High-Level Modeling and Simulation of a Novel Reconfigurable Network-on-Chip Router

In this paper, we present a novel router architecture for implementing a Reconfigurable Network-on-Chip (RNoC) at high-level design using SystemC. The RNoC is an adaptive NoC-based system-on-chip providing a dynamic reconfigurable communication mechanism. By adding a virtual port – named Routing Modification port – into the conventional router architecture, the network router is able to route communication data flexibly whenever the target routing path is blocked, by unwanted defects or intently by a software programme to meet the requirements of applications. The proposed architecture has been modeled in SystemC/C++, simulated and verified within a 2D mesh 5×5 network platform. In normal communication mode, the static XY routing algorithm is used while the West-First algorithm with a proposed prohibited router surrounding technique is applied in reconfiguration mode. Experimental results are also reported to compare the performance of the network architecture in different operation modes as well as with other works

LG-Hand: Advancing 3D Hand Pose Estimation with Locally and Globally Kinematic Knowledge

3D hand pose estimation from RGB images suffers from the difficulty of obtaining the depth information. Therefore, a great deal of attention has been spent on estimating 3D hand pose from 2D hand joints. In this paper, we leverage the advantage of spatial-temporal Graph Convolutional Neural Networks and propose LG-Hand, a powerful method for 3D hand pose estimation. Our method incorporates both spatial and temporal dependencies into a single process. We argue that kinematic information plays an important role, contributing to the performance of 3D hand pose estimation. We thereby introduce two new objective functions, Angle and Direction loss, to take the hand structure into account. While Angle loss covers locally kinematic information, Direction loss handles globally kinematic one. Our LG-Hand achieves promising results on the First-Person Hand Action Benchmark (FPHAB) dataset. We also perform an ablation study to show the efficacy of the two proposed objective functions

Application of HEC-HMS model and satellite precipitation products to restore runoff in Laigiang river basin in Vietnam

The Laigiang river basin in the South Central Coast of Vietnam plays an important role in the socio-economic development of Binhdinh Province. In recent decades, the region has experienced commonly flooding in vast areas. This research aims to simulate event-based rainfall-runoff modelling, a historical flood event in December 2016, by applying the HEC-HMS model and rainfall data from CHIRPS. The CHIRPS data is an acceptable potential data input of the hydrology model. These have been confirmed through reliable validation indexes: The peak flood flow rate of 2,542.6 m3/s corresponds to the flood frequency of 5%; NSE with the value at 0.95; R2 coefficient reached 0.87; PBIAS being around 0.45, and PFC being at 0.89. It shows better performance in the rainy season than in the dry season. Inclusive, the CHIRPS rainfall data set and the HEC model could be used for some operational purposes in weather forecasting, especially for flood warnings in river basins in the South Central Coast, Vietnam

Vietnamese Word Segmentation with CRFs and SVMs: An Investigation

PACLIC 20 / Wuhan, China / 1-3 November, 200

A Survey on Reconfigurable System-on-Chips

The requirements for high performance and low power consumption are becoming more and more inevitable when designing modern embedded systems, especially for the next generation multi-mode multimedia or communication standards. Ultra large-scale integration reconfigurable System-on-Chips (SoCs) have been proposed to achieve not only better performance and lower energy consumption but also higher flexibility and versatility in comparison with the conventional architectures. The unique characteristic of such systems is integration of many types of heterogeneous reconfigurable processing fabrics based on a Network-on-Chip. This paper analyzes and emphasizes the key research trends of the reconfigurable System-on-Chips (SoCs). Firstly, the emerging hardware architecture of SoCs is highlighted. Afterwards, the key issues of designing the reconfigurable SoCs are discussed, with the focus on the challenges when designing reconfigurable hardware fabrics and reconfigurable Network-on-Chips. Finally, some state-of-the-art reconfigurable SoCs are briefly discussed

Attentive Deep Neural Networks for Legal Document Retrieval

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task.Comment: Preprint version. The official version will be published in Artificial Intelligence and Law journa

Using Fine-Grained Sediment and Wave Attenuation as a New Measure for Evaluating the Efficacy of Offshore Breakwaters in Stabilizing an Eroded Muddy Coast: Insights from Ca Mau, the Mekong Delta of Vietnam

Offshore breakwaters can be effective in reducing the energy of incident waves through dissipation, refraction or reflection. Breakwaters are increasingly constructed to stabilize eroded muddy coasts, particularly in developing countries. Accumulation of fine-grained sediment and wave attenuation are two attributes of a stable muddy coast. Effective interventions in stabilizing eroded muddy coasts include two important elements: accumulation of fine-grained sediment and wave reduction. The efficacy of offshore breakwaters in stabilizing eroded muddy coasts is, however, not yet adequately understood. A crucial question needing attention is whether accumulation of fine-grained sediment and wave attenuation should be used in evaluating the efficacy of these offshore breakwaters in stabilizing eroded muddy coasts. To address this issue, a pile-rock offshore breakwater in Huong Mai, Tieu Dua of Ca Mau, Vietnam was selected as an appropriate example in this regard. Accumulation of fine-grained sediment and wave attenuation were tested as means to investigate the efficacy of the Huong Mai structure in stabilizing the eroded muddy coast. The study was undertaken using field-based measurements and semi-structured interviews in three stages between October 2016 and December 2020. We found that this structure has had limited efficacy in stabilizing the eroded muddy coast. The structure was effective in dissipating the energy of incident waves, but we found no evidence of fine-grained sediment accumulation due to an inappropriate structural design. There was also no monitoring system in place, leading to difficulties in evaluating its efficacy in terms of wave attenuation and accumulation of fine-grained sediment. The gaps between the shoreline and the structure have not been adequately explained, resulting in substantial challenges in replicating the structure elsewhere. The Huong Mai structure should be strengthened using supplementary measures and granulometric tests in order to improve the efficacy in stabilizing eroded muddy coasts. The methods in this study provide new insights in this regard