Verification Module Research Articles

Advanced Security Auditing Methods for Solidity-Based Smart Contracts

The development of smart contracts remains in its early stages, with significant differences in underlying programming languages and application platforms resulting in a lack of standardization. This lack of standardization increases the susceptibility to vulnerabilities and associated financial losses. To address security vulnerabilities in smart contracts on the Ethereum blockchain platform, this paper proposes a security audit method based on formal verification. The method integrates an input module, static analysis module, formal verification module, analog execution module, and report and recommendation module, which can accurately discover the security vulnerabilities and logical flaws of smart contracts through formal verification and other analysis techniques, thus realizing correctness detection. During the experiment, the method detects 8 types of common vulnerabilities in 148 smart contracts and marks 21 smart contracts with vulnerabilities. After manual review and analysis, it is found that 17 of these 21 marked smart contracts do have security vulnerabilities. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed, thus significantly improving the security of smart contracts and reducing the economic losses that may be caused by code defects.

Utilizing Blockchain Technology to Secure Database from Unauthorized Modifications

Centralized database-based applications are frequently susceptible to internal threats. The data stored in a relational database can be changed by any user who has administrative access to the hosting server or database system. Moreover, this kind of user may alter the associated log files, which would make it very challenging to identify any illegal alteration. It would also be difficult to attribute the assault to privileged users as well. In this study, we propose a solution to build a service on the context of student score management based on Blockchain technology. A database system based on the Blockchain platform along with accompanying services is built to allow storage and query of student learning outcomes. The database security system operates in parallel with the current system while still ensuring transparency, security and integrity of data. We develop a verification module that uses Blockchain to verify the integrity of the corresponding tuples and identify any unwanted modifications. Oracle database, RESTful API, and Hyperledger Fabric have all been used in the implementation of our method. Our findings show that the overhead of integrity checking remains constant per tuple in the results of a query and increases linearly using real data and SQL queries of different types and complexity.

A link prediction method for Chinese financial event knowledge graph based on graph attention networks and convolutional neural networks

Finance is a knowledge-intensive domain in nature, with its data containing a significant amount of interconnected information. Constructing a financial knowledge graph is an important application for transforming financial text/web content into machine-readable data. However, the complexity of Chinese financial knowledge and the dynamic and evolving nature of Chinese financial data often lead to incomplete knowledge graphs. To address this challenge, we propose a novel link prediction method for Chinese financial event knowledge graph based on Graph Attention Networks and Convolutional Neural Networks. Our method begins with the construction of the foundational Chinese financial event knowledge graph using a relational triple extraction module integrated with a large language model framework, along with a Prompting with Iterative Verification (PiVe) module for validation. To enhance the completeness of the knowledge graph, we introduce an encoder-decoder framework, where a graph attention network with joint embeddings of financial event entities and relations acts as the encoder, while a Convolutional Knowledge Base embedding model (ConvKB) serves as the decoder. This framework effectively aggregates crucial neighbor information and captures global relationships among entity and relation embeddings. Extensive comparative experiments demonstrate the utility and accuracy of this method, ultimately enabling the effective completion of Chinese financial event knowledge graphs.

Unclonable Encryption-Verification Strategy Based on Bilayer Shape Memory Photonic Crystals.

The ability to reversibly exhibit structural color patterns has positioned photonic crystals (PCs) at the forefront of anti-counterfeiting. However, the security offered by the mere reversible display is susceptible to illicit alteration and disclosure. Herein, inspired by the electronic message captcha, bilayer photonic crystal (BPC) systems with integrated decryption and verification modules, are realized by combining inverse opal (IO) and double inverse opal (DIO) with polyacrylate polymers. When the informationized BPC is immersed in ethanol or water, the DIO layer displayed encrypted information due to the solvent-induced ordered rearrangement of polystyrene (PS) microspheres. The verification step is established based on the different structural colors of the IO layer pattern, which result from the deformation or recovery of the macroporous skeleton induced by solvent evaporation. Moreover, through the evaporation-induced random self-assembly of PS@SiO2 and SiO2 microspheres, unclonable structurally colored identifying codes are created in the IO layer, ensuring the uniqueness upon the verification. The decrypted code in the DIO layer is valid only when the IO layer displays the pattern with the predetermined structural color; otherwise, it is a pseudo-code. This structural color-based "decryption-verification" approach offers innovative anti-counterfeiting applications in nanophotonics.

CHAM-CLAS: A Certificateless Aggregate Signature Scheme with Chameleon Hashing-Based Identity Authentication for VANETs

Vehicular ad hoc networks (VANETs), which are the backbone of intelligent transportation systems (ITSs), facilitate critical data exchanges between vehicles. This necessitates secure transmission, which requires guarantees of message availability, integrity, source authenticity, and user privacy. Moreover, the traceability of network participants is essential as it deters malicious actors and allows lawful authorities to identify message senders for accountability. This introduces a challenge: balancing privacy with traceability. Conditional privacy-preserving authentication (CPPA) schemes are designed to mitigate this conflict. CPPA schemes utilize cryptographic protocols, including certificate-based schemes, group signatures, identity-based schemes, and certificateless schemes. Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. Combining certificateless schemes with batch verification leads to certificateless aggregate signature (CLAS) schemes. In this paper, cryptanalysis of Xiong’s CLAS scheme revealed its vulnerabilities to partial key replacement and identity replacement attacks, alongside mathematical errors in the batch verification process. Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). The signature and verification modules are also redesigned to address the identified vulnerabilities in Xiong’s scheme. Additionally, we implemented the small exponents test within the batch verification module to achieve Type III security. While this enhances security, it introduces a slight performance trade-off. Our scheme has been subjected to formal security and performance analyses to ensure robustness.

Cooperative networking and information processing system of wireless communication UAV under the background of intelligent service

AbstractIn order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper.

Developing a Blockchain-Based Framework for Digital Archiving of BIM Using Axiomatic Design

Building information modeling (BIM) has been attracting increasing attention in the architecture, engineering, and construction (AEC) industry in recent years. The wide spread of BIM marks the transition from 2D and paper-based archiving to 3D and digital archiving of project information. However, the commonly used centralized managing approaches for BIM data have high risks of data loss and data tampering. With cryptographic algorithms and distributed databases, blockchain has the potential to address the limitations of data loss and trust in conventional BIM management methods. Therefore, this paper proposes a blockchain-based framework for digital archiving of BIM data. In this paper, the axiomatic design approach is utilized to design the archiving framework in order to systematically map the design parameters with the functional requirements and minimize the information contents. The proposed framework contains five modules, including the BIM module, building plan approval (BPA) module, building data simplification (BDS) module, distributed data storage (DDS) module, and digital document verification (DDV) module. Hyperledger Fabric is utilized to develop the blockchain system based on the proposed framework. In the end, a simple BIM model with 240 components and around 11,000 lines of content in the Industry Foundation Classes (IFC) file is taken as an illustrative example to validate the proposed blockchain approach. By storing the BIM model on to the developed blockchain, it is found that each transaction of a blockchain can store over 5000 lines of IFC contents with 0.09 s of uploading latency. The results show that the proposed blockchain-based approach can effectively and securely archive BIM data.

Improving Panoptic Narrative Grounding by Harnessing Semantic Relationships and Visual Confirmation

Recent advancements in single-stage Panoptic Narrative Grounding (PNG) have demonstrated significant potential. These methods predict pixel-level masks by directly matching pixels and phrases. However, they often neglect the modeling of semantic and visual relationships between phrase-level instances, limiting their ability for complex multi-modal reasoning in PNG. To tackle this issue, we propose XPNG, a “differentiation-refinement-localization” reasoning paradigm for accurately locating instances or regions. In XPNG, we introduce a Semantic Context Convolution (SCC) module to leverage semantic priors for generating distinctive features. This well-crafted module employs a combination of dynamic channel-wise convolution and pixel-wise convolution to embed semantic information and establish inter-object relationships guided by semantics. Subsequently, we propose a Visual Context Verification (VCV) module to provide visual cues, eliminating potential space biases introduced by semantics and further refining the visual features generated by the previous module. Extensive experiments on PNG benchmark datasets reveal that our approach achieves state-of-the-art performance, significantly outperforming existing methods by a considerable margin and yielding a 3.9-point improvement in overall metrics. Our codes and results are available at our project webpage: https://github.com/TianyuGoGO/XPNG.

An efficient targeted design for real-time defect detection of surface defects

In practical industrial applications, the inference speed of deep learning models directly affects the efficiency of industrial production. Therefore, the lightweight real-time detection method of surface defects is an essential task in the industrial process. We need to achieve a favorable balance between efficiency and accuracy since the rising demand for production efficiency. However, most of the existing pixel-level detection methods 1) often adopt huge computational overhead to learn rich features, resulting in slow inference speed and 2) show a performance degradation when applied to different industrial surface defect scenarios. To this end, we propose an efficient targeted design (ETD) for real-time defect detection of surface defects. It consists of two branches: (i) an efficient feature enhancement branch, with global aggregation module (GAM) and cross-scale guide module (CGM) to gradually enhance defect features, and (ii) an edge posterior branch, with verification module (VM) and scale interaction module (SIM) to implicitly guide the boundary details of defects. Specifically, while inheriting this framework, we reconsider the relationship between precision, parameters, and speed so that our model can be applied to different industrial scenarios. Extensive experimental results on four datasets indicate that ETD outperforms other leading saliency detection methods. Meanwhile, our method ETD-S achieves 347 FPS on ESDIs-SOD dataset, 254 FPS on Crack500 dataset, 227 FPS on NRSD-MN dataset and 273 FPS on DAGM dataset. Additionally, we conduct real-time analysis of ETD on an intelligent paradigm for industrial surface defect detection, further demonstrating its efficacy in practical scenarios. ETD demonstrates effective detection performance while achieving a lightweight architecture, which can be implemented using various deep learning frameworks, showcasing substantial potential for real-time surface defect detection. The source code and dataset are publicly available at https://github.com/VDT-2048/ETD.

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.

Research on Network Security Analysis and Prevention Strategies Based on Artificial Intelligence Algorithms

Abstract In this paper, we propose a new DDoS attack detection mechanism based on federated learning that employs dynamic thresholds to cope with the fluctuation of variable rate DDoS attacks. The performance of this detection mechanism is analyzed in terms of traffic classification, performance of verification module, accuracy and loss value. Experimental results show that the method has an accuracy of 99.83% in detecting regular Benign traffic. In burst attack scenarios, the technique significantly improves detection accuracy for all 10 common DDoS attack types. In a sustained attack environment, the intrusion detection system trained based on the DDoS model has the most minor performance degradation, and the average detection accuracy for all types of DDoS attacks still exceeds 90%. Compared with the traditional SVM model, the DDoS attack detection model based on federated learning has a significant performance advantage with Loss and Acc parameters of 0.1 and 0.9, respectively.

A low power circuit design of BLE baseband transmission data processing module

With the quickly development of the Internet of Things technology, Bluetooth (BLE) protocol has been increasingly adopted in consumer communication applications, industry communication utilization, and even positioning requirement, due to its advantages such as low power consumption, low cost, and low complexity. Especially the outstanding low power capability compared with other short-range communication protocol, many power limited AIoT (Artificial Intelligence IoT) devices select BLE as basic component. So, the low power, high performance, and small area BLE module become the key point of successful of whole AIoT system. In BLE system, the baseband data processing is the foundation. A good hardware circuit design of BLE baseband can improve BLE module performance and decrease power simultaneously. This paper analyzes the baseband data processing function of BLE in detail, and designs the baseband transmission data processing RTL circuit IP using Verilog HDL Hardware description language based on BLE protocol. The IP includes CRC verification module, whitening module and coding mapping function module. The RTL design is simulated and evaluated in workstation server with Synopsys VCS tool. The simulation results show that this design can achieve the Low-power BLE Bluetooth baseband function.

Secure multi-path routing for Internet of Things based on trust evaluation.

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

Feasibility study of a novel hash algorithm-based neutron activation analysis system for arms control treaty verification

Information on isotopic composition and geometric structure is necessary for identifying a true warhead. Nevertheless, such classified information should be protected physically or electronically. With a novel Hash encryption algorithm, this paper presents a Monte Carlo-based design of a neutron activation analysis verification module. The verification module employs a thermal neutron source, a non-uniform mask (physically encrypting information about isotopic composition and geometric structure), a gamma detector array, and a Hash encryption algorithm (for electronic encryption). In the physical field, a non-uniform mask is designed to distort the characteristic gamma rays emitted by the inspected item. Furthermore, as part of the Hash algorithm, a key is introduced to encrypt the data and improve the system resolution through electronic design. In order to quantify the difference between items, Hamming distance is used, which allows data encryption and analysis simultaneously. Simulated inspections of simple objects are used to quantify system performance. It is demonstrated that the method retains superior resolution even with 1% noise level. And the performances of anti-statistical attack and anti-brute force cracking are evaluated and found to be very excellent. The verification method lays a solid foundation for nuclear disarmament verification in the upcoming era.

Practical procedures of faults and aging inspection to Photovoltaic system using time domain Reflectometry

Photovoltaic (PV) technology has emerged as a promising and significant renewable energy source in recent decades. However, the assessment of the on-site power generation performance of a PV system is often hindered by inline faults. Additionally, the degradation of the PV module's performance over time indirectly affects the operation of the power conditioner, leading to a decrease in the overall conversion efficiency of the power supply. Therefore, the accurate and efficient inspection of faults and aging status in series-connected PV modules is essential for ensuring reliable operation. This study proposes an improved Time Domain Reflectometry (TDR) technology as a standard inspection method. The TDR technology is initially employed to detect both open and shorted-circuits in series-connected PV panels with the suggested breakpoint simulation. Furthermore, the proposed TDR impedance log method enhanced the evaluation of the PV modules at different aging degrees using various monocrystalline silicon modules for practical verification. The study provides TDR as a comprehensive and effective technique for simultaneously identifying faults and inspecting the aging of PV systems.

Development of SRAM-APB protocol interface and verification

The purpose of this mechanism is to enhance the chip’s internal connections and read/write memory capabilities. The Advanced Microcontroller Bus Architecture (AMBA) is one such shared bus that uses static random-access memory to achieve this goal. As a result, it’s important to weigh a variety of design options before diving into the Verilog description. It’s also important to remember that the system must be designed to accommodate a large number of interoperable modules and memories. The design, on the other hand, starts with fewer modules and a less complicated description and realisation that relies on memory access. ModelSim is used to simulate after the delay has been modelled in Verilog. Since the interface’s static random-access memory uses an APB protocol, its performance may be tested at this stage. In addition, Questasim employs verification modules and System Verilog technologies to guarantee the system’s operation. From the obtained results, the Direct Memory Access (DMA) with SRAM-APB outperforms the alternatives, particularly in frame transmission schemes, with a wire efficiency that is 1.4 times higher and a dynamic energy efficiency that is nearly twice as high as those of conventional configurations.

Transformer-based descriptors with fine-grained region supervisions for visual place recognition

Visual place recognition is a fundamental component in autonomous systems and robotics, which is easily limited in the real world with different viewpoints and changes in appearance. Existing approaches to tackle this problem mainly rely on dominant CNN-based architectures, which are difficult to model global correlation. Most recently, there has emerged little work that focuses on the effectiveness of the Transformer in modeling long-range dependencies, but this strategy ignores local interactions thus fails to localize the really important regions. To address the above issue, this paper proposes an effective Transformer-based architecture that takes full advantages of the strengths of Transformer related to global context modeling and local specific region capturing. We first design a dual-level Transformer descriptor encoder to successively perform self-attention within local windows and global extent of the CNN feature map to obtain multi-scale spatial context, which combines local interaction and global information. Specifically, multi-layer classification tokens from the Transformer encoder are integrated to form the global image representation. Moreover, a Transformer-guided geometric verification module is introduced to leverage the strengths of the hierarchical Transformer’s inherent self-attention mechanism for fusing multi-level attention, which is employed to filter the output token to obtain key patches and associated attention weights for achieving spatial matching. Finally, we propose a descriptor refinement strategy that employs fine-grained region-level supervisions to further enhance the capability of the network to learn local discriminative features, which effectively alleviates the confusion caused from weak image-level labels. Extensive experiments on benchmark datasets show that our approach outperforms state-of-the-art methods with promising performance.

The use of the Verification Module in an Electronic Training Computer Program as an Examination of the Quality of Medical Care

Automation of the examination of the quality of filling out the medical history will speed up the process and reduce time delays (ODNOPOLOV O. T., et al., 2018) For a doctor such a function is able to adjust the plan of medical and diagnostic procedures in time. The use of the verification module in the training computer program for filling out an electronic medical history at the student stage will allow you to quickly get acquainted with the procedure for examining the quality of medical care.

AFDX network equipment fault diagnosis technology

This paper focuses on the network equipment fault monitoring and diagnosis software, and studies the fault diagnosis of the monitored AFDX network based on the network algorithm. Firstly, the range fault characteristic parameters are designed to identify the fault type, and the correlation between the detection results and the fault characteristic parameters at each location can be obtained. Secondly, the data storage management scheme of the first level filtering and the second level caching mechanism is designed for the data collected in the detection. Then, according to the designed fault classification, fault diagnosis methods are given respectively, and the occasional anomalies are identified and suppressed. Finally, the network fault diagnosis verification module is designed, and the experimental verification is carried out from the perspectives of real-time and concurrency. The verification results prove the effectiveness of the method.

Development and clinical application of a GPU-based Monte Carlo dose verification module and software for 1.5 T MR-LINAC.

Adaptive radiotherapy (ART) has made significant advances owing to magnetic resonance linear accelerator (MR-LINAC), which provides superior soft-tissue contrast, fast speed and rich functional magnetic resonance imaging (MRI) to guide radiotherapy. Independent dose verification plays a critical role in discovering errors, while several challenges remain in MR-LINAC. A Monte Carlo-based GPU-accelerated dose verification module for Unity is proposed and integrated into the commercial software ArcherQA to achieve fast and accurate quality assurance (QA) for online ART. Electron or positron motion in a magnetic field was implemented, and a material-dependent step-length limit method was used to trade off speed and accuracy. Transport was verified by dose comparison with EGSnrc in three A-B-A phantoms. Then, an accurate Monte Carlo-based Unity machine model was built in ArcherQA, including an MR-LINAC head, cryostat, coils, and treatment couch. In particular, a mixed model combining measured attenuation and homogeneous geometry was adopted for the cryostat. Several parameters in the LINAC model were tuned to commission it in the water tank. An alternating open-closed MLC plan on solid water measured with EBT-XD film was used to verify the LINAC model. Finally, the ArcherQA dose was compared with ArcCHECK measurements and GPUMCD in 30 clinical cases through the gamma test. ArcherQA and EGSnrc were well matched in three A-B-A phantom tests, and the relative dose difference (RDD) was less than 1.6% in the homogenous region. A Unity model was commissioned in the water tank, and the RDD in the homogenous region was less than 2%. In the alternating open-closed MLC plan, the gamma result (3%/3mm) between ArcherQA and Film was 96.55%, better than the gamma result between GPUMCD and Film (92.13%). In 30 clinical cases, the mean three-dimensional (3D) gamma result (3%/2mm) was 99.36%±1.28% between ArcherQA and ArcCHECK for the QA plans and 99.27%±1.04% between ArcherQA and GPUMCD for the clinical patient plans. The average dose calculation time was 106 s in all clinical patient plans. A GPU-accelerated Monte Carlo-based dose verification module was developed and built for the Unity MR-LINAC. The fast speed and high accuracy were proven by comparison with EGSnrc, commission data, the ArcCHECK measurement dose, and the GPUMCD dose. This module can achieve fast and accurate independent dose verification for Unity.