Remove-Win: a Design Framework for Conflict-free Replicated Data Collections

Internet-scale distributed systems often replicate data within and across data centers to provide low latency and high availability despite node and network failures. Replicas are required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the issue of conflict resolution among concurrent updates, which is often challenging and error-prone. The Conflict-free Replicated Data Type (CRDT) framework provides a principled approach to address this challenge. This work focuses on a special type of CRDT, namely the Conflict-free Replicated Data Collection (CRDC), e.g. list and queue. The CRDC can have complex and compound data items, which are organized in structures of rich semantics. Complex CRDCs can greatly ease the development of upper-layer applications, but also makes the conflict resolution notoriously difficult. This explains why existing CRDC designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDCs. To address the challenges above, we propose the Remove-Win Design Framework. The remove-win strategy for conflict resolution is simple but powerful. The remove operation just wipes out the data item, no matter how complex the value is. The user of the CRDC only needs to specify conflict resolution for non-remove operations. This resolution is destructed to three basic cases and are left as open terms in the CRDC design skeleton. Stubs containing user-specified conflict resolution logics are plugged into the skeleton to obtain concrete CRDC designs. We demonstrate the effectiveness of our design framework via a case study of designing a conflict-free replicated priority queue. Performance measurements also show the efficiency of the design derived from our design framework.Comment: revised after submissio

Exploring the Privacy Protection Capabilities of Chinese Large Language Models

Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence. Yet, these advancements have raised growing concerns about privacy and security implications. To address these issues and explain the risks inherent in these models, we have devised a three-tiered progressive framework tailored for evaluating privacy in language systems. This framework consists of progressively complex and in-depth privacy test tasks at each tier. Our primary objective is to comprehensively evaluate the sensitivity of large language models to private information, examining how effectively they discern, manage, and safeguard sensitive data in diverse scenarios. This systematic evaluation helps us understand the degree to which these models comply with privacy protection guidelines and the effectiveness of their inherent safeguards against privacy breaches. Our observations indicate that existing Chinese large language models universally show privacy protection shortcomings. It seems that at the moment this widespread issue is unavoidable and may pose corresponding privacy risks in applications based on these models.Comment: 11 page

Research on the Development of Voice Assistants in the Era of Artificial Intelligence

Voice assistants have gradually occupied an important position in the products of many electronics companies. Artificial Intelligence voice assistants are able to interpret human speech and respond. Users can ask their assistant questions and manage other essential tasks such as email calendars through verbal commands. This paper analyzes the artificial intelligence voice assistant through the method of comparative analysis. The author studies the development situation of intelligent voice assistants, and compares the differences between Chinese and foreign voice assistants, and finally discusses the relationship between voice intelligent assistants and people’s lives. The author found that users in different countries have different functional preferences for using voice assistants, but they can help people’s work and life to a great extent. In other words, voice assistants play an important role in contemporary society. Therefore, people need to better understand the relationship between humans and machin

Crosslinked fluoropolymers exhibiting superior high-temperature energy density and charge-discharge efficiency

Superior high-temperature discharged energy densities in comparison to those of the current dielectric polymers have been demonstrated in the crosslinked fluoropolymers

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http://www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/ Contact: [email protected]