Reviewerly/exHarmony huggingface dataset:
Analyzing PTM attack traces through PageDumper: A case study

Page Table Manipulation (PTM) attack is a type of data only attack that alters memory protection attributes through manipulation of page table data structures. This attack can bypass deployed kernel hardening techniques such as Supervisor Mode Execution Prevention(SMEP) and Write ⊕ Execute (and/or DEP/NX) that are implemented at the page level of abstraction and rely heavily on the integrity of page tables. In turn it enables the attacker to execute code with kernel privileges by disabling memory protection through the manipulation of code pages and/or injection of malicious code. In this paper, we first highlight the impacts of PTM attacks by presenting a case study that bypasses the popular kernel hardening solution SMEP for Linux based systems. Thereafter, we discuss our implementation of an instance of PTM attack, targeting the manipulation of page table attributes. Finally, we present our approach of analysis for the collected PTM attack footprints from runtime system memory using our previously developed tool PageDumper. PageDumper collects such PTM based attack footprints from runtime system memory along with other paging metadata to support an in-depth analysis of Linux volatile memory.
Neural Data-to-Text Generation Guided by Predicted Plan

Data-to-text generation task aims to generate natural language text from structured data and has made great progress in recent years with the help of end-to-end neural network models. However, these end-to-end approaches often ignore the structure of the output text and convey the information in the input data in a random order. When faced with the data-to-text generation task, a person tends to make a plan for the complex input before writing the final text, which is inconsistent with end-to-end approaches. In this paper, we propose a novel plan-guided data-to-text generation framework consisting of a plan generator GT5 and a text generator Share-T5. The plan generator GT5 first predicts a plan based on the input data and then the text generator Share-T5 generates the target text based on the input data and the predicted plan. Empirical comparisons with strong baselines on two benchmark datasets show that our proposed plan-guided data-to-text generation framework can significantly improve the performance of plan prediction and text generation.
Pedestrian Detection in Thermal Images by Enhancing the Brightness of Pedestrians

Thermal image, which is not affected by visibility, has attracted significant attention in pedestrian detection. How-ever, thermal images have poorer image quality and lack color and texture features compared with RGB images. Furthermore, pedestrian detectors based on deep learning often rely heavily on feature extraction networks. As a result, the performance of the detectors tends to decrease when directly applied to thermal images. To solve this problem, we design a pre-processing network to fully use the feature that pedestrians have higher brightness in thermal images. The pre-processing network can enhance the brightness of pedestrians in thermal images. Then we filter out the brightest area from the processed image by increasing the contrast, and input the filtered result into the detector together with the original image to help the detector find pedestrians. In addition, we found that an overly complex feature extraction network is redundant for thermal images and will have a negative impact. On this basis, we simplify the feature extraction network of YOLOv3. After simplification, the accuracy and running speed are improved, and the memory usage of the model is reduced. Through sufficient experiments on the KAIST dataset, it is proved that our method can significantly improve the performance of pedestrian detection.
Text Analysis Method Based on Multi-channel Parallel Classifier

In recent years, with the continuous recognition of the value of data, text sentiment analysis in natural language processing has gradually become a research hotspot in the field of artificial intelligence. In this article, we propose a multi-channel parallel algorithm. First, train the entire network by constructing a word embedding layer, map the vocabulary to a higher-dimensional space through word2vec. Then the generated embedding matrix is integrated with the parallel classifier model based on TextCNN, LSTM and Transformer. We use web crawler technology to extract sentiment classification data set from various industries and multiple fields, and conduct comparative experiments on this data set. Experimental results show that the effect of this model is better than that of a single-kernel classifier model.
Font Design Method Based on Multi-scale CycleGAN

Font design is an important research direction in art design and has high commercial value. It requires professionals to design fonts, which is not only time-consuming and costly, but also inefficient. Font-to-font translation is a commonly used font design method. Font-to-font translation is essentially the problem of image synthesis. Currently, generative adversarial networks (GANs) have been used for image synthesis and achieved some results. However, for the task of font-to-font translation the existing methods based on GANs generally have low-quality visual effects, such as incomplete fonts and distortion of font details. In order to solve the above problems, we propose a more effective multi-scale CycleGAN for font-to-font translation and the proposed method can obtain the font images with better visual quality. The proposed method is called MSM-CycleGAN. In MSM-CycleGAN, a U-net with multiple outputs (UM) is used as the generator. UM outputs the generated images of multiple scales. And then the outputs of UM are fed into the multi-scale discriminator. Our model uses the unsupervised learning method. This multi-scale discrimination method effectively improves the detailed information of the generated image. Experimental results show that our method performs better than other state-of-the-art image synthesis methods, and can obtain the font images with higher visual quality.
Multi-view User Preference Learning with Knowledge Graph for Recommendation

To learn more comprehensive user preference, existing works in recommendation propose to utilize side information, like Knowledge Graph (KG). However, the user representation can come from many views, including ID attributes of the user, collaborative signals of interaction history, and fine-grained preferences in KG, which has not been well studied in previous works. To address the limitation, in this work, we propose the Multi-view User Preference Learning with knowledge graph (MUPL) for recommendation to address the limitation. Specifically, we propose to employ Gate Recurrent Unit (GRU) to learn the user latent collaborative feature from interaction sequence. Besides, we design a Knowledge Graph Attention Network (KGANet) to capture user fine-grained preference for the entities related with the items. Then we fusion user ID attributes, the collaborative feature and fine-grained preference for entities into the user final representation. Similarly, we employ an item encoder to get the item final representation. Finally, a predictor is proposed for recommendation. Extensive experiments on three public datasets show that our model outperforms the state-of-the-art (SOTA) methods on effectiveness.
Knowledge Graph Based Adversarial Radar Threat Assessment

In the field of military equipment knowledge, there are a large number of equipment models, weapon types, working parameters, and other time-frequency-space data information, among which there is a lot of valuable information. At present, when combat-related personnel face this massive knowledge, they cannot efficiently obtain the key knowledge, which means that they cannot provide effective guidance based on the potential key knowledge. To solve this problem, based on the investigation and analysis of the existing knowledge graph construction method, we excavate and extract military equipment knowledge, instantiate and correlate different weapon equipment, and construct the knowledge graph of military equipment. Its construction can not only deeply study the key technical difficulties of the graph in this field, but also has strong strategic support for the future development of this field. In the end, we propose a threat assessment for target radar with a TransE inference model based on the knowledge graph.
Research on Obstacle Avoidance Strategy of Grid Workspace Based on Deep Reinforcement Learning

This paper proposes a multi-agent autonomous obstacle avoidance method based on the deep learning method. This method is used in grid workspace with strict position and motion constraints. The whole process uses Continuous Lidar data as the environment description to generate actions, and guides the agent to learn excellent strategies by continuously performing actions in the environment to obtain rewards. Based on the Proximal Policy Optimization(PPO) algorithm, we improved the design of the reward function, introduced density reward, distance reward and step penalty, which reduced the occurrence of congestion and improved the task efficiency of the agent. Finally, we conducted experiments in the simulation environment. Compared with the previous distributed methods, our method has smaller redundant paths and higher success rate of task completion. Compared with centralized methods, our method has advantages in terms of computational complexity, which can guarantee short planning times in large-scale environments.
Research on satellite Modeling and Real-time Simulation Monitoring Based on MBSE

The problems existing in the text-based systems engineering method include heavy workload for change management, poor readability of design documents, low reuse rate, inability to simulate and so on. This paper firstly draws on the Magic Grid methodology, which is suitable for complex product development, and comprehensively describes complex systems from multiple views, forming a set of model-based systems engineering modeling methods(MBSE)with strong traceability and logicality;Secondly, the method is applied to the power subsystem, and the models under different views are created using the system modeling language SysML (systems modeling language); Finally, based on the model, real-time simulation monitoring is carried out for the behavior activity graph, and the model simulation process can be traced back. It is verified that the modeling method can better design the system and meet the requirements of complex system design.e system and meet the requirements of complex system design.
Research and Simulation of High Frame Rate Real-time Image Tracking Technology Based on Compressed Sensing

In recent years, the compressed sensing theory in the field of signal processing has broken the Nyquist sampling theorem that the signal sampling rate must be more than twice the signal bandwidth in the traditional sampling process to accurately reconstruct the original signal, which greatly reduces the cost of information storage, processing and transmission. Target tracking is an important research topic in the field of computer vision. Target tracking has a great impact on human life and has been widely used. For example, security monitoring, human-computer interaction, military field and so on, but there are still many challenges in target tracking, such as illumination intensity change, background disturbance, occlusion, scale change and so on. Target tracking technology is a typical application of computer image vision technology. Aiming at the problem of large amount of computation and poor real-time performance in the process of video image target tracking, this paper uses compressed sensing algorithm to reduce the dimension of target features to reduce the amount of computation, and combines naive Bayesian classifier to recognize the target. Finally, this paper presents a tracking system using compressed sensing algorithm to sparse express target features. Through experiments, it can be found that the system has high tracking accuracy.
Research on Video Captioning Method Based on Semantic Key Frame

At present, in most video captioning models, when extracting video key frames, the extracted video key frames not only contain a lot of redundancy resulting video frame features with small diversity, but also ignore the video semantic information. Due to the neglect of such uniqueness, a semantic based video key frame method is proposed in this paper. Firstly, the initial video frames are extracted by combining the depth convolution network(CNN) and feature windowing. Secondly, this paper proposes to use the image captioning network to annotate the video frames and find the video key frames according to the semantic information. This method extracts the video key frames with more diversity and low redundancy. Then, we extracts the spatial, dynamic and regional features of the video to capture the potential context information in the video. Finally, we propose a pre-interactive LSTM model to effectively integrate text features, video features and context features, so as to solve the problem of context information loss and improve the annotation performance. Through the comparative experiment on MSR-VTT datasets, the results show that the proposed method improves by 1.2, 0.2, 0.1, 0.3 and 1.5 compared with the baseline model in terms of BLEU4, METEOR, ROUGE-L and CIDEr respectively, the MSVD datasets is improved by 2.2, 0.5, 0.4 and 4.4, which effectively improves the performance of video captioning.
Classification of Landscape Pictures Based on Residual Network

The continuous development of artificial intelligence has brought great convenience to our life and work. One of the more mature applications in artificial intelligence is the function of image classification, which provides a new method for us to process massive data. When faced with an explosion of image information on the Internet, we can use deep learning to categorize images and select only the categories we want to stay in. This will save us a lot of time and reduce our workload. This paper is based on deep learning to classify several common types of landscape maps, so that we can easily choose the scenery we want to visit. The residual network structure is adopted in this paper, and the accuracy can reach 87.6&#x0025; only after low rounds of training, which is better than the previous shallow network structure.
Disclosing Features and Characteristics of. COM Registrars' WHOIS Servers

The number of top-level domain names(.COM) registrars has long been dominant in the world&#x0027;s domain name resources. Still, few relevant documents have made specific analyses and statistics on the characteristics of. COM registrars. In this context, for the first time, we conducted a large-scale detection study on the WHOIS server of the. COM registrar. This article analyzes the characteristics of the. COM registrar from five aspects: the relationship between the world&#x0027;s top. COM registrar and the server and IP; the relationship between the IP address of the WHOIS server and the WHOIS server; nonexistent domain names response to different messages; abnormalities of WHOIS server; distribution of GDPR. In this article, through large-scale detection and analysis of the WHOIS server of the. COM registrar, we can intuitively see the relevant data of the WHOIS server. The COM registrar discovers the vital relationship between the registrar, server, and IP. The research on the direction of the WHOIS server of the. COM registrar provided by scientific researchers has played a perfect guiding role.
Design of Robot Intelligent Grasping System Based on Machine Vision

In researching intelligent robots, the grasping ability of robots is a very important skill, which has received extensive attention from researchers, and this technology has been widely used in industrial production. But in the current industrial environment, the grasping design of this type of robot is not perfect. This is because the industrial environment is complex and dynamic, so there are certain difficulties in identifying and extracting the environment, making it difficult for the traditional robot grasping system to judge the environment. Therefore, this paper Mainly redesigns the robot grasping system, including designing the robot&#x0027;s attitude system and grasping path. On this basis, the relevant algorithms of the local feature recognition of the environment and the robot grasping system are used, which greatly improves the accuracy and efficiency of robot grasping.
Research on Key Techniques of Text Recognition under Strong Light Noise

Text recognition technology based on deep learning has achieved great success in recent years. However, these recognition models have poor robustness under the influence of strong light noise. The existing solution to this problem leverages preprocessing methods to remove uneven lighting, which shows just passable effects on noise reduction for strong light. In this paper, we propose a text image recognition algorithm with attention-based strong light region perception module to solve this problem. The model is trained to detect strong light regions and guide the generated feature maps to focus on the clear part of the input image. Experimental results show that this model mitigates the impact of strong light noise pollution and improves the robustness of recognition technology on multiple datasets.
Long-Term and Short-Term Integrated Learning-Based Price Forecasting for Virtual Currency

As the blockchain and digital economy are becoming mature, a growing number of investors enter the virtual money market. How to exploit the available data to predict the changing trend of the virtual currency has been a focus. On account of the SVR model, this paper proposes a virtual currency price forecasting model with long-term and short-term integrated learning, taking advantage of extensive virtual currency price data to demonstrate the proposed model. The simulation experiment confirm that the accuracy of the SVR model is indeed increased on the virtual currency price forecasting issue.
Research on relation extraction method of contract text based on BERT

Entity recognition and relation extraction techniques in text mining are very important for the extraction of sensitive information in contract texts. Due to the lack of corpus in the field of contract texts, and the existing entity relation extraction models cannot extract relationships based on the semantics of contract texts. In view of the above problems, in order to enable the relation extraction model to apply the domain characteristics and domain knowledge of the contract domain, this paper proposes a joint entity-relation extraction method that integrates the representation of contract features, and incorporates the dictionary features into the model encoder part through the self-attention mechanism. The experimental results show that the learning of entity relationship information interaction and the introduction of the fusion contract type feature encoder can effectively improve the model performance.
Research on Neural Network Hardware Scheme for Frequency Measurement and Direction Finding

There are many hardware implementation schemes of neural network, each with its own advantages and disadvantages. This scheme puts forward a feasible scheme for Hopfield network to solve the specific problem of frequency measurement and direction finding. It is characterized by fast speed, learning and network evolution and integration, can directly access I/O, simple and feasible to achieve, with faster speed than software simulation.
An Anomaly Detection Framework for Multi-Variate Time Series in Electric Vehicles

The electric vehicle is drawing great attention recently because of both the convenience it offers and the safety issues it possesses. With more vehicle driving log files available, we are able to use the data mining techniques to cope with these safety issues, e.g., thermal runaway. In this paper, we propose an anomaly detection framework for electric vehicles using the multi-variate time series data collected from vehicles driving log files. Some experimental results are shown to validate the proposed framework.
Deep Graph Learning on Bipartite Observed Networks

In real-world settings, network structure observations are often incomplete due to the difficulties of data collection. However, most existing network analysis methods assume that the whole underlying structure is observed. In this paper, we focus on a common but overlooked network observation, named bipartite observation. We systematically study the influence of bipartite observations, and give an analytical representation using the stochastic block model. Moreover, we propose a new framework LP-GNN, which combines link prediction, sparsification, and graph neural networks for node classification tasks in bipartite observations. The effectiveness of LP-GNN is illustrated by experimental results on real datasets.
A Multi-scale Convolution and Gated Recurrent Unit Based Network for Limit Order Book Prediction

Nowadays, with the development of high-frequency trading (HFT), many people have started to pay attention to limit order book (LOB) data which records the orders in market trading. There have been many works using LOB data to make predictions, but these works do not distinguish between the orders of buyers and sellers. Intuitively, the transaction in the financial market is a process of the game between buyers and sellers, and it is important to represent the features of both sides. Inspired by this thought, in this paper, we propose a novel multi-scale deep learning model to represent the features of buyer and seller, respectively, and make predictions of the future trend of LOB data based on the changes in the features of both sides. Our model uses convolutional networks to extract the independent features of buyers and sellers and gated recurrent unit (GRU) networks to capture the market state. And it combines the features of both sides with the market state using the gating mechanism to represent features better. Experiments on the FI-2010 benchmark dataset and a bitcoin cryptocurrency dataset (BTC-2021) all show that our model can achieve state-of-the-art performance.
Towards Generating RESTful Full Stack Software: A Module Based Domain Driven Design Approach

Domain-driven design (DDD) has widely been used to develop RESTful software in a range of programming language platforms. The use of code generators in these technologies helps significantly increase productivity and achieve large scale software reuse. However, there have been no work that address incremental construction of RESTful full stack software (RFS) at both the module and software levels. In this paper, we propose a generative module-based method for RFS to bridge this gap. We characterise an essential RFS and realise this in a module-based DDD software architecture, named MOSA $$ ^{R} $$ . Our method takes as input a software configuration and the required software assets and automatically generates a module-based RFS software. We present algorithms for the frontend and backend generation functions. The backend software consist of web service modules. The frontend software consists of modules that are designed with single-page views. Each view can be nested to reflect the containment tree of the module. We demonstrate by implementing the generators for two popular platforms (React and Spring Boot). The evaluation shows that the generators are able to support large software.