Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

A Linked List Encryption Scheme for Image Steganography without Embedding

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics

This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].

Teaching Reform and Practice of Embedded System Design Based on Outcome-Based Education

Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.

Design and test of soft-pot-tray automatic embedding system for light-economical pot seedling nursery machine

Most of the commercially-available pot seedling nursery machines are incompatible with soft-pot-trays and are labor-intensive and low in productivity.A soft-pot-tray automatic embedding system was developed in this study to achieve automatic embedding of the soft pot tray into the hard tray following sowing and covering with soil.A control system was constructed using the Arduino program development environment.An embedded-hard-tray automatic lowering mechanism and conveyor-belt-based pot-tray embedding system were designed.Dynamics analysis was conducted to derive an equation to describe the embedding process of the soft pot tray into the embedded hard tray.A prototype of the soft-pot-tray automatic embedding system was manufactured and tested.The analytical equation suggested that a minimum linear velocity of 0.86 m/s was required for a complete embedding process.The experimental results showed that the embedded-hard-tray automatic lowering mechanism was reliable and stable as the tray placement success rate was greater than 99%.The successful tray embedding rate was 100%and the seed exposure rate was less than 1%with a linear velocity of the conveyor belt of 0.92 m/s.The experiment findings agreed well with the analytical results.The proposed soft-pot-tray automatic embedding system satisfied the technical specifications for a light-economical pot seedling nursery machine.

NUMERICALLY SOLVING PERIODICALLY PERTURBED CONSERVATIVE SYSTEMS BY PARAMETER EMBEDDING METHODS

The parameter embedding method is applied for numerically solving the perturbed conservative systems. By means of Newtonian iteration, a simple algorithm has been constructed. Finally, the convergence of the iteration is proved.

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

An Automated System to Predict Popular Cybersecurity News Using Document Embeddings

The substantial competition among the news industries puts editors under the pressure of posting news articleswhich are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teamsin making decisions about posting a news article. Article similarity extracted from the articles posted within a smallperiod of time is found to be a useful feature in existing popularity prediction approaches. This work proposesa new approach to estimate the popularity of news articles by adding semantics in the article similarity basedapproach of popularity estimation. A semantically enriched model is proposed which estimates news popularity bymeasuring cosine similarity between document embeddings of the news articles. Word2vec model has been used togenerate distributed representations of the news content. In this work, we define popularity as the number of timesa news article is posted on different websites. We collect data from different websites that post news concerning thedomain of cybersecurity and estimate the popularity of cybersecurity news. The proposed approach is comparedwith different models and it is shown that it outperforms the other models.

Effectiveness and safety of acupoint catgut embedding combined with Chinese herbal medicine in chronic urticaria: A systematic review of randomized controlled trials

Objective:To evaluate the effectiveness and safety of acupoint catgut embedding(ACE)in combination with Chinese herbal medicine(CHM)in treating chronic urticaria(CU).Methods:We thoroughly searched Embase,PubMed,Cochrane Library,Web of Science,China National Knowledge Infrastructure(CNKI),Wangfang database,Chinese Scientific Journal Database(VIP),and Chinese biomedical literature database(SinoMed)for relevant studies from inception until May 2022.Randomized controlled trials(RCTs)on ACE combined with CHM for CU were included.Literature search,data extraction,and risk of bias assessment were independently conducted by two authors.Results:A total of 15 RCTs with 1065 participants were included in this review.Five trials reported that the combined therapy showed a higher total effective rate,and four trials reported that the combined therapy was associated with a lower level of serum immunoglobulin E.Furthermore,two,four,and four trials reported that the combined therapy was more effective in reducing itching degree,size,and number of wheals,respectively.The combined therapy was reported to be associated with a lower recurrence rate in three trials,and with a fewer adverse reaction rate in two trials.Conclusions:ACE in combination with CHM appears to be a safe and effective therapy for patients with CU.Given the relatively low quality of the included trials,these findings should be interpreted cautiously.Further high-quality RCTs are needed to confirm our findings.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

Logformer: Cascaded Transformer for System Log Anomaly Detection

Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points.These logs are valuable for analyzing performance issues and understanding the status of the system.Anomaly detection plays an important role in service management and system maintenance,and guarantees the reliability and security of online systems.Logs are universal semi-structured data,which causes difficulties for traditional manual detection and pattern-matching algorithms.While some deep learning algorithms utilize neural networks to detect anomalies,these approaches have an over-reliance on manually designed features,resulting in the effectiveness of anomaly detection depending on the quality of the features.At the same time,the aforementioned methods ignore the underlying contextual information present in adjacent log entries.We propose a novel model called Logformer with two cascaded transformer-based heads to capture latent contextual information from adjacent log entries,and leverage pre-trained embeddings based on logs to improve the representation of the embedding space.The proposed model achieves comparable results on HDFS and BGL datasets in terms of metric accuracy,recall and F1-score.Moreover,the consistent rise in F1-score proves that the representation of the embedding spacewith pre-trained embeddings is closer to the semantic information of the log.

Heterogeneous Network Embedding: A Survey

Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE.

Selection of Optimal Embedding Parameters Applied to Short and Noisy Time Series from Rossler System

Throughout scientific research, the state space reconstruction that embeds a non-linear time series is the first and necessary step for characterizing and predicting the behavior of a complex system. This requires to choose appropriate values of time delay T and embedding dimension dE. Three methods are applied and discussed on nonlinear time series provided by the Rössler attractor equations set: Cao’s method, the C-C method developed by Kim et al. and the C-C-1 method developed by Cai et al. A way to fix a parameter necessary to implement the last method is given. Focus has been put on small size and/or noisy time series. The reconstruction quality is measured by using a criterion based on the transformation smoothness.