new model sufficiently. Besides, we propose a token loss to jointly optimize the incremental cross-attention module. Experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting on multi-label class-incremental learning tasks

CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning

In real-world applications, dynamic scenarios require the models to possess the capability to learn new tasks continuously without forgetting the old knowledge. Experience-Replay methods store a subset of the old images for joint training. In the scenario of more strict privacy protection, storing the old images becomes infeasible, which leads to a more severe plasticity-stability dilemma and classifier bias. To meet the above challenges, we propose a new architecture, named continual expansion and absorption transformer~(CEAT). The model can learn the novel knowledge by extending the expanded-fusion layers in parallel with the frozen previous parameters. After the task ends, we losslessly absorb the extended parameters into the backbone to ensure that the number of parameters remains constant. To improve the learning ability of the model, we designed a novel prototype contrastive loss to reduce the overlap between old and new classes in the feature space. Besides, to address the classifier bias towards the new classes, we propose a novel approach to generate the pseudo-features to correct the classifier. We experiment with our methods on three standard Non-Exemplar Class-Incremental Learning~(NECIL) benchmarks. Extensive experiments demonstrate that our model gets a significant improvement compared with the previous works and achieves 5.38%, 5.20%, and 4.92% improvement on CIFAR-100, TinyImageNet, and ImageNet-Subset

Microalgal diversity enhances water purification efficiency in experimental microcosms

The losses of biodiversity have impaired functioning and provision of ecosystem services, and the relationship between biodiversity and ecosystem functioning has emerged as a central issue in environmental sciences. However, the majority of relevant studies are conducted in terrestrial ecosystems, and they focus predominantly on the relationship between community diversity and biomass production of terrestrial vegetation. At present, water eutrophication represents an increasingly serious problem worldwide, and the use of aquatic organisms for improving water quality represents a promising approach. However, more focus is placed on the selection of certain aquatic organisms with good performance, but neglects the effects of biodiversity in the process of water purification and the underlying mechanisms. In the present study, five microalgal species commonly found in freshwater ecosystems were used to assembly experimental microcosms with varying microalgal richness and composition. We analyzed the relationship between microalgal diversity and nitrogen removal efficiency based on mixed-effect models, and further explored the underlying mechanism of microalgal diversity in the process of water quality improvement. The results showed that with an increase in microalgal diversity, nitrogen removal efficiency of microalgal communities also increased. A further analysis of the impacts of microalgal diversity showed that the complementarity effect increased while the selection effect decreased with an increase in microalgal diversity. Meanwhile, there was a significantly positive relationship between microalgal diversity and the total abundance of microalgae. On the one hand, the present study clearly demonstrates two positive diversity-ecosystem functioning relationships. On the other hand, the present study also reveals the underlying mechanism by which microalgal diversity influences nitrogen removal efficiency, namely, high-diversity microalgal communities could use limiting nutrients such as nitrogen in a more efficient and complementary manner (e.g., stronger complementarity effect in high-diversity communities), convert them into higher aggregate community properties (e.g., higher total abundance of microalge in high-diversity communities), and thus exhibit higher purification capacity (e.g., higher nitrogen removal efficiency in high-diversity communities). Under the scenario that global ecosystems are experiencing high rates of anthropogenic nutrient inputs, the use of diverse microalgal species with proper management may help provide a promising approach for improving water quality

Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?

Deep neural networks have significantly improved the performance of face forgery detection models in discriminating Artificial Intelligent Generated Content (AIGC). However, their security is significantly threatened by the injection of triggers during model training (i.e., backdoor attacks). Although existing backdoor defenses and manual data selection can mitigate those using human-eye-sensitive triggers, such as patches or adversarial noises, the more challenging natural backdoor triggers remain insufficiently researched. To further investigate natural triggers, we propose a novel analysis-by-synthesis backdoor attack against face forgery detection models, which embeds natural triggers in the latent space. We thoroughly study such backdoor vulnerability from two perspectives: (1) Model Discrimination (Optimization-Based Trigger): we adopt a substitute detection model and find the trigger by minimizing the cross-entropy loss; (2) Data Distribution (Custom Trigger): we manipulate the uncommon facial attributes in the long-tailed distribution to generate poisoned samples without the supervision from detection models. Furthermore, to completely evaluate the detection models towards the latest AIGC, we utilize both state-of-the-art StyleGAN and Stable Diffusion for trigger generation. Finally, these backdoor triggers introduce specific semantic features to the generated poisoned samples (e.g., skin textures and smile), which are more natural and robust. Extensive experiments show that our method is superior from three levels: (1) Attack Success Rate: ours achieves a high attack success rate (over 99%) and incurs a small model accuracy drop (below 0.2%) with a low poisoning rate (less than 3%); (2) Backdoor Defense: ours shows better robust performance when faced with existing backdoor defense methods; (3) Human Inspection: ours is less human-eye-sensitive from a comprehensive user study

SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects

To interact with daily-life articulated objects of diverse structures and functionalities, understanding the object parts plays a central role in both user instruction comprehension and task execution. However, the possible discordance between the semantic meaning and physics functionalities of the parts poses a challenge for designing a general system. To address this problem, we propose SAGE, a novel framework that bridges semantic and actionable parts of articulated objects to achieve generalizable manipulation under natural language instructions. More concretely, given an articulated object, we first observe all the semantic parts on it, conditioned on which an instruction interpreter proposes possible action programs that concretize the natural language instruction. Then, a part-grounding module maps the semantic parts into so-called Generalizable Actionable Parts (GAParts), which inherently carry information about part motion. End-effector trajectories are predicted on the GAParts, which, together with the action program, form an executable policy. Additionally, an interactive feedback module is incorporated to respond to failures, which closes the loop and increases the robustness of the overall framework. Key to the success of our framework is the joint proposal and knowledge fusion between a large vision-language model (VLM) and a small domain-specific model for both context comprehension and part perception, with the former providing general intuitions and the latter serving as expert facts. Both simulation and real-robot experiments show our effectiveness in handling a large variety of articulated objects with diverse language-instructed goals