Improving spam filtering in enterprise email systems with blockchain-based token incentive mechanism

Spam has caused serious problems for email systems. To address this issue, numerous spam filter algorithms have been developed, all of which require extensive training on labeled spam datasets to obtain the desired filter performance. However, users\u27 privacy concerns and apathy make it difficult to acquire personalized spam data in real-world applications. When it comes to enterprise email systems, the problem worsens because enterprises are extremely sensitive to the possible disclosure of confidential information during the reporting of spam to the cloud. Targeting these obstacles, this study proposes a blockchain-based token incentive mechanism, with the aim of encouraging users to report spam while protecting business secrets and ensuring the transparency of reward rules. The proposed mechanism also enables a decentralized ecosystem for token circulation, fully utilizing the advantages of blockchain technologies. We developed a prototype of the proposed system, on which we conducted a user experiment to verify our design. Results indicate that the proposed incentive mechanism is effective and can raise the probability of spam reporting by more than 1.4 times

ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt Learning

Prompt learning recently become an effective linguistic tool to motivate the PLMs' knowledge on few-shot-setting tasks. However, studies have shown the lack of robustness still exists in prompt learning, since suitable initialization of continuous prompt and expert-first manual prompt are essential in fine-tuning process. What is more, human also utilize their comparative ability to motivate their existing knowledge for distinguishing different examples. Motivated by this, we explore how to use contrastive samples to strengthen prompt learning. In detail, we first propose our model ConsPrompt combining with prompt encoding network, contrastive sampling module, and contrastive scoring module. Subsequently, two sampling strategies, similarity-based and label-based strategies, are introduced to realize differential contrastive learning. The effectiveness of proposed ConsPrompt is demonstrated in five different few-shot learning tasks and shown the similarity-based sampling strategy is more effective than label-based in combining contrastive learning. Our results also exhibits the state-of-the-art performance and robustness in different few-shot settings, which proves that the ConsPrompt could be assumed as a better knowledge probe to motivate PLMs

The isolation and characterization of twelve novel microsatellite loci from Haliotis ovina

Twelve (12) microsatellite loci were developed from Haliotis ovina by magnetic bead hybridization method. Genetic variability was assessed using 30 individuals from three wild populations. The number of alleles per locus was from 2 to 5 and polymorphism information content was from 0.1228 to 0.6542. The observed and expected heterozygosities ranged from 0.0000 to 0.7778 and 0.1288 to 0.6310, respectively. These loci should provide useful information for genetic studies such as genetic diversity, pedigree analysis, construction of genetic linkage maps and marker-assisted selection breeding in H. ovina.Key words: Genetic markers, Haliotis ovina, microsatellites