Reviewerly/exHarmony huggingface dataset:
Protection against Ransomware in Industrial Control Systems through Decentralization using Blockchain

Industrial control systems (ICSs), such as Supervisory Control and Data Acquisition (SCADA) systems, are increasingly popular for manufacturing applications, leading to significant improvements in efficiency and productivity. However, the vulnerability of these systems to ransomware attacks has become a major concern. This vulnerability is mainly due to the centralized nature of ICSs, which prioritize efficiency over security. To address this issue, this paper proposes a decentralized Blockchain-Based ICS (BBICS) architecture. Such architecture uses a peer-to-peer network of nodes to replicate critical data and distribute transactions using a consensus mechanism, which synchronizes nodes and resolves single points of failure. Additionally, BBICS encrypts critical data in a tamper-resistant manner to prevent attackers from decrypting or manipulating data. Moreover, zero-trust authorization and authentication further enhance security by preventing the broadcasting of ransomware attacks in internal networks of devices. The evaluation of the proposed system with respect to performance and reliability under normal and ransomware attack situations suggest BBICS’ feasibility and practicality.
Attention in Differential Cryptanalysis on Lightweight Block Cipher SPECK

The research on combining cryptanalysis with deep learning has recently attracted increasing attention. As an ultra-lightweight cipher for IoT environments, SPECK has attracted much attention from researchers for its excellent performance, and there have been some attempts to introduce deep learning into differential cryptanalysis on SPECK. However, existing work often built differential distinguishers based on traditional residual network, whose accuracy and interpretability on other tasks is inferior to that of attention mechanisms. In order to improve model accuracy and to further utilise the deep learning model to analyse the security of SPECK, this paper introduces the attention mechanism into the differential cryptanalysis on SPECK. First of all, by introducing an attention mechanism in the output layer of the residual network, we achieve a higher accuracy than existing works, and confusion matrices prove that the enhancement brought by attention is effective. Furthermore, using the visualization algorithm, we demonstrate the effectiveness of the attention mechanism intuitively and further analyze the features extracted from the ciphertext by deep learning. In addtion, the bit transfers captured from the ciphertexts by the attention mechanism reflects the possible insecurity of the 5, 6 and 7 rounds of SPECK for specific input differentials, and our work again demonstrates the great potential of deep learning for applications in differential cryptanalysis.
An Energy-Efficient and Area-Efficient Depthwise Separable Convolution Accelerator with Minimal On-Chip Memory Access

Depthwise separable convolution (DSC) has emerged as a crucial building block for developing lightweight convolutional neural networks (CNNs). In this paper, we present a hardware accelerator for DSC that enables 100% utilization of the processing element (PE) array for depthwise convolution (DWC) and achieves up to 98% utilization for pointwise convolution (PWC), while also reducing latency. By partitioning the input feature map (ifmap) SRAM of the DWC into three banks, we minimize memory access and maximize data reuse. The input activations and weights only need to be loaded once from SRAM to PE for both DWC and PWC. Additionally, to support efficient operations across different layers, we present a layerwise matching method. The proposed DSC accelerator is implemented in 22nm FDSOI technology and validated using MobileNetV1 on the CIFAR10 dataset. The post-layout results demonstrate that the proposed accelerator can operate at 1GHz and achieve an energy efficiency of 5.07 (3.96) TOPS/W and an area efficiency of 519.2 (461.52) GOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for DWC (PWC) at 0.8V. After scaling the supply voltage down to 0.5V, the energy efficiency for the proposed accelerator increases to 13.64 TOPS/W for DWC and 10.64 TOPS/W for PWC, respectively.
A Secure Distributed Learning Framework Using Homomorphic Encryption

The increasing complexity of artificial intelligence (AI) models poses a significant challenge for individuals and organizations without sufficient computing resources to train them. While cloud-based training services can offer a solution, they require sharing sensitive data with untrusted parties, posing risks to data privacy. To address this challenge, we explore the combination of distributed training and homomorphic encryption to parallelize the training process on encrypted data. We utilize the CKKS homomorphic encryption scheme to develop a framework that can train comparably accurate AI models in less time than other homomorphically encrypted training solutions. Our experiments demonstrate reduced total runtime for homomor-phically encrypted model training while maintaining competitive classification accuracy for the MNIST handwritten digits dataset, a well-known benchmarking dataset for machine learning. Our framework brings homomorphic encryption closer to becoming a practical data privacy solution for small stakeholders who cannot afford to compromise on security.
Verifiable and Privacy-Preserving Ad Exchange for Smart Targeted Advertising

In this paper, we propose a novel verifiable and privacy-preserving ad exchange scheme (VPAE) for smart targeted advertising that enables an ad exchange to deliver promotional advertisements to Internet users according to their specific interests, traits, and preferences. VPAE achieves the distinguished feature to preserve private information of both user profiles and advertisers, while allowing users to verify why they receive specific advertisements for adverting transparency. By utilizing homomorphic proxy re-encryption, VPAE addresses the challenge that the ad exchange has the ability to select advertisements for users, but they cannot know more than what they should. Meanwhile, VPAE integrates polynomial evaluations to achieve that the user is capable of verifying why she receives the advertisement, without learning any information of other advertisements that she does not receive. The privacy of both user profiles and advertisers are protected without sacrificing the efficiency of profile matching. We show the computational and communication overhead of VPAE through extensive analysis to demonstrate practicality and resource needs in implementation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
How to select samples for active learning? Document clustering with active learning methodology

In this paper, we investigate the applicability of the Active Learning technique to text clustering and topic modeling tasks. The aforementioned problems are often a non-trivial task due to the ambiguity of text similarity meaning. In our experiments, we implemented the Active Learning approach using automatic annotation from datasets with prepared labels. In a simulated study conducted on Polish and English datasets, we show how labeling a relatively small carefully selected number of examples can improve the quality of clustering relative to approaches based on a general notion of text similarity. We compare a number of techniques for selecting samples for labeling, dimensionality reduction and training approaches in order to compare and obtain the best quality of the resulting clusters with a minimum number of annotations. The obtained results show that with a relatively simple approach it is possible to obtain good quality clusters and thus develop classification ontologies in a data-centric approach.
Analysis and Comparison of Deepfakes Detection Methods for Cross-Library Generalisation

The rise of generative artificial intelligence (GenAI) has made it increasingly possible to use Deepfakes technology to generate fake pictures and videos. While this technology has benefits, it also has downsides such as spreading misinformation and endangering public interests. To address this issue, researchers have proposed various deep forgery detection algorithms and have achieved remarkable results. However, a common problem regarding these detection methods is that while in-library detection can usually achieve high accuracy, their performance is significantly degraded in cross-library detection. This indicates a severe problem of insufficient generalisation ability.To better compare the performance differences between various detection methods, this paper analyses the detection performance of the six established models of Two-stream, MesoNet, HeadPose, FWA, VA, and Multi-task. To ensure consistency, we employ a uniform evaluation framework as a benchmark for comparison. We conduct extensive intra-library and cross-library tests to evaluate these methods’ generalisation ability by utilising accuracy and error rate as key evaluation criteria for our experiments. Additionally, we further explore areas for improvement by analysing the impact of data augmentation, dataset partitioning, and threshold selection on the performance of these detection methods. Our comparative experiments are conducted on three existing fake face video datasets, including FaceForensics++, DeepfakeTIMIT, and Celeb-DF.Our research findings indicate the database partitioning method has a direct impact on the detector’s performance, and to enhance generalisation performance, the database should be divided person-based manually. The effectiveness of data augmentation techniques in improving cross-library performance is generally limited, and setting the threshold directly using source domain data often leads to a high error rate in the target domain. The findings of this paper provide insights into the development of more effective detection methods to combat the harmful effects of Deepfakes.
MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.
Write Blocker for Internet of Things Flash Technologies

Write Blockers are an important tool preserving digital evidence integrity and protecting the data chain of custody during a digital forensics investigation, which is crucial to the admissibility and reliability of evidence in court. One area in which write blocking tools are lacking is the Internet of Things (IoT) space. There are unique challenges to the IoT storage technologies and write-blocking them, mainly the lack of standardization in the IoT space. To address it, in this paper, we propose the design of novel write blocking tools for IoT flash technologies. We first develop a set of requirements inspired by the existing requirements for hard drive write blockers as defined by the National Institute of Standards and Technology (NIST). Afterwards, we implement a Serial Peripheral Interace (SPI) flash write blocker in both hardware and software. Furthermore, a demonstration is presented to show effectiveness of the proposed write blocking systems, and the future work is proposed.
AutoMTLSpec: Learning to Generate MTL Specifications from Natural Language Contracts

A smart legal contract is a legally binding contract in which some or all of the contractual obligations are defined and performed automatically by a computer program. As its software requirement, the legal contract is composed of legal clauses expressing the execution logic and time constraints between events in natural language. When formally verifying a smart legal contract to ensure the requirements’ conformance, it is necessary to translate the time-constrained functional requirements (TFRs) into property specifications like Metric temporal logic (MTL) as the input of a model checker. Instead of costly and error-prone manual writing, this work automates the TFR detection and the specification generation using deep learning, named AutoMTL-Spec. We separate the MTL specification generation approach into four tasks: TFR detection, intermediate representation structure extraction, event sequence/time point extraction, and MTL generation, respectively. We construct a dataset including 43 contracts of four categories, 4608 terms, and 277 TFRs. The experimental results showed that all three models significantly outperform the baselines. Most of the indicators of the three learning tasks reached near to or more than 90%.
Optimized Quantum Circuit Implementation of Payoff Function

Large-scale quantum computers that can execute practical quantum algorithms have the potential to solve complex problems that are currently challenging for classical computers. This involves converting these problems into a form that can be processed by quantum circuits, a crucial process that requires minimizing quantum resources like qubit count, gate count, and circuit depth. Our work focuses on implementing and optimizing the foundational task of quantum finance, known as option pricing, as a quantum circuit. This enables the utilization of quantum computing benefits, within the financial domain. Specifically, we implement and optimize the function f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> (S) = max(S−K, 0). Taking into consideration the significant trade-offs between qubit count and circuit depth, we have developed quantum circuits for the optimized implementation of the f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> (S). Our work incorporates various optimization techniques for the circuit, such as selecting the optimal adder, optimizing the S−K operation, parallelization, and qubit reuse. Furthermore, we offer various versions of our quantum circuits for the f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> (S), each featuring different adders and Toffoli decompositions, thereby providing flexibility for a wide range of use cases.
Polyglot AST: Towards Enabling Polyglot Code Analysis

Today, a plethora of programming languages exists, each better suited for a particular concern. For example, Python is suited for data analysis but not web development, whereas JavaScript is the inverse. As software complexity grows and needs to address multiple concerns, different programming languages are often used in combination, despite the burden of bridging them (e.g., using Java Native Interface). Polyglot programming emerged as a solution allowing the seamless mixing of multiple programming languages. GraalVM and PolyNote are examples of runtimes allowing polyglot programming. However, there is a striking lack of support at design time for building and analyzing polyglot code. To the best of our knowledge, there is no uniform language-agnostic way of reasoning over multiple languages to provide seamless code analysis, since each language comes with its own form of Abstract Syntax Trees (AST). In this paper, we present an approach to build a uniform yet polyglot AST over polyglot code, so that it is easier to perform global analysis. We first motivate this challenge and identify the main requirements for building a polyglot AST. We then propose a proof of concept implementation of our solutions on GraalVM’s polyglot API. On top of the polyglot AST, we demonstrate the ability to implement several polyglot-specific analysis services, namely auto-completion, consistency checking, type inference, and rename refactoring. Our evaluation on three polyglot projects taken from GitHub, and involving JavaScript and Python code, shows that we can build a polyglot AST without significant overhead. We also demonstrate the usefulness of the polyglot analysis services through the provided automation, as well as their scalability.
An Efficient Local Differential Privacy Scheme Using Bayesian Ridge Regression

Nowadays, our personal information is highly values with companies, hospitals, and internet services, among other industries, using it to create databases to extract user statistics or to improve their services. Local Differential Privacy (LDP) studies how these services can use the information while preserving users’ privacy through various techniques. LDP approaches have been proposed to preserve the privacy of databases and provide statistical approximation, but they are limited when dealing with k-dimensional distribution estimations. To address this drawback, we propose applying Bayesian ridge regression to the central server to recover the original distribution. The propose method has been tested on three open datasets with varying characteristics, including the number of users and the list of attributes, as well as their cardinality. Further, the proposed method outperformed the well-known LoPub and LoCop algorithms. With a privacy budget set at 0.16 per attribute and k-way set at 5, our work achieves a 57% decrease in the average variant distance compared to that of LoPub and a 50% decrease compared to LoCop. Our results suggest that a Bayesian ridge algorithm can be a useful tool for privacy preservation during data publication and it may have various applications where privacy is a concern.
Efficient Design-Time Flexible Hardware Architecture for Accelerating Homomorphic Encryption

This paper presents a design-time configurable hardware generator for hardware acceleration of the CKKS Fully Homomorphic Encryption (FHE) scheme. Our design aims to accelerate the multiplication and relinearization operations of the CKKS. It includes a design-time configurable Number Theoretic Transform (NTT) multiplication hardware for polynomial sizes between 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sup> . The NTT-based multiplication realizes modular multiplication using an efficient word-level Montgomery reduction algorithm.Polynomial multiplication is a bottleneck for the FHE operations. The NTT enables very fast polynomial multiplication by reducing its complexity to ${\mathcal{O}}\left({n{{\log }_2}n}\right)$ from ${\mathcal{O}}\left({{n^2}}\right)$. The fundamental arithmetic block of the NTT operation is the butterfly, which implements four different operations, namely, modular multiplication and modular addition/subtraction.The memory access pattern (MAP) of the NTT operation is complex, and it is crucial to design an efficient MAP for NTT for implementing a high-throughput NTT architecture. We designed and implemented an efficient algorithm for the MAP of NTT and generalized this approach for polynomial sizes, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sup> .
Building Trust in Deep Learning Models via a Self- Interpretable Visual Architecture

Deep learning models are being utilized and further developed in many application domains, but challenges still exist regarding their interpretability and consistency. Interpretability is important to provide users with transparent information that enhances the trust between the user and the learning model. It also gives developers feedback to improve the consistency of their deep learning models. In this paper, we present a novel architectural design to embed interpretation into the architecture of the deep learning model. We apply dynamic pixel-wised weights to input images and produce a highly correlated feature map for classification. This feature map is useful for providing interpretation and transparent information about the decision-making of the deep learning model while keeping full context about the relevant feature information compared to previous interpretation algorithms. The proposed model achieved 92% accuracy for CIFAR 10 classifications without finetuning the hyperparameters. Furthermore, it achieved a 20% accuracy under 8/255 PGD adversarial attack for 100 iterations without any defense method, indicating extra natural robustness compared to other Convolutional Neural Network (CNN) models. The results demonstrate the feasibility of the proposed architecture.
Disproving XAI Myths with Formal Methods – Initial Results

The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety-critical domains. eXplainable artificial intelligence (XAI) is at the core of ongoing efforts for delivering trustworthy AI. Unfortunately, XAI is riddled with critical misconceptions, that foster distrust instead of building trust. This paper details some of the most visible misconceptions in XAI, and shows how formal methods have been used, both to disprove those misconceptions, but also to devise practically effective alternatives.
Implementation Efficiency Comparison Between ANN and the Cardiff Model in ADS

This paper presents CAD implementations of the Artificial Neural Network (ANN) model and the Cardiff Model (CM) in Keysight Advanced Design System (ADS). A comparative analysis of running time difference between the two behavioral models is conducted. The results indicate that the removal of certain dispensable calculations in the CM template can save 99% of time on large dataset simulations at the fundamental frequency, while maintaining a Normalized Mean Square Error (NMSE) level lower than -50 dB. The modified CM exhibits a similar runtime compared to the ANN model with selected structure. This paper also explores the impact of changing the number of the CM coefficients and hidden neurons in the ANN model on the simulation time in ADS at fundamental frequency, without considering the accuracy of models. Results reveal a linear relationship between the simulation time and the number of the hidden neurons in the ANN model whereas the simulation time of the CM is more related to the exponents for |a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> |.