Autism is a brain disease that harmfully impacts a person’s capacity for interpersonal interaction and communication. Autism is also known as autistic spectrum disorder (ASD) because of the vast range of symptoms it ...
详细信息
In this paper,we study a class of online continuous optimization *** each round,the utility function is the sum of a weakly diminishing-returns(DR)submodular function and a concave function,certain cost associated wit...
详细信息
In this paper,we study a class of online continuous optimization *** each round,the utility function is the sum of a weakly diminishing-returns(DR)submodular function and a concave function,certain cost associated with the action will occur,and the problem has total limited *** the two methods,the penalty function and Frank-Wolfe strategies,we present an online method to solve the considered *** appropriate stepsize and penalty parameters,the performance of the online algorithm is guaranteed,that is,it achieves sub-linear regret bound and certain mild constraint violation bound in expectation.
With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged...
详细信息
With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this *** methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and *** with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.
The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific d...
详细信息
The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various di...
详细信息
Since the early 1990s, open access (OA) scholarly publishing has become widespread in the international scientific community. Although there are numerous benefits of OA publishing, researchers experience a number of d...
详细信息
In recent years,the rapid development of computer software has led to numerous security problems,particularly software *** flaws can cause significant harm to users’privacy and *** security defect detection technolog...
详细信息
In recent years,the rapid development of computer software has led to numerous security problems,particularly software *** flaws can cause significant harm to users’privacy and *** security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection *** intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false ***,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)***,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency *** feature vector is then used as the learning target for the neural *** types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection *** results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.
Phase shifting transformers (PSTs) are a cost-efficient solution for controlling power flow without additional operating expenses. They can enhance power distribution in transmission systems and increase system capaci...
详细信息
Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both enti...
详细信息
Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.
In the era of digital communication, ensuring user privacy on social media platforms is essential. A major challenge is aligning platform privacy settings with individual user preferences without overwhelming them wit...
详细信息
暂无评论