Verification Tools Research Articles

Closing the Gap between Electrical and Physical Design Steps with an Analog IC Placement Optimizer Enhanced with Machine-Learning-Based Post-Layout Performance Regressors

The design of integrated circuits in the analog spectrum is intricate due to the signals’ continuous nature. Additionally, it is strongly affected by the physical implementation of their devices and interconnections on the layout, a design task that has stubbornly defied all automation attempts. In this paper, one limitative factor is identified that must be addressed to finally push automation tools into the analog integrated circuit design flow: accurate assessment of post-layout performance degradation. For this purpose, a performance-driven placement generator highly integrated with off-the-shelf tools already adopted by circuit/layout designers, i.e., circuit simulator, verification tools (layout-versus-schematic) and layout extractor, is proposed. Toward maximum post-layout accuracy, this generator promotes an exhaustive simulation-based synthesis, extracting, simulating and verifying the post-layout functional behavior of every candidate floorplan. Additionally, to bypass the time-consuming extractions/simulations and accelerate synthesis, novel post-layout performance regressors based on different highly accurate machine learning techniques are also being developed. The data used to train them can be directly and conveniently acquired from previous precise post-placement simulations. Experimental results over two analog circuit structures show that a set of performance regressors based on tree-based models, while operating on compressed design spaces, allow for the speeding up of synthesis by more than 20×, which represents a step toward an efficient fully automatic performance-driven analog integrated circuit design flow.

Unifying Model Execution and Deductive Verification with Interaction Trees in Isabelle/HOL

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models.

A modular, human body-mimicking phantom with active thermoregulation capabilities for validation and verification of convective hyperthermia devices

Objectives This study aims to design and fabricate a modular phantom for hyperthermia applications, addressing interpatient variability in thermal regulation mechanisms like sweating rate, metabolic heat production, and blood redistribution. Materials & methods The phantom can be constructed in various weights and dimensions by connecting identical units. Each unit consists of an agar-based block, an ethyl cellulose-based top layer, a heat source, deep and superficial water circulation, and a sweating mechanism. Agar and ethyl cellulose gels mimic the thermal properties of human tissues and fat respectively. The blocks are wrapped in PVC foil to prevent water evaporation. A heating wire, coiled around an embedded aluminum tubing simulates metabolic heat production. A superficial water circulation mimics skin capillaries. A water pump ensures a steady flow rate throughout the tubing system. Sweat production is simulated using a water pump and perforated tubing. A programmed controller maintains core temperature in a normal operating mode and simulates an anesthetized patient in anesthesia mode. Results Temperature uniformity and regulation were assessed under varying environmental conditions. The phantom effectively regulated its core temperature at 37.0 °C +/- 0.7 °C with an ambient temperature ranging between 21 °C and 30 °C. Activating the water circulation reduced the maximum temperature gradient within the phantom from 4.70 °C to 1.92 °C. Conclusion The versatile phantom successfully models heat exchange processes. Its thermal properties, dimensions, and heat exchange rates can be tuned to mimic different patient models. These are promising results as an effective tool for hyperthermia device validation and verification, representing human physiological responses.

The Jive Verification System and Its Transformative Impact on Weather Forecasting Operations

Abstract Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and “Evidence Targeted Automation” dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library, which supports research experiments, case studies, and the development of new verification metrics and tools. This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions. These collective impacts have resulted in more accurate forecasts, ultimately benefiting society and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.

Ensemble Machine Learning-Based Virtual Multiphase Flow Metering in High Gas/Oil Ratio and Water-Cut Reservoirs

By combining data-driven ensemble machine-learning algorithms and historical oil field portable test reports, this paper proposes a Data-Drive Multiphase Virtual Flow Meter (DD-MVFM) that estimates oil, gas, and water flow rates, provides real-time monitoring, and predicts future production for a 6-month period with appropriate accuracy. The proposed DD-MVFM utilizes the existing hardware used for measurements of basic variables such as temperature, and pressure at different locations at the well-head structure. The DD-MVFM can be employed in three ways. The first way is to be used as a verification tool for multiphase physical flow meters (MPFMs), making sure they are working properly and increasing confidence in the collected readings. The second way is to use the DD-MVFM as a redundant tool when the MPFMs are not available or going through maintenance. The third way, which is the main objective of our research, is to employ the proposed DD-MVFM as a stand-alone for the complete replacement of current and future MPFM installments. This, significantly lowers the operating cost, reducing the required portable field tests, and saving the need to build a major infrastructure for the set-up of MPFMs for new oil wells. Consequently, this contributes to the ambitious goal of reducing CO2 emissions. The DD-MVFM's development involves the fusion of data wrangling and machine learning algorithms for optimal performance. Initial testing indicates an 85% correlation with the actual production rates, with potential for further improvement as more field test data is incorporated, making it a pioneering solution in the field of oil and gas management.

Deep Speech Synthesis and Its Implications for News Verification: Lessons Learned in the RTVE-UGR Chair

This paper presents the multidisciplinary work carried out in the RTVE-UGR Chair within the IVERES project, whose main objective is the development of a tool for journalists to verify the veracity of the audios that reach the newsrooms. In the current context, voice synthesis has both beneficial and detrimental applications, with audio deepfakes being a significant concern in the world of journalism due to their ability to mislead and misinform. This is a multifaceted problem that can only be tackled adopting a multidisciplinary perspective. In this article, we describe the approach we adopted within the RTVE-UGR Chair to successfully address the challenges derived from audio deepfakes involving a team with different backgrounds and a specific methodology of iterative co-creation. As a result, we present several outcomes including the compilation and generation of audio datasets, the development and deployment of several audio fake detection models, and the development of a web audio verification tool addressed to journalists. As a conclusion, we highlight the importance of this systematic collaborative work in the fight against misinformation and the future potential of audio verification technologies in various applications.

The impact of misinformation on social media in the context of natural disasters

Abstract Background Rapid dissemination of information during natural disasters plays a crucial role in managing public responses and ensuring safety. Social media can be a tool to disseminate lifesaving information but is also a ground for the propagation of misinformation. This study examines the impact of misinformation spread through social media in the context of natural disasters and explores related dynamics, effects, and countermeasures. Methods Cochrane and PRISMA recommendations were used for protocol development and sound reporting. Key terms related to misinformation, social media and natural disasters were used, with the search conducted in PubMed, and without time limitation for adequate capture. Only research papers in English were eligible for inclusion. Journal and article quality were considered. No restriction was imposed on country or age of target populations. Results Findings reveal significant influence of misinformation on public behaviour during disasters, often leading to panic and hindering effective disaster management. Specific characteristics of misinformation were identified, incl. its origins and spread mechanisms; proximity -both spatial and social- was found to be a critical factor in terms of spread and reach. Emerging evidence highlights the importance of trusted sources to effectively counter misinformation, yet response time remains a critical challenge. Technical detection of misinformation can help combat it but requires further enhancements for real-time application in diverse disaster scenarios. Conclusions Misinformation on social media poses complex challenges to disaster management, affecting both individual and societal resilience. Proactive communication strategies are critical, as is the development of advanced verification tools, and public education strategies to combat misinformation. Future research should focus on refining strategies to enhance disaster preparedness and response, ensuring a resilient and well-informed public.

Enhancing Translation Validation of Compiler Transformations with Large Language Models

This paper presents a framework that integrates Large Language Models (LLMs) into translation validation, targeting LLVM compiler transformations where formal verification tools fall short. Our framework utilizes the existing tools, like Alive2, to perform initial validation. For transformations deemed unsolvable by traditional methods, our approach leverages fine-tuned LLMs to predict soundness or unsoundness, with subsequent fuzzing applied to identify counterexamples for unsound transformations. Our approach has proven effective in complex scenarios, such as deep-learning accelerator designs, enhancing the reliability of compiler transformations.

Verifying chip designs at RTL level

As chip designs become increasingly complex, the potential for errors and defects in circuits inevitably rises, posing significant challenges to chip security and reliability. This study investigates the use of the SAT-based bounded model checking (BMC) for Propositional Projection Temporal Logic (PPTL) to verify Verilog chip designs at the register transfer level (RTL). To this end, we propose an algorithm to implement automated extraction of state transfer relations from AIGER netlist and construction of Kripke structure. Additionally, we employ PPTL with the full regular expressiveness to describe the circuit properties to be verified, especially the periodic repetitive properties. This is not possible with Linear Temporal Logic (LTL) and Computational Tree Logic (CTL). By combining the PPTL properties with finite system paths and transforming them into conjunctive normal forms (CNFs), we utilize an SAT solver for verification. Experimental results demonstrate that our verification tool, SAT-BMC4PPTL, achieves higher verification efficiency and comprehensiveness.

Intelligent Verification Tool for Surgical Information of Ophthalmic Patients-A Study Based on Artificial Intelligence Technology.

With the development of day surgery, the characteristics of "short, frequent and fast" ophthalmic surgery are becoming more prominent. However, nurses are not efficient in verifying patients' surgical information, and problems such as patient privacy leakage are becoming more prominent. To improve the situation, we developed a new augmented reality (AR)-based tool for visual recognition and artificial intelligent (AI) interpretation of the pattern and location of patient surgical skin markings for the verification of the correct surgical site and procedure. The tool can also display a variety of other verbally requested patient information. The purpose of this proposal is to evaluate its feasibility of use by surgical nurses in a real clinical setting. We developed a tool with image recognition technologies to interpretation patient surgical skin markings and match the information obtained with the patients surgical records, thus, verify the patient's surgical information. Verification includes the proper surgical site and type of procedure to be performed. Nurses can interact with the device through its speech recognition capabilities and the device provides them with a variety of other requested patient information via a heads-up display. Three hundred patients in an outpatient ophthalmology clinic were divided into an AR intelligent verification experimental group and a manual verification control group. The accuracy of information verification, work time consumption, and economic cost data were compared between the 2 groups to evaluate the effectiveness of the AR Surgical Information Intelligent Verification Tool in clinical patient surgical information verification. There was no statistically difference in the correct rates of patient surgical information review between the experimental group (95.33%) and the control group (98.67%) (χ2 = 2.934, P = 0.087). The median time for information verification was 10.00 (10.00, 11.00) seconds in the experimental group and 21.00 (20.00, 24.00) seconds in the control group, a statistically difference (Z = 0.000, P < 0.001). The experimental group saved 11 seconds per patient per review compared with the control group. Considering 10,531 surgeries in 2023, printing 1 page of surgical information per 9 patients and requiring 4 copies, 4680 pages of printing paper could be saved. The AR Surgical Information Intelligent Verification Tool has advantages in assisting medical staff in patient surgical information verification, improving nursing efficiency, preventing surgical mark errors or nonstandardization, protecting patient privacy, and saving costs. It has certain research and application value in the scenario of patient surgical information verification in ophthalmic day ward.

IsaVODEs: Interactive Verification of Cyber-Physical Systems at Scale

We formally introduce IsaVODEs (Isabelle verification with Ordinary Differential Equations), an open, compositional and extensible framework for the verification of cyber-physical systems. We extend a previous semantic approach with methods and techniques that increase its expressivity, proof automation, and scalability to the level of state-of-the-art deductive verification tools. Our contributions include a user-friendly specification language, a flexible hybrid store model, including vectors and matrices, and separation-logic-style rules for local reasoning with hybrid stores using a novel form of differentiation called framed Fréchet derivatives. The formalisation of correctness specifications with forward predicate transformers, the certification of flows as unique solutions to systems of ordinary differential equations, and invariant reasoning for such systems also contribute to the scalability and usability of our framework. In combination, these features make our framework flexible and adaptable to several verification workflows. A suite of examples and hybrid systems verification benchmarks validate our framework relative to other state-of-the-art approaches.

Railway security checks at the border: between intrusive security technologies and fundamental traveller rights.

European railway borders are facing a particular exposure to security threats and need a delicate balance between securitization and free movement, especially amid globalisation, the current geopolitical landscape and increased migrant flows. For example, the war in Ukraine illustrated the challenges experienced at the Eastern EU borders by the refugee migration surge in early 2022. This exploratory focuses on the European border security control process from the rail border perspective. It encompasses a descriptive synthesis of the lessons learned from the UIC Refugee Task Force as well as insights from the ongoing EU-funded Horizon Europe project ODYSSEUS (Unobtrusive Technologies for Secure and Seamless Border Crossing for Travel Facilitation). Project ODYSSEUS aims to support the security and integrity of the European space, reduce illegal movements of people and goods across EU borders, facilitate travelling for citizens all while protecting fundamental rights of travellers. The project will test a combination of multi-behavioural and General Data Protection Regulation (GDPR) compliant biometric user identity verification tools, allowing, when possible, citizens to cross EU border without any interruption or queue. Further, novel luggage and baggage checks will allow citizens' vehicles and cargos to be remotely checked at land borders to speed up the border check processes in a secure and reliable manner. The project will run three pilot tests at road, rail and water borders. In this paper, we analyse the implementation of project's technologies in the rail border crossing pilot test and discuss the implications for the actors involved in the process of railway border crossing (e.g., border authorities, railway operators and railway travellers).

Quantifying Ecosystem Services to Maximize Co-Benefits under Market-Based Conservation Solutions in the Edisto River Basin, South Carolina

As climate change intensifies, the need to conserve ecosystem services and our natural resources increases. Nature-based solutions projects that focus on sequestering carbon can also have significant impacts on the ecosystem services in the project area. Herein, we describe a method to quantify ecosystem services via the Integrated Valuation of Ecosystem Services and Tradeoffs (Version 3.14) model. We use those quantitative methods to show where carbon projects and other restoration projects could increase certain ecosystem services through best practices. Using the Edisto River Basin in South Carolina as a study site, we developed a spatial additionality model that shows where water retention, carbon, and sediment retention can be improved. InVEST modeling showed high levels of sediment export and water yield, with 0.12 tons/acre of sediment exported and 256.3 cm/acre of water yielded downstream on average. The model indicates that over 70,000 acres comprised of parcels greater than 20 contiguous acres could implement management to increase ecosystem service provisioning. This model output shows spatially where best management practices can be implemented to achieve positive outcomes and where carbon projects could be implemented to derive additional co-benefits. Furthermore, it can be used as a tool for measurement and verification as data is updated.

Статистичний критерій перевірки незалежності внутрішніх станів та виходів криптопримітиву, що генерує (псевдо)випадкові послідовності

The article is devoted to creation and justification of new statistical criterion of pairwise independence of binary sequences from given set, which are considered as realization of random variables. The corresponding algorithm, which fulfills the checking of pairwise independence, is formulated in details. This algorithm is necessary tool for statistical veri-fication of cryptographic quality of different cryptoprimitives, which functioning is con-nected with random/ pseudorandom sequences generation — such as ran-dom/pseudorandom sequences generators or stream ciphers. Usage of the obtained criteri-on allows independence checking not only for output sequences, but also for its interme-diate state or inputs. Note that such independence is necessary for unpredictability of out-put sequences.

Features of Carbon Regulation in the Republic of Belarus and the Russian Federation: Comparative Analysis and Trends

The article reviews international acts in the field of combating climate change (United Nations Framework Convention on Climate Change, Kyoto Protocol and Paris Agreement), identifies the main trends in the field of carbon regulation: emissions quoting; implementation of climate projects; introduction of greenhouse gas reporting and expansion of the legislative framework. A comparative analysis of the national legislation in force in the Republic of Belarus and the Russian Federation is carried out, its conditional classification is considered with the allocation of carbon regulation instruments being applied, viz. the market ones, the restrictive ones, the supporting ones and the auxiliary ones. The regulation of greenhouse gas emissions should include a wide range of activities and cannot be reduced solely to restrictive instruments. Market instruments include the national emissions trading system, international voluntary markets and instruments of international cooperation, while restrictive instruments include the introduction of carbon taxes, quotas, and mandatory carbon reporting. Among the examples of supporting instruments subsidies, targeted financing and tax preferences, as well as information policy may be noted. Auxiliary instruments include methodologies for quantitative assessment of greenhouse gas emissions and uptake, carbon reporting standards and tools for verification of reporting on greenhouse gas emissions, legislated possibility of implementing climate projects. The normative national regulation regarding the accounting of greenhouse gas emissions is considered, the categories of emission sources and greenhouse gases whose emissions are subject to accounting are identified. It is important to note that the legislation on greenhouse gases in the Republic of Belarus and the Russian Federation, as well as around the world, has not been finalized and is in the process of formation.

Phase-change ultrasound contrast agents for proton range verification: towards an in vivo application

Objective.In proton therapy, range uncertainties prevent optimal benefit from the superior depth-dose characteristics of proton beams over conventional photon-based radiotherapy. To reduce these uncertainties we recently proposed the use of phase-change ultrasound contrast agents as an affordable and effective range verification tool. In particular, superheated nanodroplets can convert into echogenic microbubbles upon proton irradiation, whereby the resulting ultrasound contrast relates to the proton range with high reproducibility. Here, we provide a firstin vivoproof-of-concept of this technology.Approach.First, thein vitrobiocompatibility of radiation-sensitive poly(vinyl alcohol) perfluorobutane nanodroplets was investigated using several colorimetric assays. Then,in vivoultrasound contrast was characterized using acoustic droplet vaporization (ADV) and later using proton beam irradiations at varying energies (49.7 MeV and 62 MeV) in healthy Sprague Dawley rats. A preliminary evaluation of thein vivobiocompatibility was performed using ADV and a combination of physiology monitoring and histology.Main results.Nanodroplets were non-toxic over a wide concentration range (<1 mM). In healthy rats, intravenously injected nanodroplets primarily accumulated in the organs of the reticuloendothelial system, where the lifetime of the generated ultrasound contrast (<30 min) was compatible with a typical radiotherapy fraction (<5 min). Spontaneous droplet vaporization did not result in significant background signals. Online ultrasound imaging of the liver of droplet-injected rats demonstrated an energy-dependent proton response, which can be tuned by varying the nanodroplet concentration. However, caution is warranted when deciding on the exact nanodroplet dose regimen as a mild physiological response (drop in cardiac rate, granuloma formation) was observed after ADV.Significance.These findings underline the potential of phase-change ultrasound contrast agents forin vivoproton range verification and provide the next step towards eventual clinical applications.

Formalising the Double-Pushout Approach to Graph Transformation

In this paper, we utilize Isabelle/HOL to develop a formal framework for the basic theory of double-pushout graph transformation. Our work includes defining essential concepts like graphs, morphisms, pushouts, and pullbacks, and demonstrating their properties. We establish the uniqueness of derivations, drawing upon Rosens 1975 research, and verify the Church-Rosser theorem using Ehrigs and Kreowskis 1976 proof, thereby demonstrating the effectiveness of our formalisation approach. The paper details our methodology in employing Isabelle/HOL, including key design decisions that shaped the current iteration. We explore the technical complexities involved in applying higher-order logic, aiming to give readers an insightful perspective into the engaging aspects of working with an Interactive Theorem Prover. This work emphasizes the increasing importance of formal verification tools in clarifying complex mathematical concepts.

Hypra: A Deductive Program Verifier for Hyper Hoare Logic

Hyperproperties relate multiple executions of a program and are useful to express common correctness properties (such as determinism) and security properties (such as non-interference). While there are a number of powerful program logics for the deductive verification of hyperproperties, their automation falls behind. Most existing deductive verification tools are limited to safety properties, but cannot reason about the existence of executions, for instance, to prove the violation of a safety property. Others support more flexible hyperproperties such as generalized non-interference, but have limitations in terms of the programs and proof structures they support. In this paper, we present the first deductive verification technique for arbitrary hyperproperties over multiple executions of the same program. Our technique automates the generation of verification conditions for Hyper Hoare Logic. Our key insight is that arbitrary hyperproperties and the corresponding proof rules can be encoded into a standard intermediate verification language by representing sets of states of the input program explicitly in the states of the intermediate program. Verification is then automated using an existing SMT-based verifier for the intermediate language. We implement our technique in a tool called Hypra and demonstrate that it can reliably verify complex hyperproperties.

Practical Verification of Smart Contracts using Memory Splitting

SMT-based verification of low-level code requires modeling and reasoning about memory operations. Prior work has shown that optimizing memory representations is beneficial for scaling verification—pointer analysis, for example can be used to split memory into disjoint regions leading to faster SMT solving. However, these techniques are mostly designed for C and C++ programs with explicit operations for memory allocation which are not present in all languages. For instance, on the Ethereum virtual machine, memory is simply a monolithic array of bytes which can be freely accessed by Ethereum bytecode, and there is no allocation primitive. In this paper, we present a memory splitting transformation guided by a conservative memory analysis for Ethereum bytecode generated by the Solidity compiler. The analysis consists of two phases: recovering memory allocation and memory regions, followed by a pointer analysis. The goal of the analysis is to enable memory splitting which in turn speeds up verification. We implemented both the analysis and the memory splitting transformation as part of a verification tool, CertoraProver, and show that the transformation speeds up SMT solving by up to 120x and additionally mitigates 16 timeouts when used on 229 real-world smart contract verification tasks.

Monotone Procedure Summarization via Vector Addition Systems and Inductive Potentials

This paper presents a technique for summarizing recursive procedures operating on integer variables. The motivation of our work is to create more predictable program analyzers, and in particular to formally guarantee compositionality and monotonicity of procedure summarization. To summarize a procedure, we compute its best abstraction as a vector addition system with resets (VASR) and exactly summarize the executions of this VASR over the context-free language of syntactic paths through the procedure. We improve upon this technique by refining the language of syntactic paths using (automatically synthesized) linear potential functions that bound the number of recursive calls within valid executions of the input program. We implemented our summarization technique in an automated program verification tool; our experimental evaluation demonstrates that our technique computes more precise summaries than existing abstract interpreters and that our tool’s verification capabilities are comparable with state-of-the-art software model checkers.