Reference Implementation Research Articles

Iterating Pointers: Enabling Static Analysis for Loop-based Pointers

Pointers are an integral part of C and other programming languages. They enable substantial flexibility from the programmer’s standpoint, allowing the user fine, unmediated control over data access patterns. However, accesses done through pointers are often hard to track, and challenging to understand for optimizers, compilers, and sometimes, even for the developers themselves because of the direct memory access they provide. We alleviate this problem by exposing additional information to analyzers and compilers. By separating the concept of a pointer into a data container and an offset, we can optimize C programs beyond what other state-of-the-art approaches are capable of, in some cases even enabling auto-parallelization. Using this process, we are able to successfully analyze and optimize code from OpenSSL, the Mantevo benchmark suite, and the Lempel–Ziv–Oberhumer compression algorithm. We provide the only automatic approach able to find all parallelization opportunities in the HPCCG benchmark from the Mantevo suite the developers identified and even outperform the reference implementation by up to 18%, as well as speed up the PBKDF2 algorithm implementation from OpenSSL by up to 11x.

On the computation of lattice sums without translational invariance

This paper introduces a new method for the efficient computation of oscillatory multidimensional lattice sums in geometries with boundaries. Such sums are ubiquitous in both pure and applied mathematics and have immediate applications in condensed matter and topological quantum physics. The challenge in their evaluation results from the combination of singular long-range interactions with the loss of translational invariance caused by the boundaries, rendering standard tools like Ewald summation ineffective. Our work shows that these lattice sums can be generated from a generalization of the Riemann zeta function to multidimensional non-periodic lattice sums. We put forth a new representation of this zeta function together with a numerical algorithm that ensures exponential convergence across an extensive range of geometries. Notably, our method’s runtime is influenced only by the complexity of the considered geometries and not by the number of particles, providing the foundation for efficient and precise simulations of macroscopic condensed matter systems. We showcase the practical utility of our method by computing interaction energies in a three-dimensional crystal structure with 3 × 10 23 3\times 10^{23} particles. Our method’s accuracy is demonstrated through extensive numerical experiments. A reference implementation is provided online along with this article.

Dimensionality Reduction for the Real-Time Light-Field View Synthesis of Kernel-Based Models

Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which is a kernel-based representation. For SMoE to be viable in interactive light-field experiences, real-time view synthesis is crucial yet unsolved. This paper presents two key contributions: a mathematical derivation of a view-specific, intrinsically 2D model from the original 4D light field model and a GPU graphics pipeline that synthesizes these viewpoints in real time. Configuring the proposed GPU implementation for high accuracy, a frequency of 180 to 290 Hz at a resolution of 2048×2048 pixels on an NVIDIA RTX 2080Ti is achieved. Compared to NVIDIA’s instant-ngp Neural Radiance Fields (NeRFs) with the default configuration, our light field rendering technique is 42 to 597 times faster. Additionally, allowing near-imperceptible artifacts in the reconstruction process can further increase speed by 40%. A first-order Taylor approximation causes imperfect views with peak signal-to-noise ratio (PSNR) scores between 45 dB and 63 dB compared to the reference implementation. In conclusion, we present an efficient algorithm for synthesizing 2D views at arbitrary viewpoints from 4D planar light-field SMoE models, enabling real-time, interactive, and high-quality light-field rendering within the SMoE framework.

Isabelle/Solidity: A deep embedding of Solidity in Isabelle/HOL

Smart contracts are computer programs designed to automate legal agreements. They are usually developed in a high-level programming language, the most popular of which is Solidity. Every day, hundreds of thousands of new contracts are deployed managing millions of dollars’ worth of transactions. As for every computer program, smart contracts may contain bugs which can be exploited. However, since smart contracts are often used to automate financial transactions, such exploits may result in huge economic losses. In general, it is estimated that since 2019, more than $5B was stolen due to vulnerabilities in smart contracts. This paper addresses the issue of smart contract vulnerabilities by introducing an executable denotational semantics for Solidity within the Isabelle/HOL interactive theorem prover. This formal semantics serves as the basis for an interactive program verification environment for Solidity smart contracts. To evaluate our semantics, we integrate grammar-based fuzzing with symbolic execution to automatically test it against the Solidity reference implementation. The paper concludes by showcasing the formal verification of Solidity programs, exemplified through the verification of a basic Solidity token.

Optimizing crystals-dilithium implementation in 16-bit MSP430 environment utilizing hardware multiplier

Dilithium was selected as one of NIST standard Post Quantum Digital Signature algorithms and is undergoing standardization as a Module Lattice Digital Signature Algorithm (ML-DSA). However, until now research on optimization in embedded environments has primarily been conducted on ARM architectures, which are the basic benchmark targets. To prepare for future quantum secure Internet of Things environments, performance optimization on resource-constrained must be considered. Thus, in this paper, for the first time, we propose an optimized implementation of Dilithium in the 16-bit MSP430 environment, a low-resource device. We redesign the state-of-the-art optimization strategies for Dilithium to suit the MSP430 environment. By taking full advantage of MSP430’s hardware multiplier in the NTT-based polynomial multiplication, we achieve 73.0% and 80.1% of performance improvement for NTT and NTT−1 compared to those in the reference implementation, which contributes about 5.5%–7.0%, 15.3%–17.5%, and 7.5%–10.0% of performance improvement compared to Dilithium’s public reference implementation for keypair generation, signing, and verification, respectively.

Lazy Resampling: Fast and information preserving preprocessing for deep learning

Background and Objective:Preprocessing of data is a vital step for almost all deep learning workflows. In computer vision, manipulation of data intensity and spatial properties can improve network stability and can provide an important source of generalisation for deep neural networks. Models are frequently trained with preprocessing pipelines composed of many stages, but these pipelines come with a drawback; each stage that resamples the data costs time, degrades image quality, and adds bias to the output. Long pipelines can also be complex to design, especially in medical imaging, where cropping data early can cause significant artifacts. Methods:We present Lazy Resampling, a software that rephrases spatial preprocessing operations as a graphics pipeline. Rather than each transform individually modifying the data, the transforms generate transform descriptions that are composited together into a single resample operation wherever possible. This reduces pipeline execution time and, most importantly, limits signal degradation. It enables simpler pipeline design as crops and other operations become non-destructive. Lazy Resampling is designed in such a way that it provides the maximum benefit to users without requiring them to understand the underlying concepts or change the way that they build pipelines. Results:We evaluate Lazy Resampling by comparing traditional pipelines and the corresponding lazy resampling pipeline for the following tasks on Medical Segmentation Decathlon datasets. We demonstrate lower information loss in lazy pipelines vs. traditional pipelines. We demonstrate that Lazy Resampling can avoid catastrophic loss of semantic segmentation label accuracy occurring in traditional pipelines when passing labels through a pipeline and then back through the inverted pipeline. Finally, we demonstrate statistically significant improvements when training UNets for semantic segmentation. Conclusion:Lazy Resampling reduces the loss of information that occurs when running processing pipelines that traditionally have multiple resampling steps and enables researchers to build simpler pipelines by making operations such as rotation and cropping effectively non-destructive. It makes it possible to invert labels back through a pipeline without catastrophic loss of accuracy.A reference implementation for Lazy Resampling can be found at https://github.com/KCL-BMEIS/LazyResampling. Lazy Resampling is being implemented as a core feature in MONAI, an open source python-based deep learning library for medical imaging, with a roadmap for a full integration.

A UK Specification for Trusted Research Environments

ObjectivesThe need for Trusted Research Environments (TREs) is clear. Several influential reports have highlighted that personal or sensitive data which have been collected for operational, commercial or governmental reasons need to be managed securely in an environment that encourages best practices. TREs are designed to enable only authorised projects and researchers access to sensitive data whilst minimising risk of data exposure. Yet the TRE landscape has grown organically over at least the last decade resulting in heterogeneous environments, making it harder for data to be discovered, shared and used for public benefit. A baseline specification for TREs is required. ApproachWe engaged with around 60 organisations covering government, industry, health and academia through regular, online, shared spaces or “Collaboration Cafés”. We also embedded the public voice within the project by having members of the public within the research team and developed a series of workshops to include and reflect their input to the architecture. The whole project was developed in the open to instil trustworthiness. ResultsThe SATRE project has produced a reference TRE architecture and implementation through significant engagement with the UK TRE community. This baseline specification for UK TREs includes a set of four pillars, 28 capabilities and 160 statements. Conclusions and ImplicationsAn open and transparent TRE specification and architecture for a wide range of stakeholders has been developed. TREs can now use the SATRE specification to evaluate their environments against a common baseline.

Enabling PERK and other MPC-in-the-Head Signatures on Resource-Constrained Devices

One category of the digital signatures submitted to the NIST Post-Quantum Cryptography Standardization Process for Additional Digital Signature Schemes comprises proposals constructed leveraging the MPC-in-the-Head (MPCitH) paradigm. Typically, this framework is characterized by the computation and storage in sequence of large data structures both in signing and verification algorithms, resulting in heavy memory consumption. While some research on the efficiency of these schemes on high-performance machines has been done, studying their performance and optimization on resource-constrained ones still needs to be explored. In this work, we aim to address this gap by (1) introducing a general method to reduce the memory footprint of MPCitH schemes and analyzing its application to several MPCitH proposed schemes in the NIST Standardization Process. Additionally, (2) we conduct a detailed examination of potential memory optimizations in PERK, resulting in a streamlined version of the signing and verification algorithms with a reduced memory footprint ranging from 22 to 85 KB, down from the original 0.3 to 6 MB. Finally, (3) we introduce the first implementation of PERK tailored for Arm Cortex M4 alongside extensive experiments and comparisons against reference implementations.

A Not So Discrete Sampler: Power Analysis Attacks on HAWK signature scheme

HAWK is a lattice-based signature scheme candidate to the fourth call of the NIST’s Post-Quantum standardization campaign. Considered as a cousin of Falcon (one of the future NIST post-quantum standards) one can wonder whether HAWK shares the same drawbacks as Falcon in terms of side-channel attacks. Indeed, Falcon signature algorithm and particularly its Gaussian sampler, has shown to be highly vulnerable to power-analysis attacks. Besides, efficiently protecting Falcon’s signature algorithm against these attacks seems a very challenging task. This work presents the first power analysis leakage review on HAWK signature scheme: it extensively assesses the vulnerabilities of a central and sensitive brick of the scheme, the discrete Gaussian sampler. Knowing the output x of the sampler for a given signature leads to linear information about the private key of the scheme. This paper includes several demonstrations of simple power analysis attacks targeting this sample x with various attacker strengths, all of them performed on the reference implementation on a ChipWhisperer Lite with STM32F3 target (ARM Cortex M4). We report being able to perform key recoveries with very low (to no) offline resources. As this reference implementation of HAWK is not claimed to be protected against side-channel attacks, the existence of such attacks is not surprising, but they still concretely warn about the use of this unprotected signature on physical devices. To go further, our study proposes a generic way of assessing the performance of a sidechannel attack on x even when less information is recovered, in a setting where some protections are implemented or when the attacker has less measurement possibilities. While it is easy to see that x is a sensitive value, quantifying the residual complexity of the key recovery with some knowledge about x (like the parity or the sign of some coefficients) is not straightforward as the underlying hardness assumption is the newly introduced Module-LIP problem. We propose to adapt the existing methodology of leaky LWE estimation tools (Dachman-Soled et al. at Crypto 2020) to exploit the retrieved information and lower down the residual key recovery complexity. To finish, we propose an ad-hoc technique to lower down the leakage on the identified vulnerability points. These modifications prevent our attacks on our platform and come with essentially no cost in terms of performance. It could be seen as a temporary solution and encourages more analysis on proven side-channel protection of HAWK like masking.

Improving protection of falcon electronic signature software implementations against attacks based on floating point noise

The object of this study is digital signatures. The Falcon digital signature scheme is one of the finalists in the NIST post-quantum cryptography competition. Its distinctive feature is the use of floating-point arithmetic, which leads to the possibility of a key recovery attack with two non-matching signatures formed under special conditions. The work considers the task to improve the Falcon in order to prevent such attacks, as well as the use of fixed-point calculations instead of floating-point calculations in the Falcon scheme. The main results of the work are proposals for methods on improving Falcon's security against attacks based on the use of floating-point calculations. These methods for improving security differ from others in the use of fixed-point calculations with specific experimentally determined orders of magnitude in one case and proposals for modifying procedures during the execution of which the conditions for performing an attack on implementation level arise in the second case. As a result of the analysis, the probability of a successful attack on the recovery of the secret key for the reference implementation of the Falcon was clarified. Specific places in the code that make the attack possible have been localized and code modifications have been suggested that make the attack impossible. In addition, the necessary scale for fixed-point calculations was determined, at which it is possible to completely get rid of floating-point calculations. The results could be used to qualitatively improve the security of existing digital signatures. This will make it possible to design more reliable and secure information systems using digital signatures. In addition, the results could be implemented in existing systems to ensure their resistance to modern threats

OMOP-on-FHIR: A FHIR Server Development to Facilitate Data Interaction with the OMOP-CDM and FHIR for PGHD.

This paper introduces the concept of the implementation of a FHIR server for bidirectional data exchange with the OMOP-CDM. Leveraging FHIR as a metamodel, the implementation aims to promote a more interconnected and patient-centric healthcare ecosystem. The methodology involves utilizing the Java HAPI FHIR API for server architecture and validating the solution through patient data exchange with a FHIR reference implementation server. This initiative signifies a significant advancement in healthcare data interoperability, promising improved patient care quality and clinical research vigor.

Use of Beacon v2 for Improving Genomics Based Research in a Clinical Setting.

GA4GH has proposed the Beacon architecture as an interface to retrieve genomic information which also protects the privacy of the individuals. In this paper, we propose to adapt the Beacon Reference Implementation to the use case of a study comparing the susceptibility to the carcinogenic effects of tobacco. This analysis compares the germline of heavy smokers who have either never developed lung cancer or, on the contrary, have developed it at a young age. To adapt the Beacon Reference Implementation to the use case, we have added filtering capabilities and a new grouping of information allowing to retrieve the data by affected gene.

Efficient Local Classical Shadow Tomography with Number Conservation.

Shadow tomography aims to build a classical description of a quantum state from a sequence of simple random measurements. Physical observables are then reconstructed from the resulting classical shadow. Shadow protocols which use single-body random measurements are simple to implement and capture few-body observables efficiently, but do not apply to systems with fundamental number conservation laws, such as ultracold atoms. We address this shortcoming by proposing and analyzing a new local shadow protocol adapted to such systems. The All-Pairs protocol requires one layer of two-body gates and only poly(V) samples to reconstruct arbitrary few body observables. Moreover, by exploiting the permutation symmetry of the protocol, we derive a linear time postprocessing algorithm which applies to both hardcore bosons and spinless fermions in any spatial dimension. We provide a proof-of-principle reference implementation and demonstrate the reconstruction of two- and four-point functions in a paired Luttinger liquid of hardcore bosons.

A framework to improve causal inferences from visualizations using counterfactual operators

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.

Towards robotic laboratory automation plug & play: Reference architecture model for robot integration

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

TorchClim v1.0: a deep-learning plugin for climate model physics

Abstract. Climate models are hindered by the need to conceptualize and then parameterize complex physical processes that are not explicitly numerically resolved and for which no rigorous theory exists. Machine learning and artificial intelligence methods (ML and AI) offer a promising paradigm that can augment or replace the traditional parameterized approach with models trained on empirical process data. We offer a flexible and efficient plugin, TorchClim, that facilitates the insertion of ML and AI physics surrogates into the climate model to create hybrid models. A reference implementation is presented for the Community Earth System Model (CESM), where moist physics and radiation parameterizations of the Community Atmospheric Model (CAM) are replaced with such a surrogate. We present a set of best-practice principles for doing this with minimal changes to the general circulation model (GCM), exposing the surrogate model as any other parameterization module, and discuss how to accommodate the requirements of physics surrogates such as the need to avoid unphysical values and supply information needed by other GCM components. We show that a deep-neural-network surrogate trained on data from CAM itself can produce a model that reproduces the climate and variability in the original model, although with some biases. The efficiency and flexibility of this approach open up new possibilities for using physics surrogates trained on offline data to improve climate model performance, better understand model physical processes, and flexibly incorporate new processes into climate models.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

HAETAE: Shorter Lattice-Based Fiat-Shamir Signatures

We present HAETAE (Hyperball bimodAl modulE rejecTion signAture schemE), a new lattice-based signature scheme. Like the NIST-selected Dilithium signature scheme, HAETAE is based on the Fiat-Shamir with Aborts paradigm, but our design choices target an improved complexity/compactness compromise that is highly relevant for many space-limited application scenarios. We primarily focus on reducing signature and verification key sizes so that signatures fit into one TCP or UDP datagram while preserving a high level of security against a variety of attacks. As a result, our scheme has signature and verification key sizes up to 39% and 25% smaller, respectively, compared than Dilithium. We provide a portable, constanttime reference implementation together with an optimized implementation using AVX2 instructions and an implementation with reduced stack size for the Cortex-M4. Moreover, we describe how to efficiently protect HAETAE against implementation attacks such as side-channel analysis, making it an attractive candidate for use in IoT and other embedded systems.

A Semantic Digital Twin for the Dynamic Scheduling of Industry 4.0-based Production of Precast Concrete Elements

Precast concrete construction enhances project efficiency, sustainability, and durability by leveraging a controlled production environment that ensures high-quality outputs. Still, the sequential nature of off-site production, encompassing casting, curing, and storage of the precast elements, is subject to significant uncertainties, including supply chain variability and environmental factors affecting curing times. Dynamic adjustments to the production schedule are essential for aligning with real-time changes, necessitating a robust framework for real-time data acquisition and analysis. Dynamic Scheduling (DS) is a responsive and adaptive approach that accommodates real-time changes, optimizes the production flow, and minimizes downtime or delays. However, the DS approach demands real-time data to quickly and efficiently respond to unforeseen challenges, which requires acquiring and analyzing production-relevant data throughout the production process. The Digital Twin (DT) emerges in Industry 4.0 (I4.0) as a bridge between physical operations and digital capabilities, enabling a seamless flow of information, for which the Asset Administration Shell (AAS) is a reference implementation. This study introduces a DS framework to optimize precast element production, utilizing a DT for real-time data aggregation across the production system. The framework implements a Semantic DT based on the AAS and the Linked Data approach. It employs Resource Description Framework (RDF) serialization of the AAS and an ontological representation of the production system for data integration. The framework leverages a simulation-based scheduler, which exchanges data with the DT in a Service-oriented Architecture (SoA) using SPARQL, a language for querying and updating graph databases. The approach is evaluated through a proof of concept, demonstrating effective uncertainty management in a dynamic production environment.

The Role of Teachers in Handling Students’ Bullying Behavior at Sokong 2 State Elementary School

The role of teachers in dealing with bullying behavior of students in SDN 2 Sokong. The purpose of the study was to describe the role of teachers ' efforts in dealing with bullying behavior of students. This study is a type of qualitative research with a case study approach to review further related to the handling of teachers in forming positive behavior in students. This study was conducted in Sokong village with the consideration that Sokong village as a reference implementation in the handling of negative behavior of students. In this study the main instrument is the researcher himself and is supported by interviews, documentation and observation instruments. The results showed that the teachers in SDN 2 Sokong are very enthusiastic in dealing with bullying behavior in students, and ahlak ethics and manners have a psitive impact on the cultivation of the values of the character of the students themselves so that there is never a behavior that reflects bullying behavior that can interfere with the child's psychomotor development. The form of bullying siawa is bullying verball and non verball where the impact of bullying that occurs is psychological and physical then the bullying behavior factors are from family, peers, mass media and individuals from themselves.