<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ ...<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand. </div>展开更多
随着我国"一带一路"战略的推进,新疆应当充分发挥其区域优势,着力于打造丝绸之路经济带核心区,其中提高汉语-维吾尔语(汉-维)之间机器翻译的质量有着重要的现实意义。通过对低频维吾尔语词汇进行词干词缀分割进行了基于三种...随着我国"一带一路"战略的推进,新疆应当充分发挥其区域优势,着力于打造丝绸之路经济带核心区,其中提高汉语-维吾尔语(汉-维)之间机器翻译的质量有着重要的现实意义。通过对低频维吾尔语词汇进行词干词缀分割进行了基于三种不同机器翻译系统的汉-维机器翻译对比实验。该方法不仅减少词汇表大小从而减轻未登录词(Out Of Vocabulary,OOV)问题,同时也大大提升了翻译结果。其中统计机器翻译结果提升最明显,比原始提升了3.29个BLEU值。展开更多
With the advancement of network communication technology,network traffic shows explosive growth.Consequently,network attacks occur frequently.Network intrusion detection systems are still the primary means of detectin...With the advancement of network communication technology,network traffic shows explosive growth.Consequently,network attacks occur frequently.Network intrusion detection systems are still the primary means of detecting attacks.However,two challenges continue to stymie the development of a viable network intrusion detection system:imbalanced training data and new undiscovered attacks.Therefore,this study proposes a unique deep learning-based intrusion detection method.We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data.Then the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to train.Finally,the distance relationship between the triples determines whether the traffic is an attack.In addition,to improve the accuracy of detecting unknown attacks,this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature space.The proposed approach’s effectiveness,stability,and significance are evaluated against advanced models on the Android Adware and General Malware Dataset(AAGM17),Knowledge Discovery and Data Mining Cup 1999(KDDCUP99),Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation Dataset(CICIDS2017),UNSW-NB15,Network Security Lab-Knowledge Discovery and Data Mining(NSL-KDD)datasets.The achieved results confirmed the superiority of the proposed method for the task of network intrusion detection.展开更多
文摘<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand. </div>
文摘随着我国"一带一路"战略的推进,新疆应当充分发挥其区域优势,着力于打造丝绸之路经济带核心区,其中提高汉语-维吾尔语(汉-维)之间机器翻译的质量有着重要的现实意义。通过对低频维吾尔语词汇进行词干词缀分割进行了基于三种不同机器翻译系统的汉-维机器翻译对比实验。该方法不仅减少词汇表大小从而减轻未登录词(Out Of Vocabulary,OOV)问题,同时也大大提升了翻译结果。其中统计机器翻译结果提升最明显,比原始提升了3.29个BLEU值。
基金support of National Natural Science Foundation of China(U1936213)Yunnan Provincial Natural Science Foundation,“Robustness analysis method and coupling mechanism of complex coupled network system”(202101AT070167)Yunnan Provincial Major Science and Technology Program,“Construction and application demonstration of intelligent diagnosis and treatment system for childhood diseases based on intelligent medical platform”(202102AA100021).
文摘With the advancement of network communication technology,network traffic shows explosive growth.Consequently,network attacks occur frequently.Network intrusion detection systems are still the primary means of detecting attacks.However,two challenges continue to stymie the development of a viable network intrusion detection system:imbalanced training data and new undiscovered attacks.Therefore,this study proposes a unique deep learning-based intrusion detection method.We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data.Then the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to train.Finally,the distance relationship between the triples determines whether the traffic is an attack.In addition,to improve the accuracy of detecting unknown attacks,this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature space.The proposed approach’s effectiveness,stability,and significance are evaluated against advanced models on the Android Adware and General Malware Dataset(AAGM17),Knowledge Discovery and Data Mining Cup 1999(KDDCUP99),Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation Dataset(CICIDS2017),UNSW-NB15,Network Security Lab-Knowledge Discovery and Data Mining(NSL-KDD)datasets.The achieved results confirmed the superiority of the proposed method for the task of network intrusion detection.
