Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITSComment: 6 pages, 6 figures. To be published in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC

Selection of Anti-Sulfadimidine Specific ScFvs from a Hybridoma Cell by Eukaryotic Ribosome Display

BACKGROUND:Ribosome display technology has provided an alternative platform technology for the development of novel low-cost antibody based on evaluating antibiotics derived residues in food matrixes. METHODOLOGY/PRINCIPAL FINDINGS:In our current studies, the single chain variable fragments (scFvs) were selected from hybridoma cell lines against sulfadimidine (SM(2)) by using a ribosome library technology. A DNA library of scFv antibody fragments was constructed for ribosome display, and then mRNA-ribosome-antibody (MRA) complexes were produced by a rabbit reticulocyte lysate system. The synthetic sulfadimidine-ovalbumin (SM(2)-OVA) was used as an antigen to pan MRA complexes and putative scFv-encoding genes were recovered by RT-PCR in situ following each panning. After four rounds of ribosome display, the expression vector pCANTAB5E containing the selected specific scFv DNA was constructed and transformed into Escherichia coli HB2151. Three positive clones (SAS14, SAS68 and SAS71) were screened from 100 clones and had higher antibody activity and specificity to SM(2) by indirect ELISA. The three specific soluble scFvs were identified to be the same molecular weight (approximately 30 kDa) by Western-blotting analysis using anti-E tag antibodies, but they had different amino acids sequence by sequence analysis. CONCLUSIONS/SIGNIFICANCE:The selection of anti-SM(2) specific scFv by in vitro ribosome display technology will have an important significance for the development of novel immunodetection strategies for residual veterinary drugs

Reversible Data Hiding Algorithm in Fully Homomorphic Encrypted Domain

This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a DGHV fully homomorphic encrypted image without any preprocessing. On the sending side, by using two encrypted pixels as a group, a data hider can get the difference of two pixels in a group. Additional data can be embedded into the encrypted image by shifting the histogram of the differences with the fully homomorphic property. On the receiver side, a legal user can extract the additional data by getting the difference histogram, and the original image can be restored by using modular arithmetic. Besides, the additional data can be extracted after decryption while the original image can be restored. Compared with the previous two typical algorithms, the proposed scheme can effectively avoid preprocessing operations before encryption and can successfully embed and extract additional data in the encrypted domain. The extensive testing results on the standard images have certified the effectiveness of the proposed scheme

The detection of distributional discrepancy for language GANs

A pre-trained neural language model (LM) is usually used to generate texts. Due to exposure bias, the generated text is not as good as real text. Many researchers claimed they employed the Generative Adversarial Nets (GAN) to alleviate this issue by feeding reward signals from a discriminator to update the LM (generator). However, some researchers argued that GAN did not work by evaluating the generated texts with a quality-diversity metric such as Bleu versus self-Bleu, and language model score versus reverse language model score. Unfortunately, these two-dimension metrics are not reliable. Furthermore, the existing methods only assessed the final generated texts, thus neglecting the dynamic evaluating the adversarial learning process. Different from the above-mentioned methods, we adopted the most recent metric functions, which measure the distributional discrepancy between real and generated text. Besides that, we design a comprehensive experiment to investigate the performance during the learning process. First, we evaluate a language model with two functions and identify a large discrepancy. Then, several methods with the detected discrepancy signal to improve the generator were tried. Experimenting with two language GANs on two benchmark datasets, we found that the distributional discrepancy increases with more adversarial learning rounds. Our research provides convicted evidence that the language GANs fail