Benefiting from the booming of Big Data and artificial intelligence (AI) technologies, data-as-a-service is gradually transforming into knowledge-as-a-service. Extracting knowledge from massive raw data is becoming a popular paradigm to save network resources and improve efficiency, and establishing knowledge markets is receiving increasing attention from academia and industry. In this paper, we propose a one-stop knowledge acquisition ecosystem termed BWKA that covers the whole process from upper-layer knowledge trading to underlying knowledge generation. In the knowledge trading process, the knowledge-as-a-service platform (KSP) is the buyer and publishes knowledge demands to multiple local knowledge sellers (LKSs). In the knowledge generation process, each LKS aggregates data from its sensors and then trains data into knowledge according to the KSP's requirements. We resort to blockchain technology and provide a series of tailored operating rules and functions to protect the truthfulness of data gathering and the fairness of knowledge trading. In addition, we introduce incentive mechanisms to stimulate selfish and rational entities in the BWKA ecosystem to participate in knowledge acquisition. To analyze the strategic interactions among entities theoretically, we develop a nested hierarchical game model, where the upper-layer knowledge trading is evaluated based on the Contract Theory, and the lower-layer knowledge generation is formulated as a two-stage Stackelberg game. By solving the nested hierarchical game in a backward inductive way, we identify the optimal strategy for each entity in closed form. Experiments on the Ethereum blockchain and simulation results demonstrate the practical operability and outstanding performance of the BWKA ecosystem.

Division of focal plane color polarization camera becomes the mainstream in polarimetric imaging for it directly captures color polarization mosaic image by one snapshot, so image demosaicking is an essential task. Current color polarization demosaicking (CPDM) methods are prone to unsatisfied results since it's difficult to recover missed 15 or 14 pixels out of 16 pixels in color polarization mosaic images. To address this problem, a non-locally regularized convolutional sparse regularization model, which is advantaged in denoising and edge maintaining, is proposed to recall more information for CPDM task, and the CPDM task is transformed into an energy function to be solved by ADMM optimization. Finally, the optimal model generates informative and clear results. The experimental results, including reconstructed synthetic and real-world scenes, demonstrate that our proposed method outperforms the current state-of-the-art methods in terms of quantitative measurements and visual quality. The source code is available at https://github.com/roydon-luo/NLCSR-CPDM.

Mobile crowdsensing (MCS) has emerged as a popular and promising paradigm for solving challenging problems by utilizing collective wisdom and resources. However, the system architecture and operational rules for MCS have not been well-defined, and obtaining accurate and reliable results from conflicting data collected by workers is difficult due to discrepancies in sensor quality and privacy protection requirements. In this paper, we combine the methodologies of Dynamic Truth Discovery (DTD), Combinatorial Multi-Armed Bandit (CMAB), and Multi-Attribute Reverse Auction to develop a novel MCS ecosystem, with the objective of maximizing the sensing accuracy-aware utility under the budget constraint. We first establish the data collection model by jointly considering the task completion duration as well as the deviation caused by both endogenous errors and privacy protection-oriented injected noise. Then, we theoretically evaluate the accuracy of truth discovery and quantify the contribution of each worker to MCS to form the worker selection criterion. As the qualities of workers are initially unknown, the platform faces the exploration-exploitation dilemma. Therefore, we apply CMAB to transform the worker recruitment problem into a combinatorial arm-pulling problem and elaborately design an Upper Confidence Bound (UCB) algorithm to achieve a desirable exploration-exploitation tradeoff. Moreover, we design an auction-based payment method for the platform, stimulating workers to provide their quoted price honestly while enabling individual rationality. Extensive simulations and comparison results demonstrate the feasibility and effectiveness of our proposed MCS ecosystem.

Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches face two challenges in such applications: 1) convolutional or recurrent neural network based models have difficulty learning long-term dependencies; and, 2) there are not enough labeled seizure sub-type data for training such models. This paper proposes a Transformer-based self-supervised learning model for EEG-based seizure subtype classification, which copes well with these two challenges. Filter bank analysis is first employed to improve Vision Transformer as a Wavelet Transformer (WaT) encoder, which generates multi-grained feature representations of EEG signals. Then, self-supervised learning is used to pre-train WaT from unlabeled EEG data. Experiments on two public datasets demonstrated that Wavelet2Vec outperformed several other supervised and self-supervised models in cross-subject seizure subtype classification.

Blockchain technology is a promising approach for solving the security and personal privacy problems in Internet applications. The successful commercial deployment of Blockchain markets relies on a comprehensive understanding of the economic and strategic interactions among different entities involved. In this paper, we focus on a blockchain market consisting of a blockchain platform (BP), multiple miners, and blockchain users (BUs), and formulate their interactions as a three-stage Stackelberg game. In Stage I, the BP strategizes the rewards granted to the miners, so as to attract the miners to contribute more computing power used for improving the security and privacy of the blockchain. In Stage II, each miner strategizes its computing power individually for winning the mining compe-tition, which is modeled as a non-cooperative game. In Stage III, the BUs strategize the transaction fee to acquire a corresponding service experience. With the objective of utility maximization, we develop a theoretical framework to analyze the hierarchical interactive behaviors among the entities in a backward inductive way. By solving the Stackelberg equilibrium, we determine the optimal strategies of entities in closed-form. Numerical results are provided to demonstrate the performance of the strategic interactions in the blockchain market.