Mapping Rules Research Articles

Chaos key update using constellation selection and clustering for data centers

Key updating in data centers (DCs) is essential, but it presents challenges related to the consumption of channel resources. To address this issue, we propose a chaos key update scheme using constellation selection and clustering for DCs. At the transmitter side (DC1), the keys to be transmitted are encrypted by generating a digital chaotic sequence using a 4D hyperchaotic system to obtain encryption keys (EK). Then, different constellation mapping rules in the constellation mapping space are used to embed the EK. On the receiving side (DC2), the constellation mapping rules are judged by the mean shift clustering algorithm to assist the extraction of EK, which successfully improves the accuracy of the judgment. These results show that this scheme can safely transmit 70.59 Gb/s power division multiplexing-based discrete multi-tone (PDM-DMT) signal and 1.17 Gb/s error-free key over a 25 km fiber. This scheme realizes the flexible update of the key and enhances the ability to resist potential attacks, thus providing a reliable scheme for the physical layer key update.

Robust encoding of stimulus–response mapping by neurons in visual cortex

Neural activity in sensory cortex is modulated by behavioral and cognitive factors, and this modulation is thought to contribute to the selection of specific sensory information needed to achieve behavioral goals. In contrast, more abstract behavioral variables that are independent of stimulus selection, such as stimulus-response mapping, are thought to be encoded by neurons outside of sensory cortex. We show that information about such mapping is robustly encoded in the responses of neurons in primate visual cortex. Monkeys were trained to alternate between two tasks that differed in the rule governing the mapping of a remembered visual cue onto an eye movement response. During the memory-delay period, neurons in area V4 reliably signaled the remembered cue location in both tasks. However, the encoding of cue location depended critically on the stimulus-response mapping rule. Thus, V4 delay activity encoded the mapping rule and signaled the preparation of the appropriate motor response rather than spatial working memory per se, contrary to previous assumptions. In addition, we probed the origins of motor-related delay activity and found that it was reduced during local inactivation of the frontal eye field (FEF). The results demonstrate that behavioral modulation of visual cortical activity is not solely related to the selection of sensory stimuli but instead reflects a distinct mechanism for sensory-guided motor output.

A framework for coordinating energy prosumers through multi-energy sharing and carbon responsibility sharing.

In the emerging energy-sharing market, prosumers enhance resource allocation and promote low-carbon transitions through energy trade. Implicit carbon transfers in energy sharing necessitate scientific carbon responsibility allocation to guide prosumers' decisions in integrated electricity, heat, and carbon markets. To coordinate growing multi-prosumers, and stimulate multi-energy sharing and equitable carbon responsibility allocation, an innovative framework for joint multi-energy and carbon responsibility sharing is designed to enhance local energy transaction, carbon emission management, and mutual benefits under the guiding principles of individual rationality, low-carbon orientation, transparency, and scalability. Supported by the proposed mapping rules and tailored algorithms, multi-energy and carbon responsibilities are efficiently shared among multi-stakeholders. Numerical analysis confirms the model's effectiveness in improving economic and environmental outcomes, promoting low-carbon energy use, and prioritizing low-carbon participants. This research shifts prosumer development modes from individualization to group collaboration, advancing energy systems toward shared carbon responsibilities across supply and demand sides.

Orthographic-phonological mapping impairments in Chinese children with developmental dyslexia: insights from an ERP investigation.

In the realm of logographic writing systems, such as Chinese characters, orthographic transparency fundamentally differs from alphabetic languages, posing unique challenges for individuals with developmental dyslexia (DD). This study employed event-related potentials (ERPs) and a masked priming paradigm to investigate how Chinese children with DD compared to typically developing (TD) children in their utilization of orthographic-phonological mapping rules during the processing of pseudocharacters. The findings revealed noteworthy distinctions between TD and DD children. TD children exhibited a robust priming effect in radical priming, characterized by an enhanced N170 (100-200ms) amplitude and a reduced P200 (200-350ms) amplitude, whereas DD children did not display this differentiation. This observation parallels the difficulties faced by DD children in alphabetic languages. Furthermore, the study found a significant positive correlation between the N170 amplitude in the left posterior brain region of Chinese DD children and their orthographic performance: DD children with poorer orthographic awareness exhibited larger N170 amplitudes in this region. The present study sheds light on the challenges Chinese DD children encounter in processing regular sub-character routes, particularly evident in the early stages of orthographic processing. The orthographic deficits of DD children hinder their processing of Chinese orthography, resulting in increased cognitive demands.

A Robust joint coverless image steganography scheme based on two independent modules

With the development of deep learning technology, great progress has been made in the field of coverless steganography based on deep learning technology, including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods, however both of which have their limitations. The former is difficult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm. Due to the irreversibility of the process of generating secret images from message codeword, the recovery accuracy of the latter is very poor. To this end, this paper designs a robust joint coverless image steganography scheme called Joint Coverless Image Steganography (JoCS). Firstly, this paper proposes the Semantic Factorization Fitting module (SeFF) and the Transform Domain Steganography module (TrDS). The former adds the secret message to the input vector of the low resolution layer in the StyleGAN generator network, which establishs a mapping rule between message codeword and the coarse feature of the generated image, and then the extractor is used to fit the above mapping rule, which has excellent robustness and completeness; the latter encodes the main content area of the image based on the encoder in VQGAN, and then adds secret message to the latent vector of the encoded image, which achieves the steganography in the latent domain of the image. Secondly, we demonstrate the independence between two modules and the advantages of connecting two modules. By using the image generated in the SeFF module as the cover image in the TrDS module, secondary steganography of a single image is achieved, based on which we design the JoCS scheme. The results show that our scheme breaks through the communication capacity limit in the selection-based coverless methods while guaranteeing 100% completeness, excellent image quality and outstanding robustness against various image attacks. Moreover, our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication. Finally, this paper also discusses the following three points as further elaboration of the scheme: (1) the advantages of the mapping rule in the SeFF module (2) the verification of the independence between the two modules (3) the flexibility of the joint steganography scheme.

AADL architecture model construction based on FACE mode

In the development of time-sensitive, mission-critical aerospace software systems, the modeling technology that combines the FACE(future airborne capability environment) open architecture model with the AADL(architecture analysis & design language) architectural model is gradually becoming a research hot spot. This paper proposes an automatic construction method for AADL models based on the FACE model. Firstly, the formal definition of the syntactic structure of the FACE model is given using context-independent grammar. Secondly, the 26 kinds of mapping rules from FACE model elements to AADL model elements are designed, and the rationality of the mapping rules is proved by the graph homomorphism theory. Then, the StringTemplate template is designed based on the mapping rules, and the template engine algorithm for generating AADL model is given. Finally, a total of 12 kinds of FACE models elements in four aspects are taken as experimental objects, 241 FACE model instances are designed as test cases, and the generated AADL models are verified in the OSATE tool. The method in this paper has been applied in the aviation field. The results can avoid repeated modeling work in the preliminary design stage and the detailed design stage.

A Coverless Image Steganography Based on Huffman Encoding and Robust Image Wavelet Hashing

Data hiding approaches are crucial in insecure communication to protect sensi- tive information from illegal access. The robustness of existing coverless image steganogra- phy methods that utilize mapping rules is derived from their ability to extract feature patterns within the spatial domain. In order to protect sensitive data more securely and to increase its durability against attacks, a new coverless steganography is introduced in this study. The me- thod used is based on the frequency domain. A coverless image steganography based on ro- bust image wavelet hashing and Huffman encoding. Initially, the confidential data is encoded through lossless compression using Huffman Encoding, which reduces the payload and en- hances the capacity for embedding. Second, segments of an 8-bit size are created from the compressed secret data. The efficient DWT hashing technique has been employed to generate a hash sequence for an image. An inverted index structure is established, allowing for the se- lection of the image whose hash corresponds to the secret data segment. Subsequently, all chosen images along with supplementary information are transmitted to the recipient. Testing has shown that the proposed method is robust against various image processing attacks, in- cluding scaling, low pass filtering, JPEG compression, rotation, noise introduction, and me- dian/mean filtering. Research findings and analysis indicate that this method enhances the latest coverless steganography algorithms in terms of capacity, resilience, and security.

Integrated Analysis of Utilization Efficiency of Intelligent Sports Venues Based on Data Mining Algorithms

With the rapid development of information technology and the increasing prosperity of the sports industry, the construction and management of intelligent sports venues have become an important way to improve the efficiency of sports resource utilization and meet diverse fitness needs. This paper focuses on the integrated analysis of the utilization efficiency of intelligent sports venues based on data mining algorithms. Aiming to reveal the inherent laws and potential value in the operation of sports venues through advanced data processing and analysis techniques, and provide a scientific basis for optimizing resource allocation and improving service quality. Intelligent sports venues utilize modern information technologies such as the Internet of Things, big data, and cloud computing to achieve comprehensive and intelligent monitoring and management of venue facilities, personnel activities, environmental conditions, and more. This new management model not only improves the operational efficiency of the venue but also enhances the user experience and meets personalized and diverse fitness needs. This paper focuses on the current situation of the opening of university sports venues to the outside world, aiming to reveal the pain points and bottlenecks in their operation process through detailed investigation and analysis. This study introduces advanced concepts of middleware architecture and constructs a multi-level data integration framework that includes local models, global models, user models, and mapping rules. This framework can effectively integrate distributed heterogeneous data sources, achieving seamless integration and efficient interaction of data. At the same time, with the help of distributed programming technology in cloud computing, it is possible to conduct in-depth mining and analysis of integrated massive data, discover potential value, and provide a scientific basis for decision support.

Reconstruction of an execution architecture view by identifying mapping rules for connectors

Reconstruction of an execution architecture view by identifying mapping rules for connectors

Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement

Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

Reengineering Web applications into pervasive information systems

In today’s mobile-driven world, organizations face the challenge of adapting legacy Web applications to pervasive information systems, such as mobile applications, ensuring seamless accessibility across devices. This paper presents a systematic, three-phase approach to reengineering Web applications into mobile applications: Reverse-engineering, Transformation, and Forward-engineering. In the reverse-engineering phase, high-level conceptual models of the Web application are generated to capture its architecture and functionality. During the transformation phase, these models are refined using Unified Modeling Language (UML) profiles to align with the requirements of mobile systems. The forward-engineering phase utilizes the Acceleo technology, along with predefined mapping rules and code patterns, to automate the generation of mobile application code. A case study is conducted to demonstrate the effectiveness of this framework, revealing a significant reduction in manual effort throughout the reengineering process. This approach offers a scalable and efficient solution for transforming Web applications into mobile applications, contributing to advancements in model-driven engineering and software reengineering.

An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system

An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system

A robust coverless image-synthesized video steganography based on asymmetric structure

A robust coverless image-synthesized video steganography based on asymmetric structure

Multifractal Properties of Human Chromosome Sequences

The intricacy and fractal properties of human DNA sequences are examined in this work. The core of this study is to discern whether complete DNA sequences present distinct complexity and fractal attributes compared with sequences containing exclusively exon regions. In this regard, the entire base pair sequences of DNA are extracted from the NCBI (National Center for Biotechnology Information) database. In order to create a time series representation for the base pair sequence {G,C,T,A}, we use the Chaos Game Representation (CGR) approach and a mapping rule f, which enables us to apply the metric known as the Complexity–Entropy Plane (CEP) and multifractal detrended fluctuation analysis (MF-DFA). To carry out our investigation, we divided human DNA into two groups: the first is composed of the 24 chromosomes, which comprises all the base pairs that form the DNA sequence, and another group that also includes the 24 chromosomes, but the DNA sequences rely only on the exons’ presence. The results show that both sets provide fractal patterns in their structure, as obtained by the CGR approach. Complete DNA sequences show a sharper visual fractal pattern than sequences composed only of exons. Moreover, the sequences occupy distinct areas of the complexity–entropy plane, and the complete DNA sequences lead to greater statistical complexity and lower entropy than the exon sequences. Also, we observed that different fractal parameters between chromosomes indicate diversity in genomic sequences. All these results occur in different scales for all chromosomes.

A Robust Coverless Image Steganography Algorithm Based on Image Retrieval with SURF Features

With the advancement of image steganography, coverless image steganography has gained widespread attention due to its ability to hide information without modifying the carrier of images. However, existing coverless image steganography methods often require both communicating parties to transmit an amount of additional information including image blocks’ locations or a large number of parameters, which will raise a serious suspicion. In light of this issue, we propose a robust coverless image steganography algorithm based on Speeded-Up Robust Features (SURF). Firstly, the proposed method allows both communicating parties to independently create multiple coverless image datasets (CIDs) using random seeds. Then, a mapping rule is designed for creating one-to-one correspondence between hash sequences and images in CIDs. Finally, the secret information will be carried by the images whose hash sequences are equal to the secret segments. At the receiver side, the robust SURF of images is utilized to retrieve the secret information. Experimental results demonstrate that the proposed algorithm outperforms other methods in terms of capacity, robustness, and security. Furthermore, it is worth noting that the proposed method eliminates the need to transmit a large amount of additional information, which is a significant security issue in existing coverless image steganography algorithms.

A high-capacity Coverless Image Steganography Based on OMR and Mapping Rules

Securing private information during its transmission over the internet is of paramount importance, as it safeguards against unauthorized access and guarantees the integrity of data. Steganography serves as a technique for enhancing security by concealing sensitive information within digital content such as images, videos, audio, and textual carriers, thus thwarting unauthorized interception. Current methodologies involve manipulating pixels to create stego-images, effectively embedding confidential messages within the image structure. However, this approach has led to steganalysis experts uncovering these hidden messages. To address this issue, an innovative strategy for covert data concealment is proposed. This paper proposes a robust, secure, and embedding capacity coverless image steganography method using OMR and Mapping Rules. The secret information is represented by a binary string, and the bubble sheet is used to represent binary fragments. This approach ensures the confidentiality of sensitive data communicated over the internet. Notably, this approach surpasses previous alternatives in terms of embedding capacity, as substantiated by result analysis. Furthermore, it is adaptable for refinement as needed. Significantly, it exhibits remarkable resilience, safeguarding against a spectrum of attacks including scaling, color space conversion, JPEG compression, thresholding, "salt and pepper" noise, file format conversion, and steganalysis tools, among others.

A Possibility Evaluation Model for Road Transportation of Hazardous Chemicals Based on Bow-tie Theory and Bayesian Model

This paper collected 1115 road transportation accidents of hazardous chemicals in China from 2013 to 2021, and constructed a total of 120 accident chains, among which the most common accident chain was "impact → rear-end collision → fuel tank damage → leakage", with a total of 195 times. Based on the analysis of historical accident data, the occurrence mechanism of road transportation accidents of hazardous chemicals was discussed. Based on the bow-tie theory, the bow-tie diagram was established to realize the construction of qualitative reasoning bow-tie diagram for multi-disaster coupling accidents in road transportation of hazardous chemicals. The mapping rules between Bow-tie model and Bayesian network were established, and the structure of Bayesian network for road transportation of hazardous chemicals was constructed. The prior probability and conditional probability of Bayesian network nodes are determined by multi-source data, and the real-time accident probability evaluation is realized by combining quantitative and qualitative methods. Finally, a real time evaluation of the possibility of multi-disaster coupling accidents is realized every 30 seconds through a numerical example, which reflects the influence law of multi-disaster coupling scenarios on risks.

Zero-Shot Construction of Chinese Medical Knowledge Graph with GPT-3.5-turbo and GPT-4

Knowledge graphs have revolutionized the organization and retrieval of real-world knowledge, prompting interest in automatic NLP-based approaches for extracting medical knowledge from texts. However, the availability of high-quality Chinese medical knowledge remains limited, posing challenges for constructing Chinese medical knowledge graphs. As LLMs like ChatGPT show promise in zero-shot learning for many NLP downstream tasks, their potential on constructing Chinese medical knowledge graphs is still uncertain. In this study, we create a Chinese medical knowledge graph by manually annotating textual data and using ChatGPT to automatically generate the graph. We refine the results using filtering and mapping rules to align with our schema. The manually generated graph serves as the ground truth for evaluation, and we explore different methods to enhance its accuracy through knowledge graph completion techniques. As a result, we emphasize the potential of employing ChatGPT for automated knowledge graph construction within the Chinese medical domain. While ChatGPT successfully identifies a larger number of entities, further enhancements are required to improve its performance in extracting more qualified relations.

Mapping-varied modulation with labeling optimization for secure transmission in mode-division-multiplexed fiber-optic systems

To improve the physical-layer security of mode-division multiplexing (MDM) systems, a simple security scheme named mapping-varied modulation (MVM) is proposed in this paper by combining cryptographic and information-theoretic security. Specifically, on top of the information-theoretic security provided by the less-conditioned wiretap channel due to the larger mode-dependent loss induced by fiber-bend tapping, the proposed MVM security method varies the mapping rules of the adopted constellations for the subchannels (one subchannel corresponds to one mode) by using the inherently time-varying random channel state information (CSI) of the MDM fiber, under the assumption that an eavesdropper does not know the exact instantaneous CSI of the legitimate link. To maximize the difference among the binary labels of the constellation points in the same position for each subchannel, a labeling optimization method is proposed as well. Numerical results demonstrate the effectiveness of the proposed MVM method via bit-error ratio performance and secrecy rate, showing a potential way to improve the security of the MDM link for high-speed data transmission.