Reviewerly/exHarmony huggingface dataset:
Robust polarization-based underwater image enhancement method using anchor brightness adaptation

Most of the polarization-based image enhancement methods do not take into account the inaccurate parameter estimation caused by the amplified camera noise at low illumination conditions. To solve this problem, this paper proposes a robust polarization-based underwater image enhancement method using anchor brightness adaptation (ABA). Relying on the relationship between the Stokes Vector and the angle of polarization (AoP), the proposed neighborhood high fidelity constraint (NHFC) can robustly select the region of the background light that is most suitable for parameter estimation, greatly reducing the interference of camera random noise under low illumination conditions and making full use of the characteristics of polarized optical imaging. The background light intensity at infinity and the scene transmission map are then estimated based on the polarization characteristics of the scene, thus effectively enhancing the image. Finally, ABA is introduced with the help of greyscale information of the original image to ensure the best exposure of the enhanced image. The experiments simulate underwater imaging at night, and the results show that the enhancement performance of this method is stable and can effectively solve the problem of being susceptible to random noise interference at low light, which has better applicability for real-time underwater image enhancement.
Federated explainable artificial intelligence (fXAI): a digital manufacturing perspective

The industry has embraced digitalisation leading to a greater reliance on models derived from data. Understanding and getting insights into the models generated by machine learning algorithms is a challenge due to their non-explicit nature. Explainable artificial intelligence (XAI) is to enhance understanding of the digital models and confidence in the results they produce. The paper makes two contributions. First, the XRule algorithm proposed in the paper generates explicit rules meeting user’s preferences. A user may control the nature of the rules generated by the XRule algorithm, e.g. degree of redundancy among the rules. Second, in analogy to federated learning, the concept of federated explainable artificial intelligence (fXAI) is proposed. Besides providing insights into the models built from data and explaining the predicted decisions, the fXAI provides additional value. The user-centric knowledge generated in support of fXAI may lead to discovery of previously unknown parameters and subsequently models that may benefit the non-explicit and explicit perspectives. The insights from fXAI could translate into new ways of modelling the phenomena of interest. A numerical example and three industrial applications illustrate the concepts presented in the paper.
CKKS-Based Homomorphic Encryption Architecture using Parallel NTT Multiplier

This paper presents a high-throughput CKKS-based encryption architecture for homomorphic encryption. By de-ploying a parallel number theoretic transform (NTT) multiplier architecture, the polynomial multiplication is significantly accel-erated. Additionally, the modular multiplier is also improved by efficiently implementing using digital signal processing resources. The proposed NTT multiplier and homomorphic encryption architecture are evaluated using Xilinx Vivado and Xilinx XCU250 FPGA board. The evaluation results demonstrate that the proposed NTT multiplier helps improve the throughput of polynomial multiplication by at least 1.5 x compared to the most recent works. The efficiency of the proposal NTT multiplier, calculated by throughput per L UT or Slice, is much better than that of existing studies. The proposed homomorphic encryption architecture using the proposed NTT multiplier offers a high throughput of 32.7 Gbps.
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning

Recovering binary programs’ call graphs is crucial for inter-procedural analysis tasks and applications based on them. One of the core challenges is recognizing targets of indirect calls (i.e., indirect callees). Existing solutions all have high false positives and negatives, making call graphs inaccurate. In this paper, we propose a new solution Callee combining transfer learning and contrastive learning. The key insight is that, deep neural networks (DNNs) can automatically identify patterns concerning indirect calls. Inspired by the advances in question-answering applications, we utilize contrastive learning to answer the callsite-callee question. However, one of the toughest challenges is that DNNs need large datasets to achieve high performance, while collecting large-scale indirect-call ground truths can be computational-expensive. Therefore, we leverage transfer learning to pre-train DNNs with easy-to-collect direct calls and further fine-tune DNNs for indirect-calls. We evaluate Callee on several groups of targets, and results show that our solution could match callsites to callees with an F1-Measure of 94.6%, much better than state-of-the-art solutions. Further, we apply Callee to two applications – binary code similarity detection and hybrid fuzzing, and found it could greatly improve their performance.
Threshold Signatures in the Multiverse

We introduce a new notion of multiverse threshold signatures (MTS). In an MTS scheme, multiple universes – each defined by a set of (possibly overlapping) signers, their weights, and a specific security threshold – can co-exist. A universe can be (adaptively) created via a non-interactive asynchronous setup. Crucially, each party in the multiverse holds constant-sized keys and releases compact signatures with size and computation time both independent of the number of universes. Given sufficient partial signatures over a message from the members of a specific universe, an aggregator can produce a short aggregate signature relative to that universe.We construct an MTS scheme building on BLS signatures. Our scheme is practical, and can be used to reduce bandwidth complexity and computational costs in decentralized oracle networks. As an example data point, consider a multiverse containing 2000 nodes and 100 universes (parameters inspired by Chainlink’s use in the wild), each of which contains arbitrarily large subsets of nodes and arbitrary thresholds. Each node computes and outputs 1 group element as its partial signature; the aggregator performs under 0.7 seconds of work for each aggregate signature, and the final signature of size 192 bytes takes 6.4 ms (or 198K EVM gas units) to verify. For this setting, prior approaches, when used to construct MTS, yield schemes that have one of the following drawbacks: (i) partial signatures that are 48× larger, (ii) have aggregation times 311× worse, or (iii) have signature size 39× and verification gas costs 3.38× larger. We also provide an open-source implementation and a detailed evaluation.
A Selection Hyper-Heuristic for Transfer Learning in Genetic Programming

Transfer learning has recently gained popularity in the evolutionary algorithms space. Determining a good criteria for how to transfer knowledge can be very challenging. It has been a focus of research to optimize the process of deciding what, when and how to transfer knowledge. Genetic programming is an area of evolutionary algorithms that has benefited greatly from transfer learning. In this paper, a selection hyper-heuristic (SHeTL) has been used to optimize transfer learning, i.e. what to transfer, when to transfer and how to transfer, in genetic programming. The proposed approach has been applied to benchmark and real-world symbolic regression problems. SHeTL produced significant improvements over canonical genetic programming. Furthermore, the hyper-heuristic produced better results than previous transfer learning approaches used in genetic programming.
Dynamically evolving deep neural networks with continuous online learning

In a big data environment, data streams are sequences of dynamically changing data with unlimited length; they are often associated with concept drift, caused by data distribution transition. The uncertainty of data streams challenges the prediction ability of traditional static algorithms and hampers their practical application. Although many deep neural network models can be tailored to perform in an online manner, they are not suitable for non-stationary data streams that continually change over time. In this work, we propose a deep neural network model that can dynamically adjust to data distribution change by self-growth, that is, by changing its structure from narrow to wide and/or from shallow to deep. Specifically, initialized from a standard multilayer perceptron, the model grows by adding neurons according to a local error and adding layers according to the change in Jenson–Shannon divergence. The growth involves an adaptive memory mechanism, designed to avoid catastrophic forgetting, which may occur when hidden layers are added. In experiments, the proposed model achieved remarkable improvements in adaptability and prediction performance across 12 regression datasets (including non-stationary and stationary data streams), compared with the state-of-the-art dynamic neural network and other online approaches. The influence of the parameters, timeliness, convergence, and size of the pretrained model are explored in the paper.
FedABR: A Personalized Federated Reinforcement Learning Approach for Adaptive Video Streaming

Modern video streaming applications apply adaptive bitrate (ABR) algorithms to enhance user quality of experience (QoE). The existing model-based ABR algorithms failed to generalize to diverse network conditions and personalized QoE objectives due to their fixed control rules. The learning-based ABR algorithms required significant tuning to learn a well-performed model which can cause a QoE degradation during the model testing phase. In this paper, we propose FedABR, a novel ABR algorithm based on personalized federated learning to address the above challenges. To enable clients' local model dealing with network environment changes, we introduce a federated learning approach to train a global model using all the clients' local model without gathering their data together to protect clients' privacy. We also introduced an adaptation phase to train a personalized model for each client to maximize their individual QoE. By jointly training multiple learning tasks with a global model, it has the ability to provide transferable knowledge to supervise bitrate selection, and can be efficiently adapted to a new task in unseen environment with much fewer data samples and training epochs. We implement the proposed FedABR based on an emulation platform which connects to the Linux network protocol stack through a virtual network interface to send real data packets for evaluation. Extensive experiments based on real-world traces show that FedABR achieves the best comprehensive QoE compared with the state-of-the-art ABR algorithms in a variety of network environments.
The Smart Handwritten Digits Recognition Using Machine Learning Algorithm

Never before have people relied so much on technology; now, machine learning and based on machine learning can do anything from classifying objects in photos to inserting sounds to old movies. Similar to this, handwritten word extraction is an important field of study and advancement with a wide range of potential outcomes. The capacity of a robot to accept and analyze understandable handwriting inputs via sources like paper files, pictures, smart phones, or other gadgets is called as handwriting, sometimes known at text recognition. Evidently, in this study, they have carried out handwritten digit recognition utilising Support Vector Machines (SVM), Multi-Layer Number of neurons (MLP), and Deep Convolution Network (CNN) models with aid of MNIST dataset. To find the most accurate model for text detection, our major goal is to contrast the following models’ execution speed and correctness. The MNIST registry identity, which will be scrawled in numerals, may be recognised by the software. In machine language, the natural handwritten form may be recognised. The deep neural network is a machines educational approach that we use in this case. The nine integers in the MNIST data range from 0 to 9. It could be used to organise number and composition into groups that computers can understand. The main objective of this work is to guarantee reliable methods for identifying written finger numbers and simplifying and eliminating errors in banking processes. The MNIST information will be correctly identified by the system. Comparing the model’s precision and system performance is our main objective.
Designing S-Box Using Tent-Sine Chaotic System While Combining the Traits of Tent and Sine Map

Secure exchange of sensitive information between different entities is a serious challenge in today's environment.Therefore, various cryptosystems have been created specifically for this purpose.Various operations are used in these cryptosystems.The most common operations used are substitution and permutation.The substitution-box (S-box), a look-up table that takes x bits as input and replaces them with y bits, is responsible for the substitution operation.The construction of an S-box with strong cryptographic attributes is an essential part of building a robust and secure cryptosystem.Numerous researchers have dedicated their efforts to devising intricate techniques for constructing S-boxes.In this study, a novel approach is proposed to design a robust and dynamic S-box based on a compound-chaotic map.The suggested method for constructing the S-box is characterized by its simplicity and resilience.Notably, the Tent-Sine chaotic system significantly expands the chaotic range, setting it apart from other chaotic systems and rendering it more suitable for cryptographic applications.This research introduces a promising avenue for enhancing the security and effectiveness of S-boxes, contributing to the advancement of cryptographic systems.A number of widely accepted metrics, such as Non-Linearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Probability (LP), and Differential Probability (DP), are extensively exercised for the analysis and evaluation of the proposed S-Box's cryptographic strength.The results of the proposed of S-box are compared with the S-boxes of other researchers, and it is observed that it is cryptographically sound and worthy of inclusion in modern cryptosystems.
Multi-grained Temporal Segmentation Attention Modeling for Skeleton-based Action Recognition

The Transformer network has been widely studied for skeleton-based action recognition, and existing methods have made significant progress. However, these methods still suffer from severe overfitting issues and are less effective at capturing local relationships compared to methods based on graph convolutional neural networks (GCN). Building on previous research on attention, we propose MTS-Former, a novel multi-granularity temporal segmentation attention-based modeling method, to address these challenges. MTS-Former dynamically learns the topology of the skeleton by position-sensitive axis-attention, eliminating the constraints of manually crafted adjacency matrices. Furthermore, it effectively reduces overfitting by combining attention-guided regularization. It models the global-local relationship of skeleton sequences through segmental sampling and a multi-granularity aggregation strategy, thus enabling more robust local motion feature extraction. MTS-Former outperforms state-of-the-art Transformer networks on four benchmark datasets, DHG, SHREC, NUT RGB+D 60, and NTU RGB+D 120.
Incorporation of company-related factual knowledge into pre-trained language models for stock-related spam tweet filtering

Natural language processing for finance has gained significant attention from both academia and the industry as the continuously increasing amount of financial texts has resulted in an increasing demand for text analysis tools. Most previous studies regarding the development of pre-trained language models for financial applications have focused on obtaining linguistic instead of factual knowledge for solving real-world tasks, such as stock-related rumor detection. Factual knowledge is critical for performing tasks that require fact-verification. However, the incorporation of financial facts into pre-trained language models has not been sufficiently discussed. In this study, we adopt a post-training approach with a masked language modeling objective to incorporate company-related factual knowledge into existing pre-trained language models effectively and efficiently. Specifically, we propose an easily implementable knowledge incorporation framework that uses Form 10-K filings as a textual knowledge base, which applies a novel company name masking method that masks tokens corresponding to company names, thereby allowing the model to learn company-related information in a sentence. To assess this improvement quantitatively, we propose an evaluation framework that adopts a stock-related spam tweet filtering task as a downstream task. We hypothesize that the factual knowledge-enhanced model can distinguish between non-blue-chip and blue-chip stocks, thereby presenting a better performance in detecting spam messages that promote non-blue-chip stocks as if they were blue-chip stocks. Extensive experiments based on various amounts of training data for spam filtering tasks demonstrate that the proposed company name masking method significantly outperforms baseline methods in low-resource settings with training data sizes of 400 or less. In addition to financial applications, we anticipate that the proposed approach, which automatically extracts information regarding certain types of entities in a sentence, will contribute to other applications such as patent-examining procedures that require background knowledge to understand technological terms.
Genetic Programming for Automatic Design of Parameter Adaptation in Dual-Population Differential Evolution

The parameter adaptation is one of the main problems in many evolutionary algorithms, including differential evolution. Instead of manual development of new methods, a hyper-heuristic approach can be used, where an algorithm is applied to search for parameter adaptation scheme. In this study the symbolic regression genetic programming is applied to design parameter adaptation method for differential evolution algorithm with two populations L-NTADE. Due to algorithmic scheme different from popular L-SHADE, the L-NTADE may require specific adaptation mechanisms. Each solution in genetic programming consists of three trees, which generate scaling factor values based on current resource, success rate and current values in the memory cells, containing scaling factor and crossover rate. The training is performed on a set of 30 benchmark functions from CEC 2017 competition on numerical optimization, and at every generation of genetic programming new problem dimension, computational resource, optima location and rotation matrices are generated for every test function. The testing is performed on two benchmarks, CEC 2017 and CEC/GECCO 2022. The results comparison shows that the automatically designed parameter adaptation heuristics are capable of outperforming the success-history adaptation in many cases, including high-dimensional problems and problems with different computational resource.
Generative Artificial Intelligence and Copyright: Both Sides of the Black Box

The advent of generative artificial intelligence (GAI) has disrupted established norms and policies, transforming theoretical concepts into remarkable real-world applications. Through the advancement of large language models (LLMs) and deep learning, even non-artists can effortlessly produce impressive works of art, and chat bots adeptly assist in various tasks. However, the ethical implications of utilizing copyrighted materials in GAI training sets have sparked debates surrounding fair use and the impact on creators. This paper explores the multifaceted ethical landscape of GAI by examining the viewpoints of diverse stakeholders, including copyright owners, artists, and end users. It delves into the consequences of machine learning and training sets on individual rights and innovation, aiming to strike a balance that promotes progress while respecting artistic expression. Moreover, the paper presents a qualitative analysis of prompts submitted to the text-to-image GAI service Midjourney, shedding light on user perspectives. To achieve optimal outcomes, fostering open dialogue between advocates of change and preservation becomes essential, proactively addressing legal, ethical, and social challenges to harness the potential of generative artificial intelligence while safeguarding the rights of creators and nurturing human creativity.
Assessing the carbon footprint of soccer events through a lightweight CNN model utilizing transfer learning in the pursuit of carbon neutrality

Introduction Soccer events require a lot of energy, resulting in significant carbon emissions. To achieve carbon neutrality, it is crucial to reduce the cost and energy consumption of soccer events. However, current methods for cost minimization often have high equipment requirements, time-consuming training, and many parameters, making them unsuitable for real-world industrial scenarios. To address this issue, we propose a lightweight CNN model based on transfer learning to study cost minimization strategies for soccer events in a carbon-neutral context. Methods Our proposed lightweight CNN model uses a downsampling module based on the human brain for efficient information processing and a transfer learning-based module to speed up the training progress. We conducted experiments to evaluate the performance of our model and compared it with existing models in terms of the number of parameters and computation and recognition accuracy. Results The experimental results show that our proposed network model has significant advantages over existing models in terms of the number of parameters and computation while achieving higher recognition accuracy than conventional models. Our model effectively predicts soccer event data and proposes more reasonable strategies to optimize event costs and accelerate the realization of carbon neutral goals. Discussion Our proposed lightweight CNN model based on transfer learning is a promising method for studying cost minimization strategies for soccer events in a carbon-neutral context. The use of a downsampling module based on the human brain and a transfer learning-based module allows for more efficient information processing and faster training progress. The results of our experiments indicate that our model outperforms existing models and can effectively predict soccer event data and propose cost optimization strategies. Our model can contribute to the realization of carbon-neutral goals in the sports industry.
Optimal Coding of High-Cardinality Categorical Data in Machine Learning

Analyzing categorical data in machine learning generally requires a coding strategy. This problem is common to multivariate statistical techniques, and several approaches have been suggested in the literature. This article proposes a method for analyzing categorical variables with neural networks. Both a supervised and unsupervised approaches were considered, in which the variables can have high cardinality. Some simulated data applications illustrate the interest in the proposal.