With the popularity and development of network technology and the Internet, intrusion detection systems (IDSs), which can identify attacks, have been developed. Traditional intrusion detection algorithms typically employ mining association rules to identify intrusion behaviors. However, they fail to fully extract the characteristic information of user behaviors and encounter various problems, such as high false alarm rate (FAR), poor generalization capability, and poor timeliness. In this paper, we propose a network intrusion detection model based on a convolutional neural network-IDS (CNN-IDS). Redundant and irrelevant features in the network traffic data are first removed using different dimensionality reduction methods. Features of the dimensionality reduction data are automatically extracted using the CNN, and more effective information for identifying intrusion is extracted by supervised learning. To reduce the computational cost, we convert the original traffic vector format into an image format and use a standard KDD-CUP99 dataset to evaluate the performance of the proposed CNN model. The experimental results indicate that the AC, FAR, and timeliness of the CNN-IDS model are higher than those of traditional algorithms. Therefore, the model we propose has not only research significance but also practical value.

Employing large-scale pre-trained model CLIP to conduct video-text retrieval task (VTR) has become a new trend, which exceeds previous VTR methods. Though, due to the heterogeneity of structures and contents between video and text, previous CLIP-based models are prone to overfitting in the training phase, resulting in relatively poor retrieval performance. In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. In this stage, we conduct massive explorations towards the feature extraction module and feature alignment module, and conclude an efficient VTR framework. DSL is proposed to avoid the oneway optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match. DSL is easy to implement with only one-line code but improves significantly. The results show that the proposed CAMoE and DSL are of strong efficiency, and each of them is capable of achieving State-of-The-Art (SOTA) individually on various benchmarks such as MSR-VTT, MSVD, and LSMDC. Further, with both of them, the performance is advanced to a great extent, surpassing the previous SOTA methods for around 4.6% R@1 in MSR-VTT. The code will be available soon at https://github.com/starmemda/CAMoE/

Middle to Late Pleistocene human evolution in East Asia has remained controversial regarding the extent of morphological continuity through archaic humans and to modern humans. Newly found ∼300,000-y-old human remains from Hualongdong (HLD), China, including a largely complete skull (HLD 6), share East Asian Middle Pleistocene (MPl) human traits of a low vault with a frontal keel (but no parietal sagittal keel or angular torus), a low and wide nasal aperture, a pronounced supraorbital torus (especially medially), a nonlevel nasal floor, and small or absent third molars. It lacks a malar incisure but has a large superior medial pterygoid tubercle. HLD 6 also exhibits a relatively flat superior face, a more vertical mandibular symphysis, a pronounced mental trigone, and simple occlusal morphology, foreshadowing modern human morphology. The HLD human fossils thus variably resemble other later MPl East Asian remains, but add to the overall variation in the sample. Their configurations, with those of other Middle and early Late Pleistocene East Asian remains, support archaic human regional continuity and provide a background to the subsequent archaic-to-modern human transition in the region.