Reviewerly/exHarmony huggingface dataset:
On the Use of Refactoring in Security Vulnerability Fixes: An Exploratory Study on Maven Libraries

Third-party library dependencies are commonplace in today’s software development. With the growing threat of security vulnerabilities, applying security fixes in a timely manner is important to protect software systems. As such, the community developed a list of software and hardware weakness known as Common Weakness Enumeration (CWE) to assess vulnerabilities. Prior work has revealed that maintenance activities such as refactoring code potentially correlate with security-related aspects in the source code. In this work, we explore the relationship between refactoring and security by analyzing refactoring actions performed jointly with vulnerability fixes in practice. We conducted a case study to analyze 143 maven libraries in which 351 known vulnerabilities had been detected and fixed. Surprisingly, our exploratory results show that developers incorporate refactoring operations in their fixes, with 31.9% (112 out of 351) of the vulnerabilities paired with refactoring actions. We envision this short paper to open up potential new directions to motivate automated tool support, allowing developers to deliver fixes faster, while maintaining their code.
Fed-Fi: Federated Learning Malicious Model Detection Method Based on Feature Importance

Federated learning, as a new security exchange paradigm, is widely used in medical care, driverless cars, finance, and other fields. However, federated learning still faces the problem of sybil attacks common in distributed frameworks. The existing schemes mainly defend against malicious model attacks with distance comparison, neural network, and confidence vote. But they are significantly limited in dealing with collusive sybil attacks. Therefore, we propose a federated learning malicious model detection method based on feature importance (Fed-Fi). Firstly, we screen important features by feature importance reasoning method based on LRP and compare the similarity based on Hamming distance between important features. Then, we adjust model learning rate adaptively to reduce the effect of collusive sybil attacks on the global model. The experimental results indicate that it can effectively resist the attack of collusive sybils in federated learning.
Optimization of 3D Virtual Reality Technology in High School Physics Direct-Type Teaching

Virtual reality technology is a computer simulation that allows the creation and experience of virtual worlds, using computers to generate a simulated environment into which the user is immersed. Through a theoretical exposition of 3D virtual reality technology, the elements of teaching and learning and virtual reality technology are analyzed in terms of abstract virtual cognition and concrete modeling techniques, and the forms of application of virtual reality technology in high school physics teaching are described, along with associated educational technology theories. The era of core literacy development poses new challenges to basic teaching and requires new teaching methods and approaches to adapt. The development of 3D virtual reality technology has provided new opportunities for teaching reform. Firstly, the unique charm of virtual reality technology itself has deeply attracted many experts and scholars to explore its application in pedagogy. Secondly, the scale and effect of virtual reality technology in pedagogy applications are still far from the ideal goal in people’s mind, and further exploration and research are needed in the theoretical support of educational technology research. Thirdly, in the process of high school basic teaching, on the one hand, basic experiments play a crucial role, and on the other hand, transforming abstract basic principles and concepts into concrete visible and operable teaching methods is also an important direction for its future development. Combined with the development tools and software to build processes and models, the knowledge of electricity and mechanics in high school physics is modeled in 3Ds MAX and Unity3D to provide technical support for the application of virtual reality in high school physics teaching and to promote the reform of high school physics teaching. Based on an in-depth analysis of virtual reality technology in the construction of teaching resources, this paper explores the feasibility of using software systems and hardware facilities of 3D virtual reality technology in high school physics teaching, such as experimental demonstration, geometric modeling, and 3D graphic transformation. It fits the cognitive level of high school students, constructs 3D virtual scenes and virtual object motion trajectory models that meet the requirements of the high school physics curriculum, and promotes the application of virtual reality technology in high school physics teaching and visualizing abstract physics knowledge, with clear student understanding and significant improvement in learning efficiency.
TBAL: Two-stage batch-mode active learning for image classification

The success of deep learning applications relies on a large number of labeled data. Active learning aims at identifying most informative unlabeled samples for labeling so as to achieve comparable performance with as few labeled data as possible. A two-stage batch-mode active learning (TBAL) method is proposed in this work. In the first stage of TBAL, we propose a new semi-supervised learning framework that offers accurate uncertainty estimation and finds a high-dimensional feature representation for unlabeled samples. In other words, it leverages a small number of labeled data as well as a large number of unlabeled data. In the second stage of TBAL, we propose a novel technique called cluster re-balancing (CRB). It takes correlations within a batch into account and fits a query strategy in batch mode. The TBAL method can obtain informative batches of samples by considering uncertainty and diversity jointly in a high-dimensional feature space. Moreover, in order to compare the robustness of semi-supervised learning based active learning methods, a new evaluation protocol based on transfer learning is introduced. Experiments on SVHN, CIFAR-10 and CUB-200-2011 datasets show that our proposed CRB based query strategy that employs a semi-supervised model with a good balance between uncertainty and diversity outperforms various baselines as well as other state-of-the-art methods by a clear margin.
Improving small private exponent attack on the Murru-Saettone cryptosystem

The Murru-Saettone cryptosystem is an RSA-like cryptosystem constructed from a particular group with a non-standard product in a cubic field related to the general cubic form of the Pell equation. In this cryptosystem, the public key is (n,e,r), where n is a product of two large primes p, and q of the same bit size, e is a positive integer such that gcd⁡(e,ψ(n))=1, ψ(n)=(p2+p+1)(q2+q+1), and r is a non-cube integer in Zp, Zq and Zn. The private exponent d is the multiplicative inverse of e modulo ψ(n). In this paper, we use continued fractions to make three attacks on RSA work on the Murru-Saettone cryptosystem. The first attack, due to de Weger, works when d≤n3/4p−q. The second attack, due to Maitra-Sarkar (ISC 2008), works when d≤n3/42q−p. The third attack, due to Nassr et al. (AICCSA 2008), works when d<n(1−α)/2 assuming that we know an approximation p0 of p, such that |p−p0|≤nα,α≤1/2.
Characteristics Analysis of Applied Mathematics in Colleges and Universities Based on Big Data Mining Algorithm Model

To analyze the mathematical nature of applied mathematics in colleges and universities, a method based on a model of big data mining algorithms is proposed. Firstly, the modeling is carried out through the deployment of nodes, which can accurately collect the characteristics of data information in the case of massive big data; secondly, the acquisition algorithm of multi-feature fusion is systematically optimized, which can avoid data interference and collect features quickly and accurately; thirdly, by transforming the multidimensional application-oriented university applied mathematics discipline model into an unlimited experience loss minimization problem with penalty factors, the improved support vector machine algorithm is used to construct and solve the objective kernel function. It is proved that the intelligent collection method based on big data mining algorithm model for the characteristics of applied mathematics in colleges and universities is effective. The sympathetic set corresponding to the flowing data is [115∼135]; the data similarity in big data environment is 1; in order to ensure that the intelligent collection method of applied mathematics discipline characteristics in colleges and universities based on big data analysis can collect data characteristics more accurately, which are 110/76.65/78/110, respectively.
A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications.
Automatic Object Detection and Direction Prediction of Unmanned Vessels Based on Multiple Convolutional Neural Network Technology

This study aims to detect objects quickly and accurately and then start the necessary obstacle avoidance procedure when the uncrewed vessel is at sea. This study uses a multivariate convolutional neural network (CNN) to perform automatic object detection and direction prediction for uncrewed vessels. This study is divided into three parts for processing. The first part of this process uses camera calibration technology to correct the image. Discrete cosine transform (DCT) is then used to detect sea level. Finally, this study uses Kalman filtering and affine transformation to stabilize images taken by uncrewed vessels at sea. The second part of the processing system uses a CNN to detect sea objects automatically. The third part of the process uses the dual-lens camera installed on the vessel to detect the distance and direction of objects at sea. In the experiment, the detection rate can reach more than 91% in this study method. In the experiment on image stabilization, this study’s method can also effectively improve video instability. In the experiment involving the distance and direction of the object, the experimental results show that the distance and direction of the object obtained by this study method have a distance error value of less than 10%, and the prediction results have a good effect no matter whether the object is at a short or long distance. It is hoped that this paper’s method can be applied to the automatic obstacle avoidance of unmanned vessels.
Assessment of new spectral indices and multi-seasonal ASTER data for gypsum mapping

Abstract Advanced spaceborne thermal emission and reflection radiometer (ASTER) data and different spectral indices were employed to map gypsum minerals. However, most proposed gypsum indices are designed based on the 2.21 μm gypsum absorption, overlapping with most hydroxy-bearing minerals. Moreover, the seasonal mutual transformation between gypsum, bassanite, and anhydrite may lead to seasonal reflectance variability of gypsum formation pixels, affecting the classification accuracy of gypsum indices. In this research, the feasibility of 2.26 μm (ASTER band7) reflectance absorption for gypsum mapping was assessed, using lab and ASTER reflectance. On the basis of this, two new ASTER gypsum spectral indices (GI 1 : B4*B8/B6*B7; GI 2 : B4*B8/B7*B7) were proposed and applied to exclude the interference of hydroxyl-bearing minerals effectively. Seasonal reflectance variability of gypsum formation pixels was confirmed, and it causes the accuracy difference of gypsum indices for multi-seasonal ASTER data. The GI 1 achieves the most robust accuracy for multi-seasonal ASTER data with average areas under receiver operator characteristic (ROC) curve of 98.5% and 98.7% for summer and winter ASTER data. Therefore, the GI 1 can be used for gypsum mineral mapping, especially in the areas where clay minerals and other hydroxyl-containing minerals are widely distributed.
Exploring Gaze Movement Gesture Recognition Method for Eye-Based Interaction Using Eyewear with Infrared Distance Sensor Array

With the spread of eyewear devices, people are increasingly using information devices in various everyday situations. In these situations, it is important for eyewear devices to have eye-based interaction functions for simple hands-free input at a low cost. This paper proposes a gaze movement recognition method for simple hands-free interaction that uses eyewear equipped with an infrared distance sensor. The proposed method measures eyelid skin movement using an infrared distance sensor inside the eyewear and applies machine learning to the time-series sensor data to recognize gaze movements (e.g., up, down, left, and right). We implemented a prototype system and conducted evaluations with gaze movements including factors such as movement directions at 45-degree intervals and the movement distance difference in the same direction. The results showed the feasibility of the proposed method. The proposed method recognized 5 to 20 types of gaze movements with an F-value of 0.96 to 1.0. In addition, the proposed method was available with a limited number of sensors, such as two or three, and robust against disturbance in some usage conditions (e.g., body vibration, facial expression change). This paper provides helpful findings for the design of gaze movement recognition methods for simple hands-free interaction using eyewear devices at a low cost.
Smart Phone Sensor Data: Comparative Analysis of Various Classification Methods for Task of Human Activity Recognition

Human Activity Recognition has a long history of research and requires further exploration to produce useful and optimal outcomes. Areas such as medicine, daily routine, and security are some benefits that smartphone enables via embedded sensors. Our work has chosen sensor data of six activities such as standing, walking, laying from pre-recorded data set gathered via smart phone to evaluate the performance of various supervised machine learning algorithms. The results suggest that logistic regression has been an optimal choice based on experiments. Whereas, the Support Vector Machine (SVM) has shown to perform well with ninety-five percentage accuracy.
Residual Network based Single Object Tracking

In this paper, we propose the idea of single object tracking using RESNET-101. The proposed Residual Network based Single Object Tracking (R-SOT) consists of the basic idea of supervised learning. The frames are given as an input to the tracker and the tracker predict the bounding boxes and place them on a specified object and move that bounding box with that object in the entire frames of video. The detection of bounding boxes from RESNET-101 is performed by using Region Based Convolutional Neural network (RCNN) Object Detector. The performance of the proposed tracker is checked using Online Object Tracking Benchmark(OOTB) dataset. Test Results show that the proposed tracker is able to track the single object with high precision.
Propositional Satisfiability Logic via Ant Colony Optimization in Hopfield Neural Network

Minimizing the cost function that corresponds to propositional logic is vital to ensure the learning phase of HNN can occur optimally. In that regard, optimal and non-biased algorithm is required to ensure HNN will always converge to global solution. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to solve various combinatorial optimization problems. ACO simulates the behaviour of the real ants that forage for food and communication of ants through pheromone density. In this work, ACO will be used to minimize the cost function that corresponds to the logical rule in Hopfield Neural Network. ACO will utilize pheromone density to find the optimal path that leads to zero cost function without consuming more learning iteration. Performance for all learning models will be evaluated based on various performance metrics. Results collected from computer simulation implies that ACO outperformed conventional learning model in minimizing the logical cost function.
Exploring Generalizability of Fine-Tuned Models for Fake News Detection

The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an ‘infodemic’ by the CDC and WHO. Misinformation tied to the Covid-19 infodemic changes continuously; this can lead to performance degradation of fine-tuned models due to concept drift. Degredation can be mitigated if models generalize well-enough to capture some cyclical aspects of drifted data. In this paper, we explore generalizability of pre-trained and fine-tuned fake news detectors across 9 fake news datasets. We show that existing models often overfit on their training dataset and have poor performance on unseen data. However, on some subsets of unseen data that overlap with training data, models have higher accuracy. Based on this observation, we also present KMeans-Proxy, a fast and effective method based on K-Means clustering for quickly identifying these overlapping subsets of unseen data. KMeans-Proxy improves generalizability on unseen fake news datasets by 0.1-0.2 f1-points across datasets. We present both our generalizability experiments as well as KMeans-Proxy to further research in tackling the fake news problem.
Zero-Trust-Based Protection Scheme for Users in Internet of Vehicles

At present, the Internet of Vehicles technology is developing rapidly, but in the process of networking it may be attacked by hackers. In view of the tampering attack on the user’s device and identity, we establish a multifactor authentication scheme to achieve dual identity authentication through device fingerprint authentication and PKI authentication. In response to the attack by hackers in the application of data transmission, we use the SM series of state secret algorithms for data encryption and take advantage of the immutable and decentralized characteristics of blockchain to ensure the security and integrity of data collection and transmission. Through the zero-trust security network architecture, the security level of the system in the process of data transmission is effectively improved, the identity-centered dynamic access control is carried out, the user’s behavior is monitored in real time, and the malicious nodes are screened and eliminated, which improves the stability and security of the Internet of Vehicles data transmission system. By adopting the identity authentication pass rate, fingerprint authentication pass rate, API authentication pass rate, and SPA packet transmission acceptance rate as the factor set, the evaluation level of the vehicles is calculated. The experimental results show that compared with the traditional boundary-centered security protection, our scheme can protect a wider range of application security, even if there are security problems, the loss is less. Through the training simulation of the convolutional neural network, the classification accuracy of the trust level is improved.
Image Semantic Segmentation Method Based on Deep Fusion Network and Conditional Random Field

Aiming at the problems of missing points and wrong points in image semantic segmentation under complex background and small target, an image semantic segmentation method based on the fully convolution neural network and conditional random field is proposed. First, the deconvolution fusion structure is added to the fully convolution neural network to build a deep fusion network. The multiscale features are automatically obtained through the deep fusion network, and the shallow detail information and deep semantic information are fused to improve the processing accuracy of image rough segmentation. Then, the bivariate potential function of the conditional random field is optimized based on the convolution neural network, and it is used for image fine segmentation to obtain the final image segmentation result. Finally, the proposed method is experimentally analyzed based on the Cityscapes dataset. The results show that the proposed method can achieve accurate image segmentation, and the area under the segmentation curve of the overall size target is 93.6%, which is better than other methods.
PPGSign: Handwritten Signature Authentication using Wearable PPG Sensor

Handwritten signature authentication is a crucial service to defend against fraudulent activities. Existing automated solutions rely heavily on dedicated devices that are expensive and require different user efforts that affect the user experience. In this paper, we propose a new signature authentication system, PPGSign, which leverages Photoplethysmography (PPG) sensors in the existing wrist-worn wearable devices. The unique blood flow changes in the supplicant&#x2019;s hand movement are exploited in this system to validate the signature. To make PPGSign nonintrusive and secure, we explore effective algorithms to separate the signature signals from the heartbeat signals in the raw PPG signals. We build a low-cost hardware prototype to verify our proposed method. Our experimental results show that PPGSign can achieve an average F1 score of up to 98%, which verifies the feasibility and efficiency of the proposed solution.
Graph Based Document Model and its Application in Keyphrase Extraction

Graph-based document models, rather than the traditional Bag of Words (BOW) model, have opened the way to efficiently represent documents by capturing syntactic and semantic characteristics from the document. Because a graph is a mathematical model, it may effectively capture the best properties from documents and be utilized for a variety of Natural Language Processing (NLP) applications, as well as simplifying other computations. In a graph model, document terms are represented as nodes, and relationships between those terms are captured and joined together to produce edges, with a weightage based on the extent of the relationship. This work presents a systematic survey of existing graph models, with a focus on node selection, edge selection, and edge weighting factors that are appropriate for diverse NLP applications. Key extraction based on graph models, as well as various ranking algorithms, are also examined. This study shows that graph-based document models play an important role in representing textual documents and assisting with complex NLP tasks.