When generating primes $p$ and $q$ for an RSA key, the algorithm specifies that if $p-1$ and $q-1$ must be relatively prime to the public exponent $e$. If this is not done, then the decryption exponent is not well defined. However, what if a software bug allows the generation of public parameters $N$ and $e$ of an RSA key with this property and then it is subsequently used for encryption? Though this may seem like a purely academic question, a software bug in a preview release of the Windows 10 operating system makes this question of more than purely theoretical. Without a well defined decryption exponent, plaintexts encrypted to such keys will be undecryptable thus potentially losing user data, a serious software defect. Though the decryption exponent is no longer well defined, it is in fact possible to recover the a small number of potential plaintexts, if the prime factors $p$ and $q$ of the public modulus $N$ are known. This paper presents an analysis of what steps fail in the RSA algorithm and derives a plaintext recovery algorithm. The runtime of this algorithm is $O(e)$ making it practical to use, and it has been implemented in python.
Diagnosability is often considered as an important factor for measuring the self-diagnostic ability of network systems. However, classic system-level diagnosis focuses only on processor faults and ignores the objective reality of communication faults. Under real circumstances, missing edges and node failures usually occur simultaneously in multiprocessor systems (called hybrid fault circumstances). Therefore, it is important to study the diagnosability of multiprocessor systems under hybrid fault circumstances. In this paper, we propose several diagnosabilities of interconnection networks with missing edges and faulty nodes. By exploring some important relationships between diagnosability and the minimum degree of a network under hybrid fault circumstances, we present and prove the diagnosability of several classic interconnection networks, including BC (bijective connection) networks, star graphs, folded hypercubes, exchanged hypercubes, exchanged crossed cubes, k-ary n-cubes, bubble-sort star graphs and balanced hypercubes, with missing edges and broken-down nodes under the PMC (Preparata, Metze and Chien) and MM* (Maeng and Malek) models.
Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers.
A method for testing the health of ear's peripheral auditory nerve and its connection to the brainstem is called an auditory brainstem response (ABR). Manual quantification of ABR tests by an audiologist is not only costly but also time-consuming and susceptible to errors. Recently in machine learning have prompted a resurgence of research into ABR classification. This study presents an automated ABR recognition model. The initial step in our design process involves collecting a dataset by extracting ABR test images from sample test reports. Subsequently, we employ an elastic distortion approach to generate new images from the originals, effectively expanding the dataset while preserving the fundamental structure and morphology of the original ABR content. Finally, the Vision Transformer method was exploited to train and develop our model. In the testing phase, the incorporation of both the newly generated and original images yields an impressive accuracy rate of 97.83%. This result is noteworthy when benchmarked against the latest research in the field, underscoring the substantial performance enhancement achieved through the utilization of generated data.
As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and $(u,v)$-flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application.
Detecting if two functions in different compiled forms are similar has a wide range of applications in software security. We present a method that leverages both semantic and structural features of functions, learned by a neural-net model on the underlying control-flow graphs (CFGs). In particular, we devise a neural function-similarity regressor (NFSR) with attentions on dual CFGs. We train and evaluate NFSR on a dataset consisting of nearly 4 million functions from over 14 900 binary files. Experiments show that NFSR is superior to the SOTA models of SAFE, Gemini and GMN, especially for binary functions with large CFGs. An ablation study shows that attention on dual CFGs plays a significant role in detecting function similarities.
Unsupervised Domain Adaptation (UDA) techniques in real-world scenarios often encounter limitations due to their reliance on reducing distribution dissimilarity between source and target domains, assuming it leads to effective adaptation. However, they overlook the intricate factors causing domain shifts, including data distribution variations, domain-specific features and nonlinear relationships, thereby hindering robust performance in challenging UDA tasks. The Neuro-Fuzzy Meta-Learning (NF-ML) approach overcomes traditional UDA limitations with its flexible framework that adapts to intricate, nonlinear domain gaps without rigid assumptions. NF-ML enhances domain adaptation by selecting a UDA subset and optimizing their weights via a neuro-fuzzy system, utilizing meta-learning to efficiently adapt models to new domains using previously acquired knowledge. This approach mitigates domain adaptation challenges and bolsters traditional UDA methods' performance by harnessing the strengths of multiple UDA methods to enhance overall model generalization. The proposed approach shows potential in advancing domain adaptation research by providing a robust and efficient solution for real-world domain shifts. Experiments on three standard image datasets confirm the proposed approach's superiority over state-of-the-art UDA methods, validating the effectiveness of meta-learning. Remarkably, the Office+Caltech 10, ImageCLEF-DA and combined digit datasets exhibit substantial accuracy gains of 30.9%, 6.8% and 10.9%, respectively, compared with the best-second baseline UDA approach.
Closeness is one of the most studied characteristics of networks. Residual closeness is a very sensitive measure of graphs robustness. Additional closeness is a measure of growth potentials of networks. In this article, we calculate the closeness, vertex residual closeness, link residual closeness and additional closeness of lollipop graphs.
Security of computer information can be improved with the use of a network intrusion detection system. Since the network environment is becoming more complex, more and more new methods of attacking the network have emerged, making the original intrusion detection methods ineffective. Increased network activity also causes intrusion detection systems to identify errors more frequently. We suggest a new intrusion detection technique in this research that combines a Convolutional Neural Network (CNN) model with a Bi-directional Long Short-term Memory Network (BiLSTM) model for adding attention mechanisms. We distinguish our model from existing methods in three ways. First, we use the NCR-SMOTE algorithm to resample the dataset. Secondly, we use recursive feature elimination method based on extreme random tree to select features. Thirdly, we improve the profitability and accuracy of predictions by adding attention mechanism to CNN-BiLSTM. This experiment uses UNSW-UB15 dataset composed of real traffic, and the accuracy rate of multi-classification is 84.5$\%$; the accuracy rate of multi-classification in CSE-IC-IDS2018 dataset reached 98.3$\%$.
Robust and fast image recognition and matching is an important task in the underwater domain. The primary focus of this work is on extracting subsea features with sonar sensor for further Autonomous Underwater Vehicle navigation, such as the robotic localization and landmark mapping applications. With the assistance of high-resolution underwater features in the Side Scan Sonar (SSS) images, an efficient feature detector and descriptor, Speeded Up Robust Feature, is employed to seabed sonar image fusion task. In order to solve the nonlinear intensity difference problem in SSS images, the main novelty of this work is the proposed Underwater Wireless Sensor Network-based Delaunay Triangulation (UWSN-DT) algorithm for improving the performances of sonar map fusion accuracy with low computational complexity, in which the wireless nodes are considered as underwater feature points, since nodes could provide sufficiently useful information for the underwater map fusion, such as the location. In the simulated experiments, it shows that the presented UWSN-DT approach works efficiently and robustly, especially for the subsea environments where there are few distinguishable feature points.
Two vertices $u$ and $v$ in a graph $G=(V,E)$ are in the same orbit if there exists an automorphism $\phi $ of $G$ such that $\phi (u)=v$. The orbit number of a graph $G$, denoted by $Orb(G)$, is the smallest number of orbits, which form a partition of $V(G)$, in $G$. All vertex-transitive graphs $G$ are with $Orb(G)=1$. Since the $n$-dimensional hypercube, denoted by $Q_{n}$, is vertex-transitive, it follows that $Orb(Q_{n})=1$ for $n\geq 1$. Pai, Chang, and Yang proved that the $n$-dimensional folded crossed cube, denoted by $FCQ_{n}$, is vertex-transitive if and only if $n\in \{1,2,4\}$, namely $Orb(FCQ_{1})=Orb(FCQ_{2})=Orb(FCQ_{4})=1$. In this paper, we prove that $Orb(FCQ_{n})=2^{\lceil \frac{n}{2}\rceil -2}$ if $n\geq 6$ is even and $Orb(FCQ_{n}) = 2^{\lceil \frac{n}{2}\rceil -1}$ if $n\geq 3$ is odd.
Eager equality is a novel semantics for equality in the presence of partial operations. We consider term rewriting for eager equality for arithmetic in which division is a partial operator. We use common meadows which are essentially fields that contain an absorptive element $\bot $. The idea is that term rewriting is supposed to be semantics preserving for non-$\bot $ terms only. We show soundness and adequacy results for eager term rewriting w.r.t. the class of all common meadows. However, we show that an eager term rewrite system which is complete for common meadows of rational numbers is not easy to obtain, if it exists at all.
Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach, called Meta Parameters Learning via Meta-Learning (MPML), to combine online hyper-parameter adjustment scheme into meta-learning algorithm, which reduces the need to tune hyper-parameters. Specifically, a basic learning rate for each training task is put forward. Besides, the proposed algorithm dynamically adapts multiple basic learning rate and a shared meta-learning rate through conducting gradient descent alongside the initial optimization steps. In addition, the sensitivity with respect to hyper-parameter choices in the proposed approach are also discussed compared with model-agnostic meta-learning method. The experimental results on reinforcement learning problems demonstrate MPML algorithm is easy to implement and delivers more highly competitive performance than existing meta-learning methods on a diverse set of challenging control tasks.
Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few open-source annotated corpora for the CGEC task; the existing researches mainly concentrate on using data augmentation technology to alleviate the data-hungry problem. In this paper, rather than expanding training data, we propose a competitive CGEC model from a new insight for reducing model parameters. The model contains three main components: a sequence learning module, a grammatical generalization module and a parameter sharing module. Experimental results on two Chinese benchmarks demonstrate that the proposed model could achieve competitive performance over several baselines. Even if the parameter number of our model is reduced by 1/3, it could reach a comparable $F_{0.5}$ value of 30.75%. Furthermore, we utilize English datasets to evaluate the generalization and scalability of the proposed model. This could provide a new feasible research direction for CGEC research.
This paper proposes a new method to find high-probability truncated differentials using matrix muliplication. For Markov cipher with similar round function, suppose that the transition probability matrix of round function is D, then D-r contains all the differential probabilities of an r-round block cipher. To reduce the matrix dimension, we consider the word-oriented truncated differential and the truncated transition probability matrix T. Regardless of the effect of the S-box, we focus on whether there is a non-zero difference on one cell instead of the value of the difference. In this case, the matrix dimension reduces significantly and we can calculate T-r using a workstation. Then all the r-round truncated differential probabilities can be found from T-r. And the probability in T-r is the probability of the whole truncated differential hull but not a single or several truncated differential characteristics. Besides, we make a more accurate probability estimation of the truncated differential of lightweight block cipher. Combined with the truncated differential hull, we found some longer truncated differential distinguishers. And as T-r stores all the truncated differential probabilities, we can also find all the impossible truncated differentials.
IEEE 802.11ah, which is a wireless network protocol designed for Internet of Things (IoT), devised the Restrict Access Window (RAW) mechanism to tackle the collision problem. However, some RAW's slots may be overloaded; some RAW's slots may be lightly loaded or have no load because no stations (STA) allocated in these slots need to access channel. This work adopted (i) the Registered Backoff Time (RBT) mechanism such that each STA can register the backoff time for its future channel access in access point (AP) and (ii) the Claiming RAW mechanism such that STAs are allowed to notify AP that they have uplinked data to transmit. In this way, AP can (1) know which STAs have uplinked data to transmit and thus (2) re-schedule those STAs from the overloaded slots to the slots that (i) are underloaded or (ii) no STAs need to access channel based on STAs' RBTs in advance. As a result, the proposed Registration-based Regrouping for Load-Balance Channel Access (RRG-LBCA) method can achieve load balance among slots to increase the IEEE 802.11ah network's performance. The performance evaluation results shown that the proposed RRG-LBCA method can eliminate the collision situation and increase the aggregate throughput.
A web shell is a backdoor used by hackers to control Web servers and perform privilege escalation, and thus it is crucial to detect web shells effectively. However, the detection of obfuscated web shells has always been a challenge. Inspired by adversarial training methods in the field of computer vision, this paper proposes a generative adversarial network (GAN)-based web shell detection model training framework. Since there has been no method that can generate obfuscated web shells effectively, a generator based on the genetic algorithm, which combines and optimizes the pre-set obfuscation methods, is used to obtain new obfuscation combinations and generate obfuscated samples. The whole proposed framework is named the CWSOGG. When training the detection model, the generator generates web shells that can bypass the discriminator, and the discriminator catches the features of obfuscated samples. Through the adversarial training of the discriminator and generator, the detection model improves its ability to detect obfuscated web shells. To verify the proposed framework is flexible to different models, the discriminator based on four main neural networks has been implemented. Meanwhile, to build complete feature extraction models, both statistical and semantic features are extracted. Due to the lack of web shell data, a clean dataset containing 4,375 web shells is constructed and used to evaluate the CWSOGG. The results have shown that the detection accuracy of each model increases by 86.71% on the generated obfuscated web shells on average and by 7.50% on the simulated real-world obfuscated web shells on average.
Community structure over social media (SM) is the collaborative group of globally spread users with identical characteristics and ideologies. The collective features of SM are inherent with both the implicit and explicit nature of end-users. This paper presents an analytical and methodological community detection framework to bind passive users' implicit and explicit nature after scrutinizing graphical data to identify seed nodes and communities. Moreover, this work provides the concept of the unsupervised machine learning approach over the graphical perspective of SM to identify the trade-off between similarity of nodes attributes and density of connections for social theories. Subsequently, this paper evaluates a comprehensive analysis of the benchmark community detection algorithm (CDA) Label Propagation Algorithm (LPA), Clique Percolation Method (CPM), Democratic Estimate of the Modular Organization of a Network (DEMON) and Non-Negative Matrix Factorization (NMF). The evaluation has been carried out over modularity and normalized mutual information of resultant structured community on six real-time SM data set. The performance of benchmark CDAs is significantly increased after incorporating social theories. NMF, DEMON, CPM and LPA gained the highest improvement over Zachary's Karate Club data sets, i.e. approximate 26.91%, 21.68%, 18.79%, 19.96%, respectively.
