Abstract: An electrode structure of rechargeable battery includes a battery tab stack, an electrode lead, a welding protective layer and a welding seam. The battery tab stack is formed by extension of a plurality of electrode sheets. The electrode lead is joined to one side of the battery tab stack. The welding protective layer is joined to another side of the battery tab stack opposite to the electrode lead. The welding seam extends from the welding protective layer to the electrode lead through the battery tab stack.

Abstract: A display device includes a base layer, a touch sensing layer, a light guide module and a display panel. The touch sensing layer is disposed on the base layer. The light guide module is disposed on the touch sensing layer. The touch sensing layer is located between the light guide module and the display panel, and the touch sensing layer and one of the light guide module and the display panel have no adhesive material therebetween.

Abstract: A machine learning system and method are provided. The machine learning system includes a plurality of client apparatuses, and the client apparatuses include a first client apparatus and one or more second client apparatuses. The first client apparatus transmits a model update request to the one or more second client apparatuses, and the model update request corresponds to a malware type. The first client apparatus receives a second local model corresponding to each of the one or more second client apparatuses from each of the one or more second client apparatuses. The first client apparatus generates a plurality of node sequences based on a first local model and each of the second local models. The first client apparatus merges the first local model and each of the second local models based on the node sequences to generate a local model set.

Abstract: An abnormal flow detection device and an abnormal flow detection method thereof are provided. The abnormal flow detection device analyses a plurality of packets captured during a time interval to obtain a plurality of flow features of each packet and selects at least one key flow feature from the flow features based on a dimensionality reduction algorithm. The abnormal flow detection device trains a bidirectional generative adversarial network (BiGAN) by taking the at least one key flow feature of each packet as an input of the BiGAN to build a flow recognition model for detecting abnormal flows.

Abstract: An abnormal flow detection device and an abnormal flow detection method thereof are provided. The abnormal flow detection device analyses a plurality of packets captured during a time interval to obtain a plurality of flow features of each packet and selects at least one key flow feature from the flow features based on a dimensionality reduction algorithm. The abnormal flow detection device trains a bidirectional generative adversarial network (BiGAN) by taking the at least one key flow feature of each packet as an input of the BiGAN to build a flow recognition model for detecting abnormal flows.

Abstract: Upon detection of a suspicious file, a client computer sends feedback data to an anti-malware service over the Internet. Files that are not suspicious or that are known clean are not reported; files that are known malware are acted upon immediately without needing to report them to the anti-malware service. Upon detection, no alert or warning is provided to the user of the client computer. The anti-malware service correlates data from other detection engines on the client computer or from other client computers and determines whether the file is malware or not. A new virus pattern is generated if the file is malware and includes the virus signature of the file; the new virus pattern is distributed back to the client computers. If not malware, no action need be taken, or, the virus signature of the file is removed from existing pattern files.