The vSwitch, as a critical component for Virtual Machine (VM) network connectivity in cloud environments, has prompted increasing attention towards its forwarding performance. While software optimization schemes have limitations in meeting the expanding network capacity demands [11, 12, 15, 17, 18], hardware offloading architectures leveraging SoC, FPGA, and ASIC have been proposed to transfer the match-action workload [1, 3, 6, 7, 13, 16], addressing the growing need for network capacity.

In this paper, we aim to recognize materials with combined use of auditory and visual perception. To this end, we construct a new dataset named GLAudio that consists of both the geometry of the object being struck and the sound captured from either modal sound synthesis (for virtual objects) or real measurements (for real objects). Besides global geometries, our dataset also takes local geometries around different hitpoints into consideration. This local information is less explored in existing datasets. We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition. To extract features from different modalities and perform proper fusion, we propose a new deep neural network GLAVNet that comprises multiple branches and a well-designed fusion module. Once trained on GLAudio, our GLAVNet provides state-of-the-art performance on material identification and supports fine-grained material categorization.

Spreadsheets are widely used, but subject to various defects and severe consequences due to poor maintenance by end users. Existing spreadsheet defect detection techniques fall short of effectiveness, either due to limited scopes or relying on rigid patterns. In this paper, we discuss and improve one state-of-the-art technique, CUSTODES, which uses cell clustering and anomaly detection to extend its scope and make its patterns adaptive to varying spreadsheet styles, but is prone to fragile clustering when involving irrelevant cells, leading to a largely reduced detection precision. We present WARDER to refine CUSTODES's cell clustering based on validity properties, and experimental results show that WARDER improves the precision by 20.7% on average or reach 100% for 79.8% worksheets on cell clustering, which contributes to a precision improvement of 23.1% for defect detection. WARDER also exhibits satisfactory results, against other spreadsheet defect detection techniques, and on another large-scale spreadsheet corpus VEnron2.