Reviewerly/exHarmony huggingface dataset:
An Augmented MetiTarski Dataset for Real Quantifier Elimination Using Machine Learning

We contribute a new dataset composed of more than 41K MetiTarski challenges that can be used to investigate applications of machine learning (ML) in determining efficient variable orderings in Cylindrical Algebraic Decomposition. The proposed dataset aims to address inadvertent bias issues present in prior benchmarks, paving the way to development of robust, easy-to-generalize ML models.
CoProver: A Recommender System for Proof Construction

Interactive Theorem Provers (ITPs) are an indispensable tool in the arsenal of formal method experts as a platform for construction and (formal) verification of proofs. The complexity of the proofs in conjunction with the level of expertise typically required for the process to succeed can often hinder the adoption of ITPs. A recent strain of work has investigated methods to incorporate machine learning models trained on ITP user activity traces as a viable path towards full automation. While a valuable line of investigation, many problems still require human supervision to be completed fully, thus applying learning methods to assist the user with useful recommendations can prove more fruitful. Following the vein of user assistance, we introduce CoProver, a proof recommender system based on transformers, capable of learning from past actions during proof construction, all while exploring knowledge stored in the ITP concerning previous proofs. CoProver employs a neurally learnt sequence-based encoding of sequents, capturing long distance relationships between terms and hidden cues therein. We couple CoProver with the Prototype Verification System (PVS) and evaluate its performance on two key areas, namely: (1) Next Proof Action Recommendation, and (2) Relevant Lemma Retrieval given a library of theories. We evaluate CoProver on a series of well-established metrics originating from the recommender system and information retrieval communities, respectively. We show that CoProver successfully outperforms prior state of the art applied to recommendation in the domain. We conclude by discussing future directions viable for CoProver (and similar approaches) such as argument prediction, proof summarization, and more.
Category Theory in Isabelle/HOL as a Basis for Meta-logical Investigation

This paper presents meta-logical investigations based on category theory using the proof assistant Isabelle/HOL. We demonstrate the potential of a free logic based shallow semantic embedding of category theory by providing a formalization of the notion of elementary topoi. Additionally, we formalize symmetrical monoidal closed categories expressing the denotational semantic model of intuitionistic multiplicative linear logic. Next to these meta-logical-investigations, we contribute to building an Isabelle category theory library, with a focus on ease of use in the formalization beyond category theory itself. This work paves the way for future formalizations based on category theory and demonstrates the power of automated reasoning in investigating meta-logical questions.
Formalization Quality in Isabelle

Little is known about the quality of formalizations in interactive theorem proving. In this work, we analyze the relationship between static analysis warnings (lints) and maintenance effort for 6470 Isabelle theories, create models to predict lints based on structural features, and compare the results to a small ground-truth dataset collected with the help of domain experts. We find that for the majority of lints, there is a significant but low-strength correlation between frequency of occurrence and churn in maintenance change-sets. In particular, for proofs using tactic methods (which can be brittle), the Spearman correlation is highest with a strength of 0.16, $$p<0.005$$ . Furthermore, when classifying theories as lint-prone based on their formal entity graphs (which capture the dependencies between underlying logical entities), random forests outperform even deep learning models on our data, achieving 58 % precision and 21 % recall. Finally, in our ground-truth dataset of 35 good and 35 problematic theories, our pre-defined criterion that identifies theories with more than one lint every 100 lines achieves 95 % precision and 51 % recall. Remarkably, this is very close to the optimal criterion, which we observe at one lint every 109 lines. Moreover, the random forest model trained for lint-proneness even achieves perfect accuracy at 43 % recall, providing additional evidence of its effectiveness.
Construction and Implementation of a Privacy-Preserving Identity-Based Encryption Architecture

A recent proposal by Adams integrates the digital credentials (DC) technology of Brands with the identity-based encryption (IBE) technology of Boneh and Franklin to create an IBE scheme that demonstrably enhances privacy for users. We refer to this scheme as a privacy-preserving identity-based encryption (PP-IBE) construction. In this paper, we discuss the concrete implementation considerations for PP-IBE and provide a detailed instantiation (based on q-torsion groups in supersingular elliptic curves) that may be useful both for proof-of-concept purposes and for pedagogical purposes.
Proving an Execution of an Algorithm Correct?

Many algorithms in computer algebra and beyond produce answers. For some of these, we have formal proofs of the correctness of the algorithm, and for others it is easy to verify that the answer is correct. Other algorithms produce either an answer or a proof that no such answer exists. It may still be easy to verify that the answer is correct, but what about the “no such answer” case. The claim of this paper is that, at least in some cases, it is possible for the algorithm to produce “hints” such that a theorem prover can prove that, in this case, there is no such answer. This leads to the paradigm of “ad hoc UNSAT verification”.
A Study on the Existing Cybersecurity Policies and Strategies in Combating Increased Cybercrime in Zambia

The incidence of cybercrime in Zambia is rising, with perpetrators exhibiting a growing tendency to focus on corporate entities and private citizens. The Zambian government has tried to resolve the issue of network protection dangers through the execution of different approaches and procedures. Notwithstanding, the adequacy of these actions has been restricted. The present research investigates Zambia’s extant cybersecurity policies and strategies and delineates several domains where enhancements can be made. The research provides several suggestions on how the Zambian government can enhance its efforts to address cybercrime. This research employs a qualitative approach to investigate the extent of cybersecurity policies and strategies in Zambia by analyzing secondary data. The study seeks to offer valuable insights into the efficacy of Zambia’s cybersecurity framework in addressing the escalating menace of cybercrime by scrutinizing pertinent literature, government reports, and academic articles. The results of this study provide valuable insights into the difficulties encountered by the nation and propose suggestions for improving current policies and strategies.
Large-Scale Formal Proof for the Working Mathematician—Lessons Learnt from the ALEXANDRIA Project

ALEXANDRIA is an ERC-funded project that started in 2017, with the aim of bringing formal verification to mathematics. The past six years have seen great strides in the formalisation of mathematics and also in some relevant technologies, above all machine learning. Six years of intensive formalisation activity seem to show that even the most advanced results, drawing on multiple fields of mathematics, can be formalised using the tools available today.
Extracting Theory Graphs from Aldor Libraries

Aldor is a programming language for computer algebra that allows natural expression of algebraic objects while also allowing compilation to efficient code. Its language primitives, however, do not correspond exactly to those of modern proof assistants nor to those of data formats used in mathematical knowledge management. We discuss these difficulties and export the Aldor library as a diagram in the category of theories and theory morphisms, using a simplified model of the Aldor language that retains its essential expressivity. This allows us to capture a rich set of expert-designed interfaces for use in mathematical knowledge management settings.
Isabelle Formalisation of Original Representation Theorems

In a recent paper, new theorems linking apparently unrelated mathematical objects (event structures from concurrency theory and full graphs arising in computational biology) were discovered by cross-site data mining on huge databases, and building on existing Isabelle-verified event structures enumeration algorithms. Given the origin and newness of such theorems, their formal verification is particularly desirable. This paper presents such a verification via Isabelle/HOL definitions and theorems, and exposes the technical challenges found in the process. The introduced formalisation completes the verification of Isabelle-verified event structure enumeration algorithms into a fully verified framework to link event structures to full graphs.
A Comparative Study of Assessment Metrics for Imbalanced Learning

There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews several metrics for imbalanced learning in binary and multi-class problems. We emphasize considering class separability, imbalance ratio, and noise when choosing suitable metrics. Applications, advantages, and disadvantages of each metric are discussed, providing insights for different scenarios. By offering a comprehensive overview, this paper aids researchers in selecting appropriate evaluation metrics for real-world applications.
Smart Contracts and Blockchain-Based Tools for Privacy-Preservation

This research paper aims to investigate smart contracts and Blockchain technology to build a privacy-sensitive application focusing on a Digital Medication Plan (DMR) containing prescriptions. The DMR is used just as a use case. However, the proposed model applies to any context where confidential information is shared and authentication or proof of validity is necessary. We start by presenting the problem to understand the necessity of digitalizing the medication plans and how Blockchain technology fits to implement this application. After that, we explain Blockchain technology, a recent and comparatively unknown IT solution. Later we propose a design to solve the problem, and we develop a System of Smart Contracts to demonstrate how to build similar applications and suggest policies for Blockchain system design to satisfy the defined requirements. Eventually, we discuss the pertinence of multiple Blockchain solutions in handling the privacy problem.
The Potential Role of Blockchain Technology in Addressing Development Challenges in Developing Countries

Lack of transparency and accountability pose long-term challenges to the economic development of developing countries. This study argues that blockchain technology is a revolutionary innovation with the potential to address development challenges. However, it leaves behind a trail of challenges. Blockchain technology can help overcome various challenges by providing a secure digital infrastructure, facilitating faster and more cost-effective international payments, verifying identities, and securing property and property rights. The decentralized nature of blockchain technology creates new regulations by enhancing information security and transparency through the sharing of encrypted data between peer-to-peer (P2P) networks. To maximize the multifaceted contributions of blockchain technology in addressing development challenges, developing countries must act quickly to establish the necessary environment. This includes developing an organizational framework and appropriate digital infrastructure to support the economic potential and risks of blockchain technology and the digital economy. Further investigation into privacy-preserving mechanisms for blockchain is strongly recommended.
Streaming Approach to Schema Profiling

Schema profiling consists in producing key insights about the schema of data in a high-variety context. In this paper, we present a streaming approach to schema profiling, where heterogeneous data is continuously ingested from multiple sources, as is typical in many IoT applications (e.g., with multiple devices or applications dynamically logging messages). The produced profile is a clustering of the schemas extracted from the data and it is computed and evolved in real-time under the overlapping sliding window paradigm. The approach is based on two-phase k-means clustering, which entails pre-aggregating the data into a coreset and incrementally updating the previous clustering results without recomputing it in every iteration. Differently from previous proposals, the approach works in a domain where dimensionality is variable and unknown apriori, it automatically selects the optimal number of clusters, and detects cluster evolution by minimizing the need to recompute the profile. The experimental evaluation demonstrated the effectiveness and efficiency of the approach against the naïve baseline and the state-of-the-art algorithms on stream clustering.
Video Situation Monitoring to Improve Quality of Life

Video situation monitoring can be used for surveillance, regulatory (e.g., traffic) aspects, societal applications such as improving quality of life, and others. Situations (e.g., inactivity, non-participation in group activities, etc.) in the assisted living or smart home environment are difficult to detect using current customized video processing solutions. These solutions require each action or situation to be predefined and are human capital intensive both for software development and usage, not flexible, and cannot support real-time situation detection. The alternative solution is to manually detect situations using human-in-the-loop which is not practical and scalable. We propose an alternative to the above approaches which avoids (or minimizes) human-in-the-loop and can be extended to support 'real-time' detection/analysis. Our approach extracts video content only once and allows 'ad-hoc' queries (or situations) to be specified and processed without having to develop new software. In this paper, we make a case for developing expressive data models and continuous query processing of extracted video contents leveraging ideas from stream data and complex event processing. We also propose two alternative representations of extracted video contents with different benefits, identify a number of situations relevant to the assisted living domain, and indicate how they can be detected using the proposed approach. Our main contribution is defining and establishing a novel framework for a problem that is becoming very important as part of big data analytics.
Remembering the Forgotten: Clustering, Outlier Detection, and Accuracy Tuning in a Postdiction Pipeline

The ever-increasing demand to use and store data in perpetuity is limited by storage cost, which is decreasing slowly compared to computational power's exponential growth. Under these circumstances, the deliberate loss of detail in data as it ages (referred to as data decay) is useful because it allows the cost of storing data to decrease alongside the data's utility. The idea of data postdiction as a data decay method uses machine learning techniques to recover previously deleted values from data storage. This paper proposes and evaluates a new pipeline using clustering, outlier detection, machine learning, and accuracy tuning to implement an effective data postdiction for archiving data. Overall, the goal is to train a machine learning model to estimate database features, allowing for the deletion of entire columns, which can later be reconstructed within some threshold of accuracy using the stored models. We evaluate the effectiveness of our postdiction pipeline in terms of storage reduction and data recovery accuracy using a real healthcare dataset. Our preliminary results show that the order in which outlier detection, clustering, and machine learning methods are applied leads to different trade-offs in terms of storage and recovery accuracy.
Analysing Robustness of Tiny Deep Neural Networks

Real-world applications that are safety-critical and resource-constrained necessitate using compact and robust Deep Neural Networks (DNNs) against adversarial data perturbation. MobileNet-tiny has been introduced as a compact DNN to deploy on edge devices to reduce the size of networks. To make DNNs more robust against adversarial data, adversarial training methods have been proposed. However, recent research has investigated the robustness of large-scale DNNs (such as WideResNet), but the robustness of tiny DNNs has not been analysed. In this paper, we analyse how the width of the blocks in MobileNet-tiny affects the robustness of the network against adversarial data perturbation. Specifically, we evaluate natural accuracy, robust accuracy, and perturbation instability metrics on the MobileNet-tiny with various inverted bottleneck blocks with different configurations. We generate configurations for inverted bottleneck blocks using different width-multipliers and expand-ratio hyper-parameters. We discover that expanding the width of the blocks in MobileNet-tiny can improve the natural and robust accuracy but increases perturbation instability. In addition, after a certain threshold, increasing the width of the network does not have significant gains in robust accuracy and increases perturbation instability. We also analyse the relationship between the width-multipliers and expand-ratio hyper-parameters with the Lipchitz constant, both theoretically and empirically. It shows that wider inverted bottleneck blocks tend to have significant perturbation instability. These architectural insights can be useful in developing adversarially robust tiny DNNs for edge devices.
Transferable Black-Box Attack Against Face Recognition With Spatial Mutable Adversarial Patch

Deep Neural Networks (DNNs) are vulnerable to adversarial patch attacks, which raises security concerns for face recognition systems using DNNs. Previous attack methods focus on the perturbation texture and generate adversarial patches with fixed shapes at random or pre-designed locations, which causes poor adversarial transferability. This paper proposes a Spatial Mutable Adversarial Patch (SMAP) method to generate a dynamic mutable patch to be injected into the face. In the proposed SMAP, the texture, position and shape of the patch are optimized simultaneously and the patch generation pipeline is end-to-end differentiable. Specifically, a Patch Location Selection Scheme is designed to find the critical patch position with the most significant influence on the target identity by the step-based gradient search. By innovatively bridging the pre-defined mask and the dynamic update of the patch, the patch position and shape are changed based on the affine transformation and sampling mechanism in each iteration, which maintains the importance of the injected patch to the adversarial objective. To evaluate the vulnerability of face recognition models, we explore more threatening impersonation attacks under the black-box setting and design a strict evaluation metric that aligns with the real-world scenario. Extensive experiments show that the proposed SMAP improves attack performance across various face recognition models and datasets. Moreover, SMAP achieves better transferability on commercial face recognition systems than existing methods.
TinyNARM: Simplifying Numerical Association Rule Mining for Running on Microcontrollers

This paper presents a tinyNARM which is an experimental effort in approaching/tailoring the classical Numerical Association Rule Mining to limited hardware devices, precisely on ESP32 microcontrollers so that devices do not need to depend on in-cloud remote servers. The tinyNARM reduces the number of attributes in the transaction database by discretizing the continuous numeric attributes, and replacing the stochastic evolutionary algorithm for association rule mining with its deterministic counterpart. The preliminary results of the comparative study, in which the in-cloud NiaARM and the on-device tinyNARM were included by mining several UCI ML datasets, revealed that the quality of mined association rules and the time complexity were good enough for continuing the research in the direction of the tinyML.
Hyperparameter Learning Under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization

Machine learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Optimal attacks can be formulated as bilevel optimization problems and help to assess their robustness in worst case scenarios. We show that current approaches, which typically assume that hyperparameters remain constant, lead to an overly pessimistic view of the algorithms' robustness and of the impact of regularization. We propose a novel optimal attack formulation that considers the effect of the attack on the hyperparameters and models the attack as a multiobjective bilevel optimization problem. This allows us to formulate optimal attacks, learn hyperparameters, and evaluate robustness under worst case conditions. We apply this attack formulation to several ML classifiers using L2 and L1 regularization. Our evaluation on multiple datasets shows that choosing an "a priori" constant value for the regularization hyperparameter can be detrimental to the performance of the algorithms. This confirms the limitations of previous strategies and evidences the benefits of using L2 and L1 regularization to dampen the effect of poisoning attacks, when hyperparameters are learned using a small trusted dataset. Additionally, our results show that the use of regularization plays an important robustness and stability role in complex models, such as deep neural networks (DNNs), where the attacker can have more flexibility to manipulate the decision boundary.
Enhanced Wearable Force-Feedback Mechanism for Free-Range Haptic Experience Extended by Pass-Through Mixed Reality

We present an extended prototype of a wearable force-feedback mechanism coupled with a Meta Quest 2 head-mounted display to enhance immersion in virtual environments. Our study focuses on the development of devices and virtual experiences that place significant emphasis on personal sensing capabilities, such as precise inside-out optical hand, head, and controller tracking, as well as lifelike haptic feedback utilizing servos and vibration rumble motors, among others. The new prototype addresses various limitations and deficiencies identified in previous stages of development, resulting in significant user performance improvements. Key enhancements include weight reduction, wireless connectivity, optimized power delivery, refined haptic feedback intensity, improved stylus alignment, and smooth transitions between stylus use and hand-tracking. Furthermore, the integration of a mixed reality pass-through feature enables users to experience a comprehensive and immersive environment that blends physical and virtual worlds. These advancements pave the way for future exploration of mixed reality applications, opening up new possibilities for immersive and interactive experiences that combine useful aspects of real and virtual environments.