A Proof-of-Quality-Factor (PoQF) based blockchain and edge computing for vehicular message dissemination

Blockchain applications in vehicular networks can offer many advantages including decentralization and improved security. However, most of consensus algorithms in blockchain are difficult to be implemented in a Vehicular Ad-Hoc Networks (VANET) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time consuming consensus algorithms, e.g., Proof-of-Work, running on mobile edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g. Proof-of-Stake (PoS) is biased towards nodes with higher amount of stakes and Proof-of-Elapsed-Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on Proof-of-Quality-Factor (PoQF) consensus algorithm, where threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on probabilistic prediction of channel quality between transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. The paper also analyzes the throughput of block generation, as well as the asymptotic latency, security and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against collusion of nodes is proved using game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET

Large Language Models are Diverse Role-Players for Summarization Evaluation

Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluation is that there is a clear divergence between existing metrics and human evaluation. For example, the quality of a document summary can be measured by human annotators from both objective aspects, such as grammatical and semantic correctness, as well as subjective dimensions, such as comprehensiveness, succinctness, and interestingness. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to capture the above dimensions well. In this paper, we propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects. First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism. Furthermore, we introduce a context-based prompting mechanism that is able to generate dynamic roleplayer profiles based on input context. Finally, we design a multi-roleplayer prompting technology based on batch prompting to integrate multiple evaluation results into evaluation results. Experimental results on two real datasets for summarization show that our model is highly competitive and has a very high consistency with human annotators

Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks

We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM, three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.Comment: Accepted to EMNLP2019; 10 pages, 2 figure

A blockchain based federated learning for message dissemination in vehicular networks

Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proofof-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbour vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analysed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks

Inference with Reference: Lossless Acceleration of Large Language Models

We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real world scenarios (e.g., retrieved documents). LLMA first selects a text span from the reference and copies its tokens to the decoder and then efficiently checks the tokens' appropriateness as the decoding result in parallel within one decoding step. The improved computational parallelism allows LLMA to achieve over 2x speed-up for LLMs with identical generation results as greedy decoding in many practical generation scenarios where significant overlap between in-context reference and outputs exists (e.g., search engines and multi-turn conversations).Comment: 9 page

Inhibition of Thrombin Receptor Signaling on alpha-Smooth Muscle Actin(+) CD34(+) Progenitors Leads to Repair After Murine Immune Vascular Injury

OBJECTIVE: The goal of this study was to use mice expressing human tissue factor pathway inhibitor (TFPI) on α-smooth muscle actin (α-SMA)(+) cells as recipients of allogeneic aortas to gain insights into the cellular mechanisms of intimal hyperplasia (IH). METHODS AND RESULTS: BALB/c aortas (H-2(d)) transplanted into α-TFPI-transgenic (Tg) mice (H-2(b)) regenerated a quiescent endothelium in contrast to progressive IH seen in C57BL/6 wild-type (WT) mice even though both developed aggressive anti-H-2(d) alloresponses, indicating similar vascular injuries. Adoptively transferred Tg CD34(+) (but not CD34(-)) cells inhibited IH in WT recipients, indicating the phenotype of α-TFPI-Tg mice was due to these cells. Compared with syngeneic controls, endogenous CD34(+) cells were mobilized in significant numbers after allogeneic transplantation, the majority showing sustained expression of tissue factor and protease-activated receptor-1 (PAR-1). In WT, most were CD45(+) myeloid progenitors coexpressing CD31, vascular endothelial growth factor receptor-2 and E-selectin; 10% of these cells coexpressed α-SMA and were recruited to the neointima. In contrast, the α-SMA(+) human TFPI(+) CD34(+) cells recruited in Tg recipients were from a CD45(-) lineage. WT CD34(+) cells incubated with a PAR-1 antagonist or taken from PAR-1-deficient mice inhibited IH as Tg cells did. CONCLUSIONS: Specific inhibition of thrombin generation or PAR-1 signaling on α-SMA(+) CD34(+) cells inhibits IH and promotes regenerative repair despite ongoing immune-mediated damage