Verification Time Research Articles

Flux: Liquid Types for Rust

We introduce Flux, which shows how logical refinements can work hand in glove with Rust's ownership mechanisms to yield ergonomic type-based verification of low-level pointer manipulating programs. First, we design a novel refined type system for Rust that indexes mutable locations, with pure (immutable) values that can appear in refinements, and then exploits Rust's ownership mechanisms to abstract sub-structural reasoning about locations within Rust's polymorphic type constructors, while supporting strong updates. We formalize the crucial dependency upon Rust's strong aliasing guarantees by exploiting the Stacked Borrows aliasing model to prove that "well-borrowed evaluations of well-typed programs do not get stuck". Second, we implement our type system in Flux, a plug-in to the Rust compiler that exploits the factoring of complex invariants into types and refinements to efficiently synthesize loop annotations-including complex quantified invariants describing the contents of containers-via liquid inference. Third, we evaluate Flux with a benchmark suite of vector manipulating programs and parts of a previously verified secure sandboxing library to demonstrate the advantages of refinement types over program logics as implemented in the state-of-the-art Prusti verifier. While Prusti's more expressive program logic can, in general, verify deep functional correctness specifications, for the lightweight but ubiquitous and important verification use-cases covered by our benchmarks, liquid typing makes verification ergonomic by slashing specification lines by a factor of two, verification time by an order of magnitude, and annotation overhead from up to 24% of code size (average 14%), to nothing at all.

Research on Optimal Operation of Low Carbon Rural Microgrid Integrated with Optical Storage and Charging

With the huge consumption of fossil fuels leading to increasingly serious environmental pollution, new energy has been vigorously promoted in China. And solar energy is the energy focused on development. Aiming at the impact of photovoltaic (PV) on rural microgrid consumption due to its volatility and randomness, and in response to the national policy of developing new energy EVs. Taking into account the constraints of various energy conversion, storage, transmission devices, and system balance constraints, the paper proposes an optimal operation control strategy for a low-carbon rural microgrid based on time-sharing price (TSP) and integration of optical storage and charging. Combined with the actual power consumption and time of use price, simulation analysis and verification are carried out.

Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method

The hospital's status as a health center requires it to ensure patient safety, decrease incidents and treat patients. Identification of the patient is the primary source of patient safety difficulties. In addition to the patient's name and number, further patient-identifying components are needed to reduce this neglect. This work provides a solution in the form of biometric authentication, namely, face recognition. The convolutional neural network (CNN) approach can enable machine facial recognition. CNN is one of the deep learning techniques used to detect and identify picture objects. In this study, facial recognition was carried out using the transfer learning technique, VGGFace2 model pretraining, and SENet 50 model architecture. The dataset was collected via one-shot learning or a single sample per individual sampling. Applying the CNN model to the patient identification system yields two distinct outcomes: patient registration and verification. Registration utilizes a minimum distance of 0.35 and matches data with the complete database, whereas patient verification has a minimum distance of 0.28 and matches only the face in question. At the time of patient registration, the accuracy was between 90% and 100%. However, at the time of patient verification, the accuracy was 100%.

Timing verification of rapid single flux quantum logic cell

Digital simulation is popular in rapid single flux quantum (RSFQ) large-scale integrated (LSI) circuit design for its high efficiency and low resource requirements. However, its reliability highly depends on the timing parameters extracted by analog simulation. Therefore, it is essential to establish a closed-loop verification process of the timing parameters, including delay, setup time (ST), and hold time (HT). Current verification methods, which utilize the ring oscillator, can only measure the delay of the SFQ cells and any circuit structure that can efficiently test ST and HT have yet to be reported. In this paper, we will introduce a timing verification method that can finely estimate the delay, ST, and HT of RSFQ sequential cells. The resolution of our unique test circuit is one Josephson junction delay in the Josephson transmission line (JTL). We also perform a closed-loop verification process between simulation and measurement by bringing the process parameters derived from the process control monitor (PCM) on the same batch back to the post-simulation. The process parameters contain Josephson junction critical current density JC and the sheet resistance of the Mo resistor RSh. With our method, the post-simulation analysis of several cells' delay, ST, and HT in the SIMIT-Nb03 cell library closely follows the test results. The consistency between the simulation and test indicates that the proposed timing verification method is efficient and reliable. With the measured timing parameters of cells, the reliability of the static timing analysis (STA) in circuit design before tape-out would be increased, which would improve the yield of circuits after fabrication.

TinyLedger: A Lightweight Blockchain Ledger Protocol for the MEC Network

With the development of the Internet of Things, multi-access edge computing (MEC) is widely used in industrial IoT, intelligent transport systems, and IoT data management. As a decentralized, tamper-proof, multiparty trusted ledger technology, blockchain can enhance MEC security, privacy, and automatic use of resources. However, highly redundant storage and complex consensus prevent the blockchain from being directly deployed to edge computing nodes with limited resources. Unlike existing lightweight schemes, which do not consider liberating transaction data from blocks, this paper proposes a lightweight blockchain ledger protocol for MEC networks. The novelty of this work is that the transaction data in the block is compressed into a succinct proof through the zero-knowledge proof algorithm. To reduce the storage of block data, this paper proposes the proof-of-state-transfer model in which nodes verify the correctness of the global state transfer in the block without transactions. To reduce the communication complexity of consensus, this paper proposes a lightweight consensus protocol, which reduces the communication complexity of block voting and view switching to O(n). This paper develops the prototype system of TinyLedger to experiment and evaluate the performance of the system in terms of storage, verification, throughput, and communication. Experimental results show that compared to Ethereum, TinyLedger reduces storage capacity by 98.16%, communication volume by 74.53%, and block verification time by 87%.

Formal verification of fraud-resilience in a crowdsourcing consensus protocol

Crowdsourcing has emerged as a promising computing paradigm that utilizes human intelligence to achieve complex tasks, but it encounters several security and trust issues. Blockchain is a potential technology that can resolve most of these issues, however, it is difficult to find an appropriate consensus protocol applicable to crowdsourcing systems. Therefore, this work presents a Trust and Transactions Chain (TTC) consensus protocol built upon blockchain technology. It selects a trusted leader and validators considering a trust model which depends on deposit ratio, block generation and validation rate, and waiting rate. The TTC protocol addresses the main challenge of ensuring correctness related to critical systems of crowdsourcing which has extreme significance as their failure can result in disastrous consequences. This work is primarily focused on fraud-resilience avoiding double-spending attack. It also deals with sybil and eclipse attacks. Model checking is exploited because it is effective and automatic to conduct formal verification. The TTC protocol is formally modeled utilizing Communicating Sequential Programs, and the fraud-resilience property is specified using Linear Temporal Logic. The verification of the model is done using Process Analysis Toolkit that takes the formal model and specified properties as input to inspect the properties’ satisfaction or violation. The results of the formal verification are analyzed with respect to the verification time and the number of visited states.

Topical glucocorticosteroids: from high clinical efficacy to the problem of uncontrolled use (case report)

Due to their pronounced biological activity, glucocorticosteroids are widely used in various fields of medical practice. The variety of effects of these hormones makes them an important part of the treatment of dermatological patients. This group of drugs has a pronounced positive effect on various links in the pathogenesis of many chronic dermatoses due to anti-inflammatory, vasoconstrictive, immunosuppressive and antiproliferative effects. The creation of combined external preparations containing a glucocorticosteroid in combination with an antibacterial and/or antifungal component has become a significant achievement in dermatological practice. Due to the rapid relief of signs of inflammation, as well as additional antibacterial and antifungal effects, these external agents have become especially attractive to patients. Widespread advertising and over-the-counter sale of drugs containing glucocorticosteroids leads to the fact that a significant part of patients seek advice from a dermatologist only after their ineffective use. At the same time, their uncontrolled use can lead to a number of side effects, including both pathological changes in the skin and pronounced negative systemic reactions. This fact significantly changes the clinical picture of the disease, limits the possibilities of early laboratory diagnosis of infection, which often prolongs the time for verification of the diagnosis and complicates therapy. The article presents our own clinical observations of patients with skin mycoses who have been using combined topical preparations containing glucocorticosteroids for a long time. This led to an atypical clinical course of the infection and required additional diagnostics using histological examination. The analysis of clinical observations is intended to focus the attention of primary care physicians on this problem, which can increase patient awareness of the risks of using topical drugs containing glucocorticosteroids.

LACT+: Practical Post-Quantum Scalable Confidential Transactions

A “confidential monetary value” carries information about the real monetary value but does not disclose it. Post-quantum private blockchains with confidential monetary values—large-sized blockchains with large verification times—have the least scalability because they need to save and verify more information than those with “plain-text monetary values”. High scalability is an essential security requirement for decentralized blockchain payment systems because the more honest peers who can afford to verify the blockchain copies are, the higher the security. We propose a quantum-safe transaction protocol for confidential monetary blockchains, LACT+ (Lattice-based Aggregable Confidential Transactions), which is more scalable than previous post-quantum confidential blockchains, i.e., many input/output transactions with logarithmic sized complexity.

Deep Learning Robustness Verification for Few-Pixel Attacks

While successful, neural networks have been shown to be vulnerable to adversarial example attacks. In L 0 adversarial attacks, also known as few-pixel attacks, the attacker picks t pixels from the image and arbitrarily perturbs them. To understand the robustness level of a network to these attacks, it is required to check the robustness of the network to perturbations of every set of t pixels. Since the number of sets is exponentially large, existing robustness verifiers, which can reason about a single set of pixels at a time, are impractical for L 0 robustness verification. We introduce Calzone, an L 0 robustness verifier for neural networks. To the best of our knowledge, Calzone is the first to provide a sound and complete analysis for L 0 adversarial attacks. Calzone builds on the following observation: if a classifier is robust to any perturbation of a set of k pixels, for k > t , then it is robust to any perturbation of its subsets of size t . Thus, to reduce the verification time, Calzone predicts the largest k that can be proven robust, via dynamic programming and sampling. It then relies on covering designs to compute a covering of the image with sets of size k . For each set in the covering, Calzone submits its corresponding box neighborhood to an existing L ∞ robustness verifier. If a set’s neighborhood is not robust, Calzone repeats this process and covers this set with sets of size k ′< k . We evaluate Calzone on several datasets and networks, for t ≤ 5. Typically, Calzone verifies L 0 robustness within few minutes. On our most challenging instances (e.g., t =5), Calzone completes within few hours. We compare to a MILP baseline and show that it does not scale already for t =3.

Building Applications and Developing Digital Signature Devices based on the Falcon Post-Quantum Digital Signature Scheme

Falcon is an efficient and secure postquantum signature scheme for services based on quantum computing. It employs the hash-and-sign approach in conjunction with the Gentry, Peikert, and Vaikuntanathan (GPV) framework on Number Theory Research Unit (NTRU) lattices. This study evaluated the operation procedure and the capacity to run the Falcon scheme using a key length of 1024 bits on different hardware and software platforms, such as personal computers and Raspberry Pi 4 and Windows, Ubuntu, and Android operating systems. The following results were obtained: file sizes ranged from 30 to 5449268 KB, digital signature times ranged from 50 to 19500ms, and signature verification times ranged from 14 to 19000ms. The results show that the Falcon post-quantum signature scheme works stably and ensures execution speed on different platforms, similar to current digital signature schemes.

Cost-effective anonymous data sharing with forward security using improved authentication

Cloud computing gives customers instant access to a network of remote servers, networks, and data centres. Cloud computing makes data analysis helpful to society and individuals. Sharing data with many people causes efficiency, integrity, and privacy issues. Ring signatures may enable secure and anonymous data transfer. It anonymizes data verification for cloud-based analytics. Identity-based (ID) ring signatures are becoming popular alternatives to public key infrastructure (PKI)-based public-key encryption. PKI bottlenecks are certificate verification time and cost. ID-based ring signatures speed up certificate verification. We observed that encrypting ID-based ring signatures with a variation of SHA-384 and adding forward security considerably improves their security. Padding divides the input text into 512-byte blocks and adds the length as a 48-bit value to the hash in newer SHA versions. Signatures made before a user's secret key was compromised are legitimate. If a user's secret key is compromised, it's impossible to compel all data owners to re-verify their data, hence a large-scale data sharing system must contain this feature. We implement, secure, and prove our method's use. SHA-384 is safer for cloud-based anonymous data sharing.

Testing, Validation, and Verification of Robotic and Autonomous Systems: A Systematic Review

We perform a systematic literature review on testing, validation, and verification of robotic and autonomous systems (RAS). The scope of this review covers peer-reviewed research papers proposing, improving, or evaluating testing techniques, processes, or tools that address the system-level qualities of RAS. Our survey is performed based on a rigorous methodology structured in three phases. First, we made use of a set of 26 seed papers (selected by domain experts) and the SERP-TEST taxonomy to design our search query and (domain-specific) taxonomy. Second, we conducted a search in three academic search engines and applied our inclusion and exclusion criteria to the results. Respectively, we made use of related work and domain specialists (50 academics and 15 industry experts) to validate and refine the search query. As a result, we encountered 10,735 studies, out of which 195 were included, reviewed, and coded. Our objective is to answer four research questions, pertaining to (1) the type of models, (2) measures for system performance and testing adequacy, (3) tools and their availability, and (4) evidence of applicability, particularly in industrial contexts. We analyse the results of our coding to identify strengths and gaps in the domain and present recommendations to researchers and practitioners. Our findings show that variants of temporal logics are most widely used for modelling requirements and properties, while variants of state-machines and transition systems are used widely for modelling system behaviour. Other common models concern epistemic logics for specifying requirements and belief-desire-intention models for specifying system behaviour. Apart from time and epistemics, other aspects captured in models concern probabilities (e.g., for modelling uncertainty) and continuous trajectories (e.g., for modelling vehicle dynamics and kinematics). Many papers lack any rigorous measure of efficiency, effectiveness, or adequacy for their proposed techniques, processes, or tools. Among those that provide a measure of efficiency, effectiveness, or adequacy, the majority use domain-agnostic generic measures such as number of failures, size of state-space, or verification time were most used. There is a trend in addressing the research gap in this respect by developing domain-specific notions of performance and adequacy. Defining widely accepted rigorous measures of performance and adequacy for each domain is an identified research gap. In terms of tools, the most widely used tools are well-established model-checkers such as Prism and Uppaal, as well as simulation tools such as Gazebo; Matlab/Simulink is another widely used toolset in this domain. Overall, there is very limited evidence of industrial applicability in the papers published in this domain. There is even a gap considering consolidated benchmarks for various types of autonomous systems.

A Simple and Optimal Algorithm for Strict Circular Seriation

Recently, Armstrong, Guzmán, and Sing Long [SIAM J. Math. Data Sci., 3 (2021), pp. 1223–1250] presented an optimal time algorithm for strict circular seriation (called also the recognition of strict quasi-circular Robinson spaces). In this paper, we give a very simple time algorithm for computing a compatible circular order for strict circular seriation. When the input space is not known to be strict quasi-circular Robinson, our algorithm is complemented by an time verification of compatibility of the returned order. This algorithm also works for recognition of other types of strict circular Robinson spaces known in the literature. We also prove that the circular Robinson dissimilarities (which are defined by the existence of compatible orders on one of the two arcs between each pair of points) are exactly the pre-circular Robinson dissimilarities (which are defined by a four-point condition).

An evolutionary algorithmic framework cloud based evidence collection architecture

Forensic in cloud computing is an advancement of evolutionary modern forensic science that protects against cyber criminals. Single centralize point compilation and storage of data, however, overcome the authenticity of digital evidence. In order to address this serious issue, this article suggests a evolutionary modern algorithm automated forensic platform leveraging infrastructure as a cloud service (IaaS) based on Blockchain concept. This proposed forensic structural design, evidence collection of evidence and stored on a blockchain which is circulated around several peer blocks. Secure Block Verification Mechanism (SBVM) is proposed to Safeguarding the device from unauthorised users. Using the Backtracking Search Optimization Algorithm search optimization algorithm for strengthening of the cloud environment, secret keys are optimally generated. On the bases of level of confidentiality, all data is stored and encrypted at cloud authentication server. Confidentiality-based Algebraically Homomorphic Cryptosystems learning is presented with a fast-forwarding algorithm for encryption. A block in the SDN controller is created for every data and information is stored in the cloud service provider and the history is recorded as metadata data about data. A hash based tree is constructed in each block by Secure Hash Algorithm version - 3 of 512 bits. By implementing graph theory-based graph neural networks in Smart Contracts, our framework enables users to track their data (GNNSC). Finally, the construction of a evidence graph using blockchain data enables evidence analysis. Experiments was carried out in a Python programming and blockchain integrated cloud environment with network simulator-3.30 (for Software Defined Network). As part of result our newly designed forensic architecture using blochchain (FAuB) good results in terms of evidence response time, insertion times of cloud evidence, verification time of evidence, computational overhead of evidence, hashes calculation time, keys generations times of evidence, evidence encryption time, evidence decryptions time, and total overall change rate of evidence, according to a comprehensive comparative study.

Health Passport: A blockchain-based PHR-integrated self-sovereign identity system

During the COVID-19 pandemic, it was necessary to validate a person’s health status along with their identity to permit travel. This was facilitated via paper-based certificates and centralized digital apps. Even after COVID-19, it is anticipated that such health status verifications will be required for travel and other purposes. As a result, there needs to be an additional credential, a “Health Passport,” that establishes whether a person satisfies the health requirements for various purposes. Digital credentials so prepared should be trustable, unforgeable, and verifiable. The Health Passport should be designed to protect the end-users’ privacy and give people control over the data they use to confirm their credentials. This article explores the requirements for a generalized Health Passport system and uses agent-oriented modeling (AOM) to design a blockchain-based self-sovereign identity (SSI) system integrated with the Personal Health Record (PHR) to address this requirement. The article demonstrates the feasibility of the solution by implementing a proof of concept on Hyperledger Indy and Aries, integrated with the PHR – MediTrans. Credential issuance and verification time were calculated, and it was observed that the time overhead was minimal. This solution allows users to verify their credentials with the verifier without revealing any significant personal information. Our solution can be integrated into any PHR solution as the SSI solution is added as a plugin to the PHR accessible via a mobile/web app.

Strengthening the Integrity of Forwarding First Communication Using Forward Key Chain and Bloom Filter in the Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are collections of sensor nodes that can help people monitor things wirelessly, replacing the need for human presence in hard-to-reach areas. While forwarding first communication reduces communication delays among the sensor nodes, it lacks a security mechanism. Since sensor nodes have limited resources, using complex security measures can shorten their lifetime. This study proposes a protocol modification for forwarding first communication using a forward keychain and Bloom Filter Vector (BFV). The sender is required to send a BFV in every message exchange. The process of sending the BFV is separated to increase the size of the message payload in each communication. The results show that while the communication overhead and verification time increase to an acceptable level, the message payload is larger and message integrity is guaranteed.

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.

Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.

Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.

Theoretical modeling and verification of storage time of cold box based on network method of radiation heat transfer

The storage time of cold box is an important subject. At present, the research method of the storage time is mainly an experiment, and the theoretical research is incomplete. Therefore, in this work, theoretical modeling combined with a network method of radiation heat transfer, the shape factor of heat conduction, and sensible heat of refrigerant were proposed to calculate the storage time of a cold box at various ambient temperatures and amounts of refrigerant. The experimental and simulation results were used to verify the theoretical modeling of the storage time. The influences of the sensible heat of refrigerant and radiation heat transfer in the cold box were analyzed. The stability of the theoretical modeling was studied via simulation of the different cold box and refrigerant materials. The results of the accuracy of the theoretical model confirmed that the maximum error between the theoretical and the experimental storage times is 8.45%, and the error between the theoretical and the simulation results is 7.51%, indicating that the sensible heat of refrigerant and radiation heat transfer have big impacts on the storage time. The stability of the theoretical model is confirmed under the different box and refrigerant materials, and different shapes of refrigerant. The storage time model has stability on different box materials and refrigerant materials. The theoretical model can be severed as a fundamentally guidance for the cold box production design.

Security Verification of an Authentication Algorithm Based on Verifiable Encryption

A new class of cryptosystems called verifiable encryption (VE) that facilitates the verification of two plaintexts without decryption was proposed in our previous paper. The main contributions of our previous study include the following. (1) Certain cryptosystems such as the one-time pad belong to the VE class. (2) We constructed an authentication algorithm for unlocking local devices via a network that utilizes the property of VE. (3) As a result of implementing the VE-based authentication algorithm using the one-time pad, the encryption, verification, and decryption processing times are less than 1 ms even with a text length of 8192 bits. All the personal information used in the algorithm is protected by Shanon’s perfect secrecy. (4) The robustness of the algorithm against man-in-the-middle attacks and plaintext attacks was discussed. However, the discussion about the security of the algorithm was insufficient from the following two perspectives: (A) its robustness against other theoretical attacks such as ciphertext-only, known-plaintext, chosen-plaintext, adaptive chosen-plaintext, chosen-ciphertext, and adaptive chosen-ciphertext attacks was not discussed; (B) a formal security analysis using security verification tools was not performed. In this paper, we analyze the security of the VE-based authentication algorithm by discussing its robustness against the above theoretical attacks and by validating the algorithm using a security verification tool. These security analyses, show that known attacks are ineffective against the algorithm.