Fast Automatic Verification of Large-Scale Systems with Lookup Tables

Modern safety-critical systems are difficult to formally verify, largely due to their large scale. In particular, the widespread use of lookup tables in embedded systems across diverse industries, such as aeronautics and automotive systems, create a critical obstacle to the scalability of formal verification. This paper presents a novel approach for the formal verification of large-scale systems with lookup tables. We use a learning-based technique to automatically learn abstractions of the lookup tables and use the abstractions to then prove the desired property. If the verification fails, we propose a falsification heuristic to search for a violation of the specification. In contrast with previous work on lookup table verification, our technique is completely automatic, making it ideal for deployment in a production environment. To our knowledge, our approach is the only technique that can automatically verify large-scale systems lookup with tables. We illustrate the effectiveness of our technique on a benchmark which cannot be handled by the commercial tool SLDV, and we demonstrate the performance improvement provided by our technique

Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification

Knowledge distillation (KD) is used to enhance automatic speaker verification performance by ensuring consistency between large teacher networks and lightweight student networks at the embedding level or label level. However, the conventional label-level KD overlooks the significant knowledge from non-target speakers, particularly their classification probabilities, which can be crucial for automatic speaker verification. In this paper, we first demonstrate that leveraging a larger number of training non-target speakers improves the performance of automatic speaker verification models. Inspired by this finding about the importance of non-target speakers' knowledge, we modified the conventional label-level KD by disentangling and emphasizing the classification probabilities of non-target speakers during knowledge distillation. The proposed method is applied to three different student model architectures and achieves an average of 13.67% improvement in EER on the VoxCeleb dataset compared to embedding-level and conventional label-level KD methods.Comment: Submitted to ICASSP 202

Opiskelijoiden itsearvioinnin laaduntarkastelua suuren kurssin kontekstissa

This study is part of an ongoing larger project concerning student self-assessment skills in university courses. We have developed a method enabling large cohorts of students to assess their own learning outcomes and to give their own course grades with the help of an automatic verification system. This paper explores the question of accuracy, namely, whether the self-assessed grades correspond to the students’ actual skills, and how well the automatic system can pick up issues in the self-assessment. Based on an expert’s evaluation of the skills of two students, we conclude that although for large part the model works as intended, there are some cases where neither the self-assessment nor the computer verification seem to be accurate.Peer reviewe

Acoustic individual identification in birds based on the band-limited phase-only correlation function

A new technique based on the Band-Limited Phase-Only Correlation (BLPOC) function to deal with acoustic individual identification is proposed in this paper. This is a biometric technique suitable for limited data individual bird identification. The main advantage of this new technique, in contrast to traditional algorithms where the use of large-scale datasets is assumed, is its ability to identify individuals by the use of only two samples from the bird species. The proposed technique has two variants (depending on the method used to analyze and extract the bird vocalization from records): automatic individual verification algorithm and semi-automatic individual verification algorithm. The evaluation of the automatic algorithm shows an average precision that is over 80% for the identification comparatives. It is shown that the efficiencies of the algorithms depend on the complexity of the vocalizations

A Security Verification Framework of Cryptographic Protocols Using Machine Learning

We propose a security verification framework for cryptographic protocols using machine learning. In recent years, as cryptographic protocols have become more complex, research on automatic verification techniques has been focused on. The main technique is formal verification. However, the formal verification has two problems: it requires a large amount of computational time and does not guarantee decidability. We propose a method that allows security verification with computational time on the order of linear with respect to the size of the protocol using machine learning. In training machine learning models for security verification of cryptographic protocols, a sufficient amount of data, i.e., a set of protocol data with security labels, is difficult to collect from academic papers and other sources. To overcome this issue, we propose a way to create arbitrarily large datasets by automatically generating random protocols and assigning security labels to them using formal verification tools. Furthermore, to exploit structural features of protocols, we construct a neural network that processes a protocol along its series and tree structures. We evaluate the proposed method by applying it to verification of practical cryptographic protocols.Comment: 14 pages, 5 figure

Certifications of Critical Systems – The CECRIS Experience

In recent years, a considerable amount of effort has been devoted, both in industry and academia, to the development, validation and verification of critical systems, i.e. those systems whose malfunctions or failures reach a critical level both in terms of risks to human life as well as having a large economic impact.Certifications of Critical Systems – The CECRIS Experience documents the main insights on Cost Effective Verification and Validation processes that were gained during work in the European Research Project CECRIS (acronym for Certification of Critical Systems). The objective of the research was to tackle the challenges of certification by focusing on those aspects that turn out to be more difficult/important for current and future critical systems industry: the effective use of methodologies, processes and tools.The CECRIS project took a step forward in the growing field of development, verification and validation and certification of critical systems. It focused on the more difficult/important aspects of critical system development, verification and validation and certification process. Starting from both the scientific and industrial state of the art methodologies for system development and the impact of their usage on the verification and validation and certification of critical systems, the project aimed at developing strategies and techniques supported by automatic or semi-automatic tools and methods for these activities, setting guidelines to support engineers during the planning of the verification and validation phases

Fast Automatic Verification of Large-Scale Systems with Lookup Tables

Modern safety-critical systems are difficult to formally verify, largely due to their large scale. In particular, the widespread use of lookup tables in embedded systems across diverse industries, such as aeronautics and automotive systems, create a critical obstacle to the scalability of formal verification. This paper presents a novel approach for the formal verification of large-scale systems with lookup tables. We use a learning-based technique to automatically learn abstractions of the lookup tables and use the abstractions to then prove the desired property. If the verification fails, we propose a falsification heuristic to search for a violation of the specification. In contrast with previous work on lookup table verification, our technique is completely automatic, making it ideal for deployment in a production environment. To our knowledge, our approach is the only technique that can automatically verify large-scale systems lookup with tables. We illustrate the effectiveness of our technique on a benchmark which cannot be handled by the commercial tool SLDV, and we demonstrate the performance improvement provided by our technique