Reviewerly/exHarmony huggingface dataset:
Graph regularization centrality

This paper introduces a novel centrality for the nodes of a graph. Our centrality is based on the graph regularization, a tool of graph signal processing theory. For this reason, it is called graph regularization centrality (GRC). In order to define the centrality of a node, a delta signal centered on this node is defined and a new smooth signal is generated by the graph regularization of the delta signal. The transformation of the delta signal into the new smooth signal strongly depends on the position of the node in the graph. Our centrality takes advantage of this feature to define a centrality for each node of the graph. Synthetic and real-world graphs are used to demonstrate the effectiveness of our centrality that combines local and global positioning of nodes in one measure. Furthermore, it is compared against classical centralities and graph Fourier transform centrality, which is also based on graph signal processing theory. We conclude with a discussion of the main features of GRC and a proposal for potential future work.
Symmetric LINEX loss twin support vector machine for robust classification and its fast iterative algorithm

Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas traditional TSVM can be limited for data with outliers or noises. To address this problem, we propose a novel TSVM with the symmetric LINEX loss function (SLTSVM) for robust classification. There are several advantages of our method: (1) The performance of the proposed SLTSVM for data with outliers or noise can be improved by using the symmetric LINEX loss function. (2) The introduction of regularization term can effectively improve the generalization ability of our model. (3) An efficient iterative algorithm is developed to solve the optimization problems of our SLTSVM. (4) The convergence and time complexity of the iterative algorithm are analyzed in detail. Furthermore, our model does not involve loss function parameter, which makes our method more competitive. Experimental results on synthetic, benchmark and image datasets with label noises and feature noises demonstrate that our proposed method slightly outperforms other state-of-the-art methods on most datasets.
EM-test for homogeneity in location-scale mixture model with a structural scale

AbstractThe testing problem for the homogeneity of a location-scale mixture model is fundamental for subsequent statistical inference. In this paper, we employ the EM-test method to test the homogeneity of two commonly encountered location-scale mixture models with a structural scale, and we also establish the theoretical properties of test statistics. Further, to enhance the reliability of the test, based on the computer experiments approach, we develop a Bartlett-corrected approximate distribution for the proposed test statistics. Through Monte Carlo studies and real data example, the effectiveness of the proposed testing procedure is compared with that of the classical one, especially for small or moderate sample sizes.Keywords: Bartlett correctionComputer experimentsEM-testHomogeneity testMixture modelSmall sample size Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is supported by National Natural Science Foundation of China [No. 12101270], The Natural Science Foundation of the Jiangsu Higher Education Institutions of China [No. 22KJB110001], Ph.D. Teacher’s Research Support Project Foundation of Jiangsu Normal University [No. 21XFRX019], and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
Application of artificial intelligence in modern medicine

Over the last decade, artificial intelligence in medicine has attracted much attention and interest for its robust automation and efficiency in disease diagnosis, treatment and prognosis. It has shown great potential in bringing the revolutionary change in clinical practice. This review aimed to summarize the progress AI has made recently in several fields of medicine, including patient screening, diagnosis and treatment of cancerous and non-cancerous diseases, pharmaceutical development, clinical trial management and basic science. Meanwhile, we will discuss its future direction and important considerations, focusing on its utility, usability, validation, safety and ethnic problem.
LOFReg: An outlier-based regulariser for deep metric learning

Deep metric learning aims to create a feature space where the projected samples have maximized inter-class and minimized intra-class distance. Most approaches employ only distance-based metrics to achieve this objective, but neglect other properties of the projections in the embedding space, such as their density, sparsity and presence of outliers. In this paper, we propose a novel density-based regulariser, LOFReg, designed to be used as a complement to previously proposed distance-based metric learning loss functions for the re-identification (ReID) and few-shot classification (FSC) tasks. Our method is based on the well-known, in anomaly detection literature, local outlier factor (LOF) algorithm, which estimates the local density deviation of a data point with respect to its neighbours. These measurements are used in our regularisation methodology to achieve an embedding space of evenly distributed samples and to increase the generalisation ability of the model compared to solely distance-based learning. Comprehensive experiments on four publicly available datasets for ReID and FSC, demonstrate consistent improvement against previously proposed metric learning loss functions. Particularly, our experiments show up to 5% improvement in ReID settings, up to 13% in FSC settings and up to 6% improvement against other state-of-art regularisers.
Modulation recognition of optical and electromagnetic waves based on transfer learning

Light is a type of electromagnetic wave. Research on the modulation recognition of communication signals, which are also electromagnetic waves, can be applied to the field of modulation and demodulation of optical waves. The limited signal data and varying data distribution have an impact on the automatic modulation recognition of signals. To address this, we proposed a method to enhance the characteristic information of modulation patterns carried by signal data. We used the Wigner–Ville distribution and short-time Fourier transform to convert the original I/Q multi-channel modulated communication signal into time–frequency representation images (TFRIs). Additionally, we adopted a transfer learning approach, the domain adaptation network (DAN), to identify modulation modes of signals under different SNRs. The combination of signal conversion and transfer learning significantly improved the identification of modulation recognition. Our experiments demonstrated that our proposed method outperformed existing state-of-the-art methods in terms of classification accuracy at both high and low signal-to-noise ratios (SNRs). Specifically, our method achieved a 10% increase in Top1-accuracy under high SNR, and overall improved the classification accuracy under low SNR compared to the adversarial transfer learning architecture (ATLA). This is attributed to the use of a more diverse and accurate dataset that provides a better representation of real-world communication signals. We also compared our experimental results with those of other papers, and the results show that our method achieved the best performance among the current state-of-the-art methods, considering the classification accuracy at different SNRs.
Static vulnerability detection based on class separation

Software vulnerability detection is a key step to prevent the system from being attacked. However, tens of thousands of codes have brought great challenges to engineers, so we urgently need an automatic and intelligent vulnerability detection method. The existing vulnerability detection model based on deep learning has the problem that it is difficult to separate the features of vulnerable and neutral code. Based on the code data drive, this paper proposes a static vulnerability detection method SDV(Statically Detecting Vulnerability) for C∖C++ programs. SDV is a function-level vulnerability code detection method. This paper uses a code property graph to represent the code and decouples the feature extractor and the classifier. In the graph feature extraction stage, we use Jump Graph Attention Network layers and convolutional pooling layers. Their combination can not only prevent the over-smoothing problem but also separate the sample classes deeply. Finally, on the chrdeb dataset, SDV outperforms state-of-the-art function-level vulnerability detection methods by 52.3%, 15.9%, and 39.6% in Precision, Recall, and F1-Score, respectively. On the real project sard, the number of vulnerabilities detected by SDV is 10.7 times more than Reveal.
How “What you think you know about cybersecurity” can help users make more secure decisions

The increasing use of information technology artifacts in daily life makes security a shared responsibility of both users and companies. In recent years, increasing a user's objective (i.e., actual) security knowledge and providing applications with more secure default settings appear among the most ubiquitous tools companies use to broaden their efforts to help users make more secure decisions. Examining both solutions matters because they are widely used, cost effective, and understood by many security practitioners. Additionally, default settings and users' objective knowledge provide anchors for decision-making. However, human errors and insecure default settings are increasing and raising further questions about the efficacy of such efforts. Using the theory of bounded rationality, we investigated the role of objective, subjective (i.e., self-assessed) security knowledge, and default settings security level on the overall decision security. We found that objective security knowledge can lead to secure decisions when paired with high subjective security knowledge. In the absence of the latter, objective security knowledge is unable to lead to better security decisions. Furthermore, subjective security knowledge reduces the extent to which users fully accept default security settings, thereby mitigating bias toward insecure default settings.
Proceedings of the 38th Annual Conference of The Society for Organic Petrology: Organics in the geological cycle

• Nine articles are reviewed for the VSI of the 38th Annual Conference of The Society for Organic Petrology
Phrasal Proper Names in German and Norwegian

Abstract This paper discusses the morpho-syntax of phrasal proper names like Deutsche Bahn ‘German Railway’ and Norske Skog ‘Norwegian Forest’ in German and Norwegian. As regards determiner elements, there are three types of phrasal proper names in German: some proper names do not have a definite article, some do, and yet others exhibit a possessive. Depending on the syntactic context, the first two types pattern the same as regards the presence or absence of the article but contrast with the third, where the possessive is always present. It is proposed that proper names in German vary in their structure as regards the presence of the DP-level: unlike articles, possessives have a referential marker, and a DP is obligatorily projected with the latter element. Norwegian is different. While proper names in Norwegian also vary in the presence or absence of determiners, there is no flexibility—determiners are always present or always absent, independent of the syntactic context. It is proposed that unlike in German, the DP-level in Norwegian is always present. As argued by Roehrs (Glossa J Gen Linguist, 5(1):1–38, 2020, https://doi.org/10.5334/gjgl.1267 ), phrasal proper names involve a regular syntactic derivation. Given that elements of regular DPs are sensitive to definiteness in Norwegian, it is proposed that Norwegian proper names involve an obligatory definiteness feature. As this feature surfaces in the DP-level, the latter must be present in that language in all instances. Besides this cross-linguistic difference, we document that phrasal PN may show features of recursivity evidenced most clearly in Norwegian.
Corrigendum to “l-LIPs of codes over finite chain rings” [Discrete Math. 345 (12) (2022) 113087]

In this note, we first give an example to illustrate that the Lemma 2.22 in [8] is not correct. Then we prove that a special case of the Lemma 2.22 is correct. This result ensures that the subsequent conclusions in [8] are correct (except for a minor modification to the conditions of Theorem 3.3).
Security‐based code smell definition, detection, and impact quantification in Android

Abstract Android's high market share and extensive functionality make its security a significant concern. Research reveals that many security issues are caused by insecure coding practices. As a poor design indicator, code smell threatens the safety and quality assurance of Android applications (apps). Although previous works revealed specific problems associated with code smells, the field still lacks research reflecting Android features. Moreover, the cost and time limit developers to repairing numerous smells timely. We conducted a study, including Def inition, D etection, and I mpact Q uantification for Android code smell (DefDIQ): (1) define 15 novel code smells in Android from a security programming perspective and provide suggestions on how to eliminate or mitigate them; (2) implement DACS (Detect Android Code Smell) to automatically detect the custom code smells based on ASTs; (3) investigate the correlation between individual smells with DACS detection results, select suitable code smells to construct fault counting models, then quantify their impact on quality, and thereby generating code smell repair priorities. We conducted experiments on 4575 open‐source apps, and the findings are: (i) Lin's CCC between DACS and manual detection results reaches 0.9994, verifying the validity; (ii) the fault counting model constructed by zero‐inflated negative binomial is superior to negative binomial (AIC = 517.32, BIC = 522.12); some smells do indicate fault‐proneness, and we identify such avoidable poor designs; (iii) different code smells have different levels of importance and the repair priorities constructed provide a practical guideline for researchers and inexperienced developers.
An entropy-based weighted dissimilarity metric for numerical data clustering using the distribution of intra feature differences

Suitable selection of a proximity measure is one of the fundamental requirements of clustering. With conventional (dis)similarity measures, many clustering algorithms do not yield satisfactory results on complex high-dimensional datasets. The problem lies with widely varying distributions along each feature which existing (dis)similarity measures fail to capture. In this work, we study the distribution of all-pair absolute distances in some standard real datasets over the feature space and observe that most of them have values near to zero. The frequency of data pairs decreases with the increasing value of distance which suggests an exponential distribution of values along features. The exponential decay rate constant, termed as characteristic length indicates the inhomogeneity in a feature and therefore, we use it as a weighting factor across attributes. The dissimilarity for a pair of data points is computed by considering the weights of each attribute along with a continuum adaptation of Boltzmann’s notion of entropy which uses feature-wise absolute differences as input. We prove that the proposed measure is a metric. For experimental analysis, we compare different proximity measures with the proposed, in terms of clustering results. The combination of feature wise characteristic length and the continuous version of Boltzmann’s entropy proves its excellence in terms of clustering results on diversified synthetic, real, and gene expression datasets.
A subgraph sampling method for training large-scale graph convolutional network

Graph Convolutional Network (GCN) is a powerful model for graph representation learning. Since GCN updates nodes with a recursive neighbor aggregation scheme, training GCN on large-scale graphs suffers from enormous computational cost and large memory requirement. The subgraph sampling method trains GCN on sampled small-scale subgraphs to speed up GCN. However, they also suffer from problems, such as training GCN on unconnected and scale-unbalanced subgraphs, which reduce performance and efficiency. Moreover, existing subgraph sampling methods train GCN on subgraphs independently and ignore the relation information among different subgraphs. This paper proposes a novel subgraph sampling method, Improved Adaptive Neighbor Sampling (IANS), and a novel loss function, Subgraph Contrastive Loss. Subgraphs sampled by the IANS method are scale-balanced, inside nodes are significantly relevant, and the sample ratio controls the sparsity of subgraphs. To recover the lost relation information between different subgraphs, the Subgraph Contrastive Loss is defined, which constrains the initially connected nodes in different subgraphs to be closer and pushes unconnected nodes far away in feature space. A series of experiments are conducted, which train GCN with IANS and Subgraph Contrastive Loss for node classification on three different scale datasets. The training time and classification accuracy demonstrate the effectiveness of the proposed method.
Driver's visual fixation attention prediction in dynamic scenes using hybrid neural networks

The traffic driving scenario is dynamically varying and experienced drivers can allocate visual attention effectively in brief time intervals, focusing on prominent targets and areas in advance, thus ensuring safe driving. Therefore, it is significant to research the behavior of driver's visual attention allocation in the development of driver assistance systems and autonomous vehicles. Recently, inspired by top-down and bottom-up attention mechanisms, most visual attention models based on neural networks have been proposed. However, these models tend to yield highly distributed predictions for the specific driving scenario, and have relatively low prediction accuracy, making them unfeasible for being used in practice. Hence, to overcome these challenges, we propose a driver's visual attention model (DVAM) built on the encoder-decoder architecture with the utilization of the convolutional neural network (CNN) and the recurrent neural networks (RNN). Specifically, the model leverages the temporal and spatial dimension information to better mimic realistic dynamic driving, and extract abundant feature information, thus ensuring that the final predictions are authentic and effective. Then, extensive experiments demonstrate that our suggested DVAM achieves better robustness and precision in predicting driver attention to areas or targets compared to state-of-the-art methods on the DR(eye)VE dataset. Finally, our model is deployed on the TDV dataset to validate its generalization capabilities, which prove the potential for field adaptation.
RASP: Regularization-based Amplitude Saliency Pruning

Due to the prevalent data-dependent nature of existing pruning criteria, norm criteria with data independence play a crucial role in filter pruning criteria, providing promising prospects for deploying deep neural networks on resource-constrained devices. However, norm criteria based on amplitude measurements have long posed challenges in terms of theoretical feasibility. Existing methods rely on data-derived information such as derivatives to establish reasonable pruning standards. Nonetheless, achieving quantitative analysis of the "smaller-norm-less-important" notion remains elusive within the norm criterion context. To address the need for data independence and theoretical feasibility, we conducted saliency analysis on filters and proposed a regularization-based amplitude saliency pruning criterion (RASP). This amplitude saliency not only attains data independence but also establishes norm criteria for usage guidelines. Furthermore, we further investigated the amplitude saliency, addressing the issues of data dependency in model evaluation and inter-class filter selection. We introduced model saliency and an adaptive parameter group lasso (AGL) regularization approach sensitive to different layers. Theoretically, we thoroughly analyzed the feasibility of amplitude saliency and employed quantitative saliency analysis to validate the advantages of our method over previous approaches. Experimentally, conducted on the CIFAR-10 and ImageNet image classification benchmarks, we extensively validated the improved top-level performance of our method compared to previous methods. Even when the pruned model has the same or even smaller number of FLOP, our method can achieve equivalent or higher model accuracy. Notably, in our ImageNet experiment, RASP achieved a 51.9% reduction in FLOPs while maintaining an accuracy of 76.19% on ResNet-50.
MAGD: Minimal Attack Graph Generation Dynamically in Cyber Security

Cyber security faces challenges in detecting and mitigating complex attacks. Security solutions have employed Attack Graphs (AGs) for modeling multi-stage attacks, but traditional AGs suffer from scalability issues and may miss new vulnerabilities and attack paths. Also, traditional AGs construct the graph using information about previously known attacks. In this paper, we propose Minimal Attack Graph Generation Dynamically (MAGD), which leverages data from a deception system based on Honeypots to generate a minimal AG dynamically. In this paper, the AG has been constructed from real-time attacker’s behavior data directly. In addition, MAGD specifically focuses on modeling the attacker’s behavior at the host level, in contrast to traditional network-based AGs that encompass all possible attack paths at the network level. MAGD contains three custom algorithms to construct attacker behavior, generate a minimal AG, and continuously update the graph with new attack information. Complexity analyses demonstrate that MAGD’s generation process can accomplish within polynomial time. Our approach offers several advantages over traditional AGs, including the ability to model attackers’ real-time behavior, construct attackers’ action paths in the target host, and detect new vulnerabilities and attack paths in the victim host. Also, MAGD includes information about the effects of the actions in the target system. This information can be used for other security purposes. We demonstrate MAGD’s efficacy through a case study. MAGD provides a more effective way to detect and mitigate cyber threats by utilizing Honeypot data and proposed algorithms.