Reviewerly/exHarmony huggingface dataset:
VDBWGDL: Vulnerability Detection Based On Weight Graph And Deep Learning

Vulnerability detection has always been an essential part of maintaining information security, and the existing work can significantly improve the performance of vulnerability detection. However, due to the differences in representation forms and deep learning models, various methods still have some limitations. In order to overcome this defect, We propose a vulnerability detection method VDBWGDL, based on weight graphs and deep learning. Firstly, it accurately locates vulnerability-sensitive keywords and generates variant codes that satisfy vulnerability trigger logic and programmer programming style through code variant methods. Then, the control flow graph is sliced for vulnerable code keywords and program critical statements. The code block is converted into a vector containing rich semantic information and input into the weight map through the deep learning model. According to specific rules, different weights are set for each node. Finally, the similarity is obtained through the similarity comparison algorithm, and the suspected vulnerability is output according to different thresholds. VDBWGDL improves the accuracy and F1 value by 3.98% and 4.85% compared with four state-of-the-art models. The experimental results prove the effectiveness of VDBWGDL.
Real-Time Laparoscopic Cholecystectomy Simulation Using a Particle-Based Physical System

Laparoscopic cholecystectomy is used to treat cholecystitis and cholelithiasis. Because the high risk of the surgery prevents novice doctors from practicing it on real patients, VR-based surgical simulation has been developed to simulate surgical procedures to train surgeons without patients, cadavers, or animals. In this study, we propose a real-time system designed to provide plausible visual and tactile simulation of the main surgical procedures. To achieve this, the physical properties of organs are modeled by particles, and cluster-based shape matching is used to simulate soft deformation. The haptic interaction between tools and soft tissue is modeled as a collision between a capsule and particles. Constraint-based haptic rendering is used to generate feedback force and the non-penetrating position of the virtual tool. The proposed system can simulate the major steps of laparoscopic cholecystectomy, such as the anatomy of Calot's triangle, clipping of the cystic duct and biliary artery, disjunction of the cystic duct and biliary artery, and separation of the gallbladder bed. The experimental results show that haptic rendering can be performed at a high frequency (> 900 Hz), whereas mesh skinning and graphics rendering can be performed at 60 frames per second (fps).
A Comprehensive Dynamic Quality Assessment Method for Cyber Threat Intelligence

Extraordinary growth of the Internet poses a great challenge for defending worldwide evolution of cyber attacks. Introducing cyber threat intelligence (CTI) is a promising approach for alleviating malicious attacks, which heavily relies on the quality of CTI themselves. However, most of current studies develop CTI quality assessment from the perspective of source or content separately, regardless of their availability in practical. In this paper, a dynamic method named CTIC to comprehensively assess CTI quality is proposed. Specifically, we propose a novel CTI feed assessing scheme by modeling the interactions of feeds as a correlation graph. An iterative algorithm is elaborated to depict the feed quality precisely. We design a CTI content assessing scheme together with a machine learning algorithm to score the availability of content from multi-dimensions. Experimental results on real data confirm our proposed mechanism can quantitatively as well as effectively assess CTI quality.
Creation of a Distance Communication Channel With Gamification Elements

The chapter investigates the ways of a distance communication channel with gamification elements created in the electronic educational environment of maritime higher education institutions. The example of the gamified educational electronic environment on LMS Moodle is presented in the chapter. The chapter also gives examples of gamified activities on LMS Moodle to form communicative competency of future maritime professionals. The distance communication channel presented in the chapter contains the use of a social virtual reality platform. The static 360-degree virtual environment with a 2D video stream for English classes was analyzed. According to the received results, the quality and success of training increased. The use of gamification proved useful and helpful in the distance education of future maritime specialists.
Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization

To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.
Knowledge Graph Completion Based on Multi-Relation Graph Attention Network

Knowledge Graph Completion (KGC), can be performed mainly by inferring missing facts from entities and relations already in the knowledge graphs. However, most methods for KGC only focus on modeling undirected or single relational graph data, ignoring semantic information of multiple relations. We argue that information in the directed edge flows bi-directionally and there exists a latent reverse relation for each relation. These latent reverse relations contain additional information for completing knowledge graphs. Thus, in this paper, we propose a novel method called Knowledge Graph Completion based on Multi-relation Graph Attention neTwork (KGC-MGAT) for more accurate knowledge graph completion. We propose a multi-relational graph attention network to embed each triple and its reverse one respectively. Finally, we design a novel optimal objective for training. We conduct extensive experiments, and the results show the superiority of our method.
Design of remote injury diagnosis system for Wushu competition based on wireless sensor network

INTRODUCTION: In this paper, a remote injury diagnosis system for Wushu competition based on wireless sensor network is designed to improve the safety of athletes in the process of Wushu competition. OBJECTIVES: Improve the safety of athletes during martial arts competitions. METHODS: The pulse sensor, temperature sensor and ECG sensor are set as the terminal nodes of the remote injury diagnosis system for Wushu competition. The physiological parameters of athletes in Wushu competition are collected by the sensor, and the collected parameters are transmitted to the wireless RF module. The wireless RF module uses the wireless sensor network to realize the wireless communication of various parameter data through the routing node and terminal node, and transmits the data to the remote diagnosis module. The remote diagnosis module uses the collected physiological parameters of athletes to realize the remote diagnosis of injury in Wushu competition through particle swarm optimization-support vector machine diagnosis model. RESULTS: The experimental results show that the designed system can remotely collect the physiological parameters of athletes in Wushu competition, and remotely diagnose the injury of Wushu competition according to the collected data, and the diagnosis accuracy is as high as 99%. CONCLUSION: It has good safety performance and is of practical significance.
On the Design of a Matrix Adaptation Evolution Strategy for Optimization on General Quadratic Manifolds

An evolution strategy design is presented that allows for an evolution on general quadratic manifolds. That is, it covers elliptic, parabolic, and hyperbolic equality constraints. The peculiarity of the presented algorithm design is that it is an interior point method. It evaluates the objective function only for feasible search parameter vectors and it evolves itself on the nonlinear constraint manifold. Such a characteristic is particularly important in situations where it is not possible to evaluate infeasible parameter vectors, e.g., in simulation-based optimization. This is achieved by a closed form transformation of an individual’s parameter vector, which is in contrast to iterative repair mechanisms. This constraint handling approach is incorporated into a matrix adaptation evolution strategy making such algorithms capable of handling problems containing the constraints considered. Results of different experiments are presented. A test problem consisting of a spherical objective function and a single hyperbolic/parabolic equality constraint is used. It is designed to be scalable in the dimension. As a further benchmark, the Thomson problem is used. Both problems are used to compare the performance of the developed algorithm with other optimization methods supporting constraints. The experiments show the effectiveness of the proposed algorithm on the considered problems. Additionally, an idea for handling multiple constraints is discussed. And for a better understanding of the dynamical behavior of the proposed algorithm, single run dynamics are presented.
exploring Fine-Grained Sparsity in Convolutional Neural Networks for Efficient Inference

Neural networks contain considerable redundant computation, which drags down the inference efficiency and hinders the deployment on resource-limited devices. In this paper, we study the sparsity in convolutional neural networks and propose a generic sparse mask mechanism to improve the inference efficiency of networks. Specifically, sparse masks are learned in both data and channel dimensions to dynamically localize and skip redundant computation at a fine-grained level. Based on our sparse mask mechanism, we develop SMPointSeg, SMSR, and SMStereo for point cloud semantic segmentation, single image super-resolution, and stereo matching tasks, respectively. It is demonstrated that our sparse masks are well compatible to different model components and network architectures to accurately localize redundant computation, with computational cost being significantly reduced for practical speedup. Extensive experiments show that our SMPointSeg, SMSR, and SMStereo achieve state-of-the-art performance on benchmark datasets in terms of both accuracy and efficiency.
Online monitoring of the axial thermal displacement of machine tools using a laser mouse sensor

We present a low-cost online system for monitoring the axial thermal displacement of machine tools. The proposed monitoring system includes an embedded optical sensor derived from a laser mouse; an image acquisition microcontroller; speckle patterns; and an edge computer that hosts software, including an image display module, a displacement calculation module, an image enhancement module, and a data visualization module. The proposed sensing system can measure the displacement in two orthogonal directions simultaneously by employing digital image correlation; thus, the proposed system is a two-dimensional displacement sensor. The sensing system is benefited from image enhancement techniques and customized optimal speckle patterns printed using a standard low-cost monochrome laser printer. Experimental results indicate that the proposed displacement sensing system has an accuracy and a precision of <5 μm in both orthogonal directions. The two-dimensional displacement sensing system was used for the online monitoring of the thermal deformation of a feed drive system for machine tools, and the performance was assessed experimentally. The complete procedure for measuring the displacement due to thermal deformation was conducted automatically and can be completed in 2 min using a program coded in a computer numerical control controller.
Guided Gaussian range kernel filtering based on similarity-aware window

We present a window-adaptive Gaussian guided filtering method to smooth images while preserving edge features. The key to our algorithm is designing a similarity-aware filtering window based on density clustering to protect the edge structure and introducing a small-scale Gaussian spatial kernel as the input of the Gaussian range kernel to construct a guided filter for image smoothing. Specifically, we first utilized the Gaussian spatial kernel filtering with a small spatial bandwidth to yield a guidance input. Then, the Mahalanobis metric was used to calculate the distance between the center point and the other pixels in the box filtering window, by which we employed the center density clustering algorithm to obtain a better nonbox region (i.e., similarity-aware window) in which each pixel was similar to the center point. Finally, based on the guidance input and the similarity-aware window, the guided Gaussian range filter performed better on image smoothing. Our proposed algorithm is simple and easy to implement. In particular, the window-aware technology effectively improved edge protection, and it can be widely used in image denoising, background smoothing, detexturing, detail enhancement, and edge extraction.
Towards a category-extended object detector with limited data

Object detectors are typically learned on fully-annotated training data with fixed predefined categories. However, categories are often required to be increased progressively. Usually, only the original training set annotated with old classes and some new training data labeled with new classes are available in such scenarios. Based on the limited datasets, a unified detector that can handle all categories is strongly needed. We propose a practical scheme to achieve it in this work. A conflict-free loss is designed to avoid label ambiguity, leading to an acceptable detector in one training round. To further improve performance, we propose a retraining phase in which Monte Carlo Dropout is employed to calculate the localization confidence to mine more accurate bounding boxes, and an overlap-weighted method is proposed for making better use of pseudo annotations during retraining. Extensive experiments demonstrate the effectiveness of our method.
Progressive enhancement network with pseudo labels for weakly supervised temporal action localization

Weakly supervised temporal action localization (WSTAL) is crucial for real world applications, as it relieves the huge burden of frame-level annotations for fully supervised action detection. Most existing WSTAL methods focused on classifying video snippets, or detecting action boundaries. However, the predictions from these well-designed models have not been fully utilized. Accordingly, we propose a weakly-supervised framework called the progressive enhancement network (PEN), which takes full advantages of the predictions generated by the preceding models to enhance the subsequent models. Specifically, snippet-level pseudo labels are generated from the preceding predictions by considering the similarity and temporal distance between action snippets. Then subsequent models are progressively enhanced by using pseudo labels as a supervision, and utilizing their underlying semantics to make the feature representation more qualified for the temporal localization task. Extensive experiments which are carried out on two popular benchmarks, THUMOS'14 and ActivityNet v1.2, demonstrate the effectiveness of our method.
Self-feeding training method for semi-supervised grammatical error correction

Grammatical error correction (GEC) has been successful with deep and complex neural machine translation models, but the annotated data to train the model are scarce. We propose a novel self-feeding training method that generates incorrect sentences from freely available correct sentences. The proposed training method can generate appropriate wrong sentences from unlabeled sentences, using a data generation model trained as an autoencoder. It can also add artificial noise to correct sentences to automatically generate incorrect sentences. We show that the GEC models trained with the self-feeding training method are successful without extra annotated data or deeper neural network-based models, achieving F0.5 score of 0.5982 on the CoNLL-2014 Shared Task test data with a transformer model. The results also show that fully unlabeled training is possible for data-scarce domains and languages.
The use of bilinear interpolation filter to remove image noise

Abstract The bilinear interpolation filter method is introduced to reduce the image noise in this work that contains the following three procedures. Firstly, the algorithm principle of bilinear interpolation filter is analyzed. On this basis, a case study is investigated to validate the effectiveness of bilinear interpolation filter. The signal-to-noise ratio, SNR, is finally applied for the image quality evaluation. Form the above procedures we find that the SNR to the image after processed by bilinear interpolation filter method is larger than that to the original image, which illustrates that the bilinear interpolation filter is an effective method to remove image noise.
Research on Rule Matching Model Based on Spark

Abstract Rule engines are widely used in engineering and academic fields because they can flexibly separate facts and rules from the rule matching process. Since the rule matching process is very time-consuming, and the traditional rule matching uses single-computer operation when many facts and rules exceed the computer’s memory and computational capacity limit, it will cause the application to crash and paralyze. To solve the above problems, this paper investigates the Spark framework and Rete algorithm to take advantage of Spark’s in-memory computation to alleviate the time-consuming problem of the traditional rule matching process. A high-performance distributed rule matching model is designed by combining the actual rule matching scenario and the development process. In addition, according to the form of rules and facts in the actual scenario, this paper effectively divides the matching process and improves the scalability of the rule matching model.
Improved ViBe algorithm based on multi-frame combined with adaptive threshold

Abstract For moving human target detection under video surveillance, the traditional vibe algorithm is often disturbed by environmental changes, resulting in “ghosts”, incomplete detection results and “holes” in the human body. An improved vibe algorithm based on multi frame combined with adaptive threshold is proposed. Firstly, the sample set of vibe algorithm is expanded to 24 fields to reduce the possibility of pixel misclassification; Secondly, the historical pixel queue is introduced, and the initialization background model without ghost is obtained according to the change of foreground and background pixels in time domain; Finally, the distance determination threshold is dynamically adjusted by using the gray characteristic convergence and divergence, and the adaptive update factor of the model is calculated by introducing two fixed parameters to optimize the update rate of the background model. The experimental results show that the algorithm is not only suitable for general scenes, but also for dynamic complex scenes, it can accurately detect the foreground of moving human targets, eliminate “ghosts”, obtain complete moving targets, avoid the appearance of “holes”, improve the accuracy of detecting foreground human targets in dynamic complex environments, and enhance the robustness of the algorithm.
Survey of Various Graphical Password Techniques and Their Schemes

User authentication mostly relies on using the text password methods for its simplicity; however, it is often predictable and vulnerable to some common attacks. To overcome the shortcomings of the text password, alternative authentication methods have been introduced, including graphical password authentication methods. The graphical password technique depends on users choosing a series of images, patterns, or drawing something to be the password. Although the graphical password technique helps users to remember their passwords better, this technique is also vulnerable to some attacks. This paper provides a summary of authentication methods and a survey for several graphical password techniques, where these techniques are categorized into three categories: Recognition-based technique, Recall-based technique, and Hybrid technique. In addition, a study of the schemes of each technique and a summary of the attacks against this technique were presented. This paper can help researchers to design more secure graphical password schemes based on the results of this study, which helps in designing new schemes with more usability and security features.
Efficient and error-bounded spatiotemporal quantile monitoring in edge computing environments

Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM.
Smile: Secure Memory Introspection for Live Enclave

SGX enclaves prevent external software from accessing their memory. This feature conflicts with legitimate needs for enclave memory introspection, e.g., runtime stack collection on an enclave under a return-oriented-programming attack. We propose SMILE for enclave owners to acquire live enclave contents with the assistance of a semi-trusted agent installed by the host platform’s vendor as a plug-in of the System Management Interrupt handler. SMILE authenticates the enclave under introspection without trusting the kernel nor depending on the SGX attestation facility. SMILE is enclave security preserving as breaking of SMILE does not undermine enclave security. It allows a cloud server to provide the enclave introspection service. We have implemented a SMILE prototype and run various experiments to read enclave code, heap, stack and SSA frames. The total cost for introspecting one page is less than 300 microseconds.