scholarly journals SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jun Chen ◽  
Shize Guo ◽  
Xin Ma ◽  
Haiying Li ◽  
Jinhong Guo ◽  
...  
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Malicious Code ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Execution Sequence ◽  
Window Method ◽  
Great Progress ◽  
Sliding Window Method

Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723.

scholarly journals Visual-Inertial Odometry of Smartphone under Manhattan World

2020 ◽  
Vol 12 (22) ◽  
pp. 3818
Author(s):  
YuAn Wang ◽  
Liang Chen ◽  
Peng Wei ◽  
XiangChen Lu
Keyword(s):  
Mobile Phone ◽  
Structural Feature ◽  
Real Time ◽  
Structural Features ◽  
Feature Extraction Method ◽  
The Public ◽  
Window Method ◽  
Tightly Coupled ◽  
Sliding Window Method ◽  
Public Datasets

Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.

scholarly journals Commonsense Knowledge Aware Conversation Generation with Graph Attention

2018 ◽  
Author(s):  
Hao Zhou ◽  
Tom Young ◽  
Minlie Huang ◽  
Haizhou Zhao ◽  
Jingfang Xu ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Generation Model ◽  
Dynamic Graph ◽  
Commonsense Knowledge ◽  
Word Generation ◽  
Proposed Model ◽  
Knowledge Graphs

Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than state-of-the-art baselines. 

scholarly journals Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge

2022 ◽  
Vol 40 (1) ◽  
pp. 1-33
Author(s):  
Yang Deng ◽  
Yuexiang Xie ◽  
Yaliang Li ◽  
Min Yang ◽  
Wai Lam ◽  
...  
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Question Answering ◽  
Representation Learning ◽  
Attention Mechanism ◽  
Convolutional Network ◽  
Structure Information ◽  
Dialog Systems ◽  
External Knowledge ◽  
Knowledge Based

Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware attention mechanism to summarize both the context-based and knowledge-based interactions between questions and answers. To handle the diversity and complexity of KG information, we further propose a Contextualized Knowledge-aware Attentive Neural Network, which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network and comprehensively learns context-based and knowledge-based sentence representation via the multi-view knowledge-aware attention mechanism. We evaluate our method on four widely used benchmark QA datasets, including WikiQA, TREC QA, InsuranceQA, and Yahoo QA. Results verify the benefits of incorporating external knowledge from KG and show the robust superiority and extensive applicability of our method.

scholarly journals Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 92
Author(s):  
Xiaoning Han ◽  
Shuailong Li ◽  
Xiaohui Wang ◽  
Weijia Zhou
Keyword(s):  
Mobile Robots ◽  
Semantic Information ◽  
Semantic Mapping ◽  
Human Beings ◽  
Future Directions ◽  
Essential Requirement ◽  
Semantic Map ◽  
Semantic Maps ◽  
Great Progress ◽  
Indoor Scenes

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

scholarly journals A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1885
Author(s):  
Qiong Yao ◽  
Dan Song ◽  
Xiang Xu ◽  
Kun Zou
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Spatial Information ◽  
Individual Recognition ◽  
Vein Pattern ◽  
Low Contrast ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Finger Vein ◽  
Specific Implementation

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

scholarly journals Establishment of Effective Biomarkers for Depression Diagnosis With Fusion of Multiple Resting-State Connectivity Measures

2021 ◽  
Vol 15 ◽  
Author(s):  
Yanling Li ◽  
Xin Dai ◽  
Huawang Wu ◽  
Lijie Wang
Keyword(s):  
Clinical Diagnosis ◽  
Resting State ◽  
Causal Modeling ◽  
Support Vector ◽  
Major Depressive ◽  
Window Method ◽  
Underlying Basis ◽  
Sliding Window Method ◽  
The Right ◽  
Resting State Connectivity

Major depressive disorder (MDD) is a severe mental disorder and is lacking in biomarkers for clinical diagnosis. Previous studies have demonstrated that functional abnormalities of the unifying triple networks are the underlying basis of the neuropathology of depression. However, whether the functional properties of the triple network are effective biomarkers for the diagnosis of depression remains unclear. In our study, we used independent component analysis to define the triple networks, and resting-state functional connectivities (RSFCs), effective connectivities (EC) measured with dynamic causal modeling (DCM), and dynamic functional connectivity (dFC) measured with the sliding window method were applied to map the functional interactions between subcomponents of triple networks. Two-sample t-tests with p < 0.05 with Bonferroni correction were used to identify the significant differences between healthy controls (HCs) and MDD. Compared with HCs, the MDD showed significantly increased intrinsic FC between the left central executive network (CEN) and salience network (SAL), increased EC from the right CEN to left CEN, decreased EC from the right CEN to the default mode network (DMN), and decreased dFC between the right CEN and SAL, DMN. Moreover, by fusion of the changed RSFC, EC, and dFC as features, support vector classification could effectively distinguish the MDD from HCs. Our results demonstrated that fusion of the multiple functional connectivities measures of the triple networks is an effective way to reveal functional disruptions for MDD, which may facilitate establishing the clinical diagnosis biomarkers for depression.

scholarly journals Network Embedding via a Bi-Mode and Deep Neural Network Model

2017 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Semantic Information ◽  
Dimensional Space ◽  
Relation Extraction ◽  
Network Embedding ◽  
Structure Information ◽  
Second Mode ◽  
Real World Datasets ◽  
Low Dimensional

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

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