Research on application of patch near-field acoustic holography using acoustic radiation modes with double layers

To solve the problems of near-field acoustic holography in actual applications, research based on acoustic radiation modes with double layers is studied in this paper. The sound pressure model by using acoustic radiation modes is selected to express the sound field firstly. Then, the sound field separation with double pressure measurement layers has been applied to remove the interference from the opposite direction. Thirdly, the method based on data interpolation and extrapolation is put forward to increase the holographic value equivalently, the results of which are closer to the actual measured value obtained by more measuring points. Numerical simulations based on the theoretical description are conducted to demonstrate the validity of the proposed method, and experiments for a boundary constraint steel plate and a loudspeaker are performed to demonstrate its feasibility

Not only Online Review but also its Helpfulness is Manipulated: Evidence from Peer to Peer Lending Forum

Online reviews have become proposed as useful information for consumers to make decision. Meanwhile, review manipulation will weaken the credibility of online reviews. Except manipulating the review text and rating, we propose that review helpfulness, an important signal for consumer to filter the reviews, could also be manipulated. This study thus explores the existence of review helpfulness manipulation and the relationship between firm quality and review manipulation. Based on a dataset from a review forum in www.wdzj.com which is the leading and largest portal of peer to peer lending industry in China, we get the following interesting results. First, due to the manipulation of review helpfulness, a manipulated positive review is more likely to receive higher helpfulness, while a manipulated negative is more likely to get lower helpfulness. Second, a manipulated review tends to be lower quality in terms of readability and word count, which are found as positive predictors for review helpfulness. Third, high quality firms tend to manipulate more positive reviews, and at the same time high quality firms will receive more negative manipulated reviews. This study extends current understanding about online review manipulation, thereby providing theoretical and practice implications

Transforming Wikipedia into Augmented Data for Query-Focused Summarization

The manual construction of a query-focused summarization corpus is costly and timeconsuming. The limited size of existing datasets renders training data-driven summarization models challenging. In this paper, we use Wikipedia to automatically collect a large query-focused summarization dataset (named as WIKIREF) of more than 280,000 examples, which can serve as a means of data augmentation. Moreover, we develop a query-focused summarization model based on BERT to extract summaries from the documents. Experimental results on three DUC benchmarks show that the model pre-trained on WIKIREF has already achieved reasonable performance. After fine-tuning on the specific datasets, the model with data augmentation outperforms the state of the art on the benchmarks

Sound field separation technique using the principle of double layer patch acoustic radiation modes

In order to solve the problems of near-field acoustic holography in applications such as external interference and aperture effects, a sound field separation technique using the principle of double layer patch acoustic radiation modes is proposed in this paper. The radiated acoustic pressures over two planar surfaces at certain distances from the sources are calculated first. Then, the effects resulting from the backscattering interference in non-free sound fields can be eliminated by a double-layer sound field separation technique. Next, data interpolation and extrapolation are performed on the separated data to increase the sound source's pressures on the holographic plane equivalently for holographic images with higher spatial resolution. Simulation and experimental results demonstrate that good agreements can be obtained with few measuring points

Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication

This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of high communication and computing resource requirements. Our proposed methodology incorporates a parallel optimization algorithm with improved consensus ADMM considering a more realistic locally connected topology network, and time complexity of O(N) is achieved by exploiting the sparsity in the dual update process. To further enhance the computational efficiency, we employ a lightweight evolution strategy for the dynamic connectivity graph of CAVs, and each sub-problem split from the consensus ADMM only requires managing a small group of CAVs. The proposed method implemented with the receding horizon scheme is validated thoroughly, and comparisons with existing numerical solvers and approaches demonstrate the efficiency of our proposed algorithm. Also, simulations on large-scale cooperative driving tasks involving 80 vehicles are performed in the high-fidelity CARLA simulator, which highlights the remarkable computational efficiency, scalability, and effectiveness of our proposed development. Demonstration videos are available at https://henryhcliu.github.io/icadmm_cmp_carla.Comment: 15 pages, 10 figure

Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision

Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short sequences and require ground-truth annotations of each video frame/sequence, which is quite costly and time-consuming. In this paper, given only video-level annotations, we propose a novel weakly supervised framework to simultaneously locate action frames as well as recognize actions in untrimmed videos. Our proposed framework consists of two major components. First, for action frame localization, we take advantage of the self-attention mechanism to weight each frame, such that the influence of background frames can be effectively eliminated. Second, considering that there are trimmed videos publicly available and also they contain useful information to leverage, we present an additional module to transfer the knowledge from trimmed videos for improving the classification performance in untrimmed ones. Extensive experiments are conducted on two benchmark datasets (i.e., THUMOS14 and ActivityNet1.3), and experimental results clearly corroborate the efficacy of our method