Symmetric Technique Research Articles

Computational Framework for Target Tracking Information Fusion Problems

In this work, we propose computationally tractable techniques for extracting valuable information from diverse data sources collected by multiple sensors in a variety of formats (visual, sonar, quantitative, qualitative, social information, etc.). More specifically, we develop an integrated approach consisting of two algorithms for extracting information and achieving a consensus-based, robust solution. The first algorithm extracts solutions from sensors within each data source, whereas the second algorithm reaches a compromise among the generated solutions from the previous algorithm across all data sources. To accomplish these goals, we initially transform the multisensor multitarget tracking problem (MSMTT) problem into a multidimensional assignment problem. Subsequently, we introduce a decomposition-based multisensor recursive approach referred to as a revised multisensor recursive algorithm, which can efficiently deliver a robust solution for each single data source MSMTT problem. In the second algorithm, we extend our methodology to the multisource MSMTT problem by introducing a connection-based symmetric nonnegative matrix factorization technique, which is shown to be computationally feasible and efficient in obtaining high-quality solutions. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the Army Research Laboratory [Grant G00006831]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0016 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0016 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Explanatory and predictive modeling of cybersecurity behaviors using protection motivation theory

ContextProtection motivation theory (PMT) is the most frequently used theory in understanding cyber security behaviors. However, most studies have used a cross-sectional design with symmetrical analysis techniques such as structure equation modeling (SEM) and regression. A data-driven approach, such as predictive modeling, is lacking and can potentially evaluate and validate the predictive power of PMT for cybersecurity behaviors. ObjectiveThe objective of this study is to assess the explanatory and predictive power of PMT for cyber security behaviors related to computers and smartphone. MethodAn online survey was employed to collect data from 1027 participants. The relationship of security behaviors with threat appraisal (severity and vulnerability) and coping appraisal (response efficacy, self-efficacy and response cost) components were tested via explanatory and predictive modeling. Explanatory modeling was employed via SEM, whereas three machine learning algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) were used for predictive modeling. Wrapper feature selection was employed to understand the most important factors of PMT in predictive modeling. ResultsThe results revealed that the threat severity from the threat appraisal component of PMT significantly influenced computer security and smartphone security behaviors. From the coping appraisal, response efficacy and self-efficacy significantly influenced computer and smartphone security behaviors. The ML analysis showed that the highest predictive power of PMT for computer security was 76 % and for smartphone security 68 % by KNN algorithm. The wrapper feature selection approach revealed that the most important features in predicting security behaviors are self-efficacy, response efficacy and intention to secure devices. Thus, the findings indicate the complementarity of the cross-sectional and data driven methods.

V2X application server and vehicle centric distribution of commitments for V2V message authentication

Safety applications, such as intersection collision warnings and emergency brake warnings, enhance road safety and traffic efficiency through periodic broadcast messages by vehicles and roadside infrastructure. While the Elliptic Curve Digital Signature Algorithm (ECDSA) is a widely used security approach, its performance limitations make it unsuitable for time-critical safety applications. As such, a symmetric cryptography-based technique called Timed Efficient Stream Loss-tolerant Authentication (TESLA) offers a viable alternative. However, applying standard TESLA in the context of vehicle-to-vehicle (V2V) communications has its own challenges. One challenge is the difficulty of distributing authentication information called commitments in the highly dynamic V2V environment. In this paper, we propose two novel solutions to this problem, namely, V2X Application Server (VAS)-centric and vehicle-centric. The former is an application-level solution that involves selective unicasting of commitments to vehicles by a central server, the VAS, and the latter is a reactive scheme that involves the periodic broadcast of commitments by the vehicles themselves. Extensive simulations are conducted using representatives of the real V2V environment to evaluate the performance of these approaches under different traffic situations; as well as performance comparison with a state-of-the-art distribution solution. The simulation results indicate that the VAS-centric solution is preferable for use in a TESLA-like V2V security scheme. It demonstrates desirable features, including timely delivery of commitments and high distribution efficiency, with over 95 % of commitments sent by the VAS are associated with relevant safety messages when compared with the vehicle-centric and state-of-the-art solutions. Formal security analysis, conducted using the Random Oracle Model (ROM), proves the correctness of our proposed distribution schemes. Additionally, an informal security analysis shows the resilience of the proposed schemes against various attacks, including impersonation, replay, and bogus commitment messages.

A secure and privacy preserving model for healthcare applications based on blockchain-layered architecture

Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. This paper explores the application of blockchain technology-based layered architecture integrated with Interplanetary File System (IPFS) storage to provide a secure and robust sharing platform for healthcare system. A multilayer security is provided in the proposed system including a 128-bit Random Deoxyribonucleic Acid (DNA) encoding sequence with a modified version of the 128-bit Advanced Encryption Standard (AES) key expansion symmetric key technique is being used before storing the records in the IPFS storage, Rivest–Shamir–Adleman (RSA) 2048 digital envelope is used for secure transmission of symmetric keys, RSA 1024 is used for digital signature, and Secure Hash Algorithm 256-bit (SHA-256) is used for hashing the record files. Comparative analysis with existing state-of-the-art in blockchain-based E-healthcare system is discussed. The performance of various assessment metrics is evaluated, including encryption and decryption time for EHR records, mining time, record retrieval time, smart contract execution time and network latency. Additionally, a reputation-based filtering mechanism specifically designed in the proposed model to detect and mitigate the various attacks.

A type II radio burst associated with solar filament–filament interaction

Aims. Solar radio type II bursts are often associated with coronal shocks driven by solar eruptions. In this study, we report a type II burst associated with filament–filament interaction. Methods. Combining the high-quality multiwavelength observations from CHASE, SDO, STEREO, and CALLISTO, we conducted a detailed study of the type II burst associated with filament–filament interaction. Results. On 2023 September 11, an erupting filament (F1) likely disturbed a nearby long filament (F2), causing F2 to subsequently erupt. As a result of possible magnetic reconnection between ejective materials from the two filaments, loop-like structures formed perpendicular to them. Subsequently, the expansion of these loop-like structures triggered a strong coronal mass ejection (CME). Interestingly, a type II burst appeared on the solar spectrum around the time when the loop-like structures formed and the CME appeared above the occulting disk of STEREO/COR1. By converting the frequency of the type II burst to the coronal height using polarization brightness data recorded by the COR1 coronagraph and the spherically symmetric polynomial approximation technique, we determined the formation height of the type II burst to be around 1.45 R⊙, with a speed of approximately 440 km s−1. This is comparable to the observed height of the CME (∼1.43 R⊙), although slightly lower in speed (540 km s−1). Conclusions. All these results indicate that the type II burst was closely associated with filament–filament interactions and was possibly excited by the accompanying CME at the flank. We suggest that the filament–filament interactions played an important role in producing the type II burst by acting as a piston to trigger a strong CME.

Elevating Data Entropy and Efficiency through Symmetric Cryptographic technique Based on Finite State Machine

Elevating Data Entropy and Efficiency through Symmetric Cryptographic technique Based on Finite State Machine

Hybrid Elephant Herding Optimization Approach for Cluster Head Selection And Secure Data Transmission In Wsn Using Hybrid Approach Cryptography Techniques

Introduction: The wireless nature of sensor networks makes safe transfer of data from one node to another a major challenge in communications. Sensing tasks connect these sensor nodes which have limitations of memories and energies. Cryptography techniques are utilised to handle critical issues of security in these networks. The performance of large-scale networks is enhanced in this case by optimisation algorithm mimicking natural behaviours.Methods: This work uses H-EHO (Hybrid Elephant Herding Optimisation technique based on Individual strategies to enhance cluster head selections in WSNs (Wireless Sensor Networks) and thus extend networks’ lifetime. WSNs complete cluster head selection processes, and proposed optimisation approach which selects cluster heads based on tracking of sensor nodes for enhancements. The clan operators of optimisation algorithms are adjusted to handle random walk scale factors of elephants. Clusters of WSNs elect updated sensor nodes in principle. Hybrid algorithm HSR19, a novel security symmetric technique offers greater security during data transfers. It offers integrity, confidentiality, and authentication for cryptographic primary keys. Results: The output of the simulation demonstrates the energy consumption, network longevity, end to end delay, and secure data transfer metrics. The results for choosing an effective and time-efficient cluster head selection process for WSNs are improved by contrasting the two approaches. Conclusion: This comparison also shows the efficiency of communication devices in terms of calculation times for encoding, decoding and energies consumed for various file sizes

An Efficient Image Encryption Scheme for Medical Image Security

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security. The efficacy of the proposed encryption scheme is validated through comprehensive experimental evaluations, which demonstrate its proficiency in ensuring data security while maintaining computational efficiency. The scheme's compatibility with existing medical imaging systems is also examined, affirming its seamless integration into contemporary healthcare infrastructures. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. The primary contributions include the development of a DNA-based encryption algorithm and a novel key management system, both of which significantly enhance the security of medical images. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. By safeguarding the confidentiality and integrity of medical images, the proposed scheme empowers healthcare providers to uphold patient privacy and trust in the digital age. Experimental results show that this approach ensures robust encryption without compromising image quality, making it suitable for sensitive medical imaging applications.

Simultaneous desulfurization and dearomatization of simulated straight-run diesel with novel green DBN-based ionic liquids

Given the surplus of diesel fuel in China and the extensive utilization of renewable energy sources, there is a growing inclination to eliminate organic sulphur and aromatic hydrocarbons from diesel fuel for its application as high-quality ethylene cracking feedstock. In this study, a series of 1,5-diazabicyclo[4.3.0]non-5-ene (DBN)-based ionic liquids with [DBNH]+ as the cation and imidazole as the anion were designed and synthesized for the simultaneous desulfurization and dearomatization of straight-run diesel fuel. Considering factors such as extractant stability, residue content in oil, and separation performance, [DBNH][Tr], exhibiting excellent performance characteristics, was screened as the optimal extractant. Following three-stage cross-flow extraction with an extractant/oil ratio of 3:1 at 30 °C, complete removals of sulfides (benzothiophene) and polycyclic aromatic hydrocarbons (1-methylnaphthalene) were achieved while saturated alkanes reached up to 95.8 wt%, making it suitable for ethylene cracking applications. The interaction mechanisms between ionic liquids and oil components were revealed by atoms in molecules (AIM) topology analysis, independent gradient model based on Hirshfeld partition (IGMH) analysis, and symmetric adaptive perturbation theory energy decomposition techniques, revealing that dispersion-dominated van der Waals (VDW) forces primarily govern interactions due to π-π stacking.

Unveiling the complexity of social commerce continuance intention: a fuzzy set qualitative comparative analysis

PurposeComprehending the determinants influencing the sustained engagement in social commerce assumes paramount importance for its success, particularly within the context of developing countries. This study employs complexity theory to examine the antecedents of the intention toward social commerce continuance.Design/methodology/approachA structured online survey was designed to collect data regarding four behavioural factors: satisfaction, trust, online social support, and continuance intention. The net effect of each construct was evaluated using a symmetrical modelling technique, and to identify the asymmetric effect, the fuzzy-set qualitative comparative analysis was conducted.FindingsThe findings of the structural equation modelling confirmed the importance of satisfaction and online social support for continuance intention. The asymmetric analysis revealed three recipes leading to social commerce continuance intention and three combinations for its absence, contributing to understanding the complexity of online consumer decision-making.Research limitations/implicationsTheoretically, this research highlights the interplay between online social support, trust, satisfaction, and s-commerce continuance intention. Gaining insights into the main factors influencing social media participants' behaviour assists business organizations and policymakers to foster the growth, and the sustainability of the s-commerce.Originality/valueCombining two distinct methodologies, the findings of this paper allow for a comprehensive exploration of the multifaceted dynamics that underlie consumer behaviour and provide actionable insights for businesses seeking to refine their strategies within the dynamic realm of social commerce. Further, the study offers a valuable roadmap for marketers and policymakers to enhance consumer relationships and augment the efficacy of social commerce initiatives.

Simulative analysis of carrier suppressed return to zero based symmetrical compensated optical link

Abstract Optical communication systems provide high data rates to fulfil users’ demands. In addition, the modulation format is essential to the system’s transmission quality and spectral efficiency. Furthermore, the effects of chromatic dispersion, which impair the performance and quality of transmitted signals, are mitigated using dispersion compensation techniques. In optical communication systems, dispersion compensation techniques are essential for preserving signal quality, increasing transmission distances, improving system performance, and offering network designers flexibility. These methods seek to improve signal quality and enable transmission at longer distances by reducing or eliminating the distortion brought on by dispersion. This paper uses a symmetrical compensation technique to analyze the performance of the CSRZ modulation format-based optical communication system. The research is conducted within a communication range of 150–350 km. The system is evaluated in terms of input power, filter order, and filter depth. The results indicate that the proposed system performed better at an input power of 4 dBm using a third-order filter and 60 dB filter depth.

PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRIsimulation. Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equationsusing the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulationdevelopment. The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement betweenmodels. A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRIsimulation.

Performance of GFRP Sheets in Strengthening Concrete Beams in Flexure

Plain concrete beam repair and strengthening are increasingly important in structural strengthening and retrofitting. This study examines the glass fibre-reinforced polymer (GFRP) sheets in plain concrete beams under flexural behaviour by using its patching sheets. Through the implementation of a symmetrical four-point static loading technique, a series of tests were conducted on plain concrete beams that had been externally joined with GFRP sheets. These beams were subjected to rigorous testing until they reached their ultimate failure point. The experimental test consists of casting nine plain concrete beams with identical specifications. Three beams are utilised as control beams; three are reinforced with GFRP sheets measuring 50 mm × 100 mm, and another three are reinforced with GFRP sheets measuring 100 mm × 200 mm. The flexural test is designed to determine the tensile strength of concrete under bending. Compared to the control beams, the specimens strengthened with GFRP sheets demonstrated a significantly enhanced ultimate load capacity. The beams with larger GFRP sheets exhibited a higher ultimate load of 14.84 kN, while those with smaller sheets showed an ultimate load of 8.54 kN, marking an appreciable improvement in performance. Moreover, the smaller area GFRP-reinforced beams failed at a 45% higher shear stress compared to those with a larger area GFRP, indicating a differential impact based on the size of the reinforcement used. This study highlights the effectiveness of GFRP sheets in enhancing the structural performance of plain concrete beams, providing crucial insights into the benefits of different sizes of reinforcement.

The symmetric technique of formant transition generation for use in speech synthesis in Arabic

The symmetric technique of formant transition generation for use in speech synthesis in Arabic

Secured lightweight authentication for 6LoWPANs in machine-to-machine communications

The development of machine-to-machine (M2M) technologies is becoming increasingly important in the rapidly growing domain of wireless sensor networks (WSNs) and the Internet of Things (IoT). Adopting IPv6 over 6LoWPANs (Low-Power Wireless Personal Area Networks) is instrumental in communicating across diverse domains within WSNs, albeit with its challenges. Particularly, resource limitations and security vulnerabilities remain significant concerns. 6LoWPAN-based M2M protocols that rely on authentication and key establishment schemes (AKE) often fall short due to inadequate security issues and excessive resource requirements. This paper addresses these challenges by introducing a secure and resource-efficient framework—Lightweight AKE for 6LoWPAN Nodes (LAKE-6LN). LAKE-6LN capitalizes on the clustering architecture's merits and contrasts conventional router-centric approaches. To ensure lightweight and efficient operation, it uses hash functions, XOR functions, and symmetric encryption techniques. Pseudo-identity, sequence tracking numbers, and secure parameters ensure privacy and protection against attacks, including traceability, perfect forward secrecy, ephemeral secret leakage, and secure the session key. An informal analysis of LAKE-6LN's security confirms that compliance with all essential security properties has been achieved. In addition, the framework's logical robustness and security analysis are rigorously verified using BAN logic, AVISPA, and Scyther tools. LAKE-6LN has demonstrated superior performance over related schemes, demonstrating a reduction in storage costs (by 33.33 % to 85.71 %), computational overhead (by 14.28 % to 95.97 %), communication overhead (by 16.12 % to 51.85 %), and energy consumption (by 22.04 % to 99.40 %). In our comparative analysis, LAKE-6LN demonstrates its resilience against various security threats, demonstrating its potential to secure 6LoWPAN networks in M2M.

Improvement of the discrete Hardy inequality

We establish a novel improvement of the classical discrete Hardy inequality, which gives the discrete version of a recent (continuous) inequality of Frank, Laptev, and Weidl. Our arguments build on certain weighted inequalities based on discrete analogues of symmetric decreasing rearrangement techniques.

Encryption techniques for financial data security in fintech applications

In the dynamic world of financial technology (Fintech), securing financial data is a key priority. Increasing digital connectivity, adoption of cloud-based services requiring complex measures to protect the integrity, privacy and availability of sensitive information. Encryption techniques are emerging as a key tool to achieve these goals about itself by converting plaintext into ciphertext, protected from unauthorized access and probability violations. This review paper examines the various encryption techniques required to secure financial information in fintech applications. The main methods described include symmetric encryption, asymmetric and hybrid encryption techniques. Additionally, the function of end-to-end encryption (E2EE) is discussed in terms of protecting data privacy while it is being sent, which is essential for safeguarding sensitive financial activities such as mobile banking and digital payments. With its sophisticated method of permitting calculations on encrypted data without the need for decryption, homomorphic encryption shows promise for facilitating safe data analysis in Fintech settings while preserving data confidentiality. Each encryption method is scrutinized in terms of its strengths, weaknesses and practical applications in Fintech. Considerations such as computing efficiency, scalability, and regulatory compliance are addressed to provide insights for optimizing data protection strategies while adhering to industry standards and regulatory frameworks. The future of Fintech security is expected to be shaped by new developments in encryption technology, including post-quantum cryptography, artificial intelligence integration for adaptive security measures, and privacy-preserving solutions. The goal of these advancements is to strengthen the robustness of financial data security techniques in an increasingly linked digital world while mitigating changing cyber risks.

Key-Plain Mapping Algorithm: A Symmetric Key Cryptographic Technique for Secure Communication

Abstract:- In the realm of cryptography, ensuring secure communication is paramount. Symmetric key cryptographic techniques offer a robust means of encryption and decryption. The Key-Plain Mapping Algorithm presents a novel approach to symmetric key cryptography, utilizing a fixed keyword and systematic mapping between plaintext and ciphertext alphabets. This thesis delves into the principles, operations, and security aspects of the Key-Plain Mapping Algorithm, demonstrating its efficacy in securing digital communication. Keywords:- plain text; cipher text or encrypted message; encryption; decryption; symmetric key cryptography; decrypted message; keyword

An enhanced multimode phase imaging method based on the transport of intensity equation.

Label-free biological cell imaging relies on rapid multimode phase imaging of biological samples in natural settings. To improve image contrast, phase is encoded into intensity information using the differential interference contrast (DIC) and Zernike phase contrast (ZPC) techniques. To enable multimode contrast-enhanced observation of unstained specimens, this paper proposes an improved multimode phase imaging method based on the transport of intensity equation (TIE), which combines conventional microscopy with computational imaging. The ZPC imaging module based on adaptive aperture adjustment is applied when the quantitative phase results of biological samples have been obtained by solving the TIE. Simultaneously, a rotationally symmetric shear-based technique is used that can yield isotropic DIC. In this paper, we describe numerical simulation and optical experiments carried out to validate the accuracy and viability of this technology. The calculated Michelson contrast of the ZPC image in the resolution plate experiment increased from 0.196 to 0.394.

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.