Reviewerly/exHarmony huggingface dataset:
Provable security against generic attacks on stream ciphers

Abstract Recent lightweight hardware-based stream cipher designs keep an external non-volatile internal state that is not part of the cipher’s hardware module. The purpose of these so-called small-state ciphers is to keep the size of the hardware and the power consumption low. We propose a random oracle model for stream ciphers. This will allow us to analyse the recent small-state stream cipher designs’ resistance against generic attacks and, in particular, time-memory-data tradeoff attacks. We analyse the conventional construction underlying stream ciphers like Grain and Trivium, constructions continuously using the external non-volatile secret key during keystream generation like Sprout, Plantlet, Fruit, and Atom, constructions continuously using the external non-volatile IV, and constructions using a combination of the IV and the key like DRACO. We show the tightness of all bounds by first presenting the time-memory-data tradeoff attacks on the respective constructions, establishing the upper bound on security, and then presenting the proof of security to establish the lower bound on security. In this work, we extend the theoretical work done by Hamann et al. who introduced the DRACO stream cipher at FSE 2023. We use the same random oracle model as the aforementioned work and apply it to the earlier work by Hamann et al. presented at SAC 2019, which showed security for two of the four constructions we consider in this work. Our model is equivalent but allows for a much simpler proof of security. Furthermore, we provide a proof of security for stream ciphers continuously using the secret key during keystream generation, giving upper and lower bounds for all four generic stream cipher constructions proposed so far.
Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes

Nowadays, due to their excellent prediction capabilities, the use of artificial neural networks (ANNs) in software has significantly increased. One of the most important aspects of ANNs is robustness. Most existing studies on robustness focus on adversarial attacks and complete redundancy schemes in ANNs. Such redundancy methods for robustness are not easily applicable in modern embedded systems. This work presents a study, based on simulations, about the robustness of ANNs used for prediction purposes based on weight alterations. We devise a method to increase the robustness of ANNs directly from ANN characteristics. By using this method, only the most important neurons/connections are replicated, keeping the additional hardware overheads to a minimum. For implementation and evaluation purposes, the networks-on-chip (NoC) case, which is the next generation of system-on-chip, was used as a case study. The proposed study/method was validated using simulations and can be used for larger and different types of networks and hardware due to its scalable nature. The simulation results obtained using different PARSEC (Princeton Application Repository for Shared-Memory Computers) benchmark suite traffic show that a high level of robustness can be achieved with minimum hardware requirements in comparison to other works.
Underwater Image Enhancement Method Based on Improved GAN and Physical Model

Underwater vision technology is of great significance in marine investigation. However, the complex underwater environment leads to some problems, such as color deviation and high noise. Therefore, underwater image enhancement has been a focus of the research community. In this paper, a new underwater image enhancement method is proposed based on a generative adversarial network (GAN). We embedded the channel attention mechanism into U-Net to improve the feature utilization performance of the network and used the generator to estimate the parameters of the simplified underwater physical model. At the same time, the adversarial loss, the perceptual loss, and the global loss were fused to train the model. The effectiveness of the proposed method was verified by using four image evaluation metrics on two publicly available underwater image datasets. In addition, we compared the proposed method with some advanced underwater image enhancement algorithms under the same experimental conditions. The experimental results showed that the proposed method demonstrated superiority in terms of image color correction and image noise suppression. In addition, the proposed method was competitive in real-time processing speed.
Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network

Matching remotely sensed multimodal images is a crucial process that poses significant challenges due to nonlinear radiometric differences and substantial image noise. To overcome these difficulties, this study presents a novel and practical template-matching algorithm specifically designed for this purpose. Unlike traditional approaches that rely on image intensity, the proposed algorithm focuses on matching multimodal images based on their geometric structure information. This approach enables the method to effectively adapt to variations in grayscale caused by radiometric differences. To enhance the matching performance, principal component analysis calculation based on the log-Gabor filter is proposed to estimate the structural feature of the image. The proposed method can estimate the structure feature accurately even under severe noise distortion. In addition, a learnable matching network is proposed for similarity measuring to adapt to the gradient reversal caused by the radiometric difference among remotely sensed multimodal images. Infrared, visible light, and synthetic aperture radar images are adopted for the evaluation, to verify the performance of the proposed algorithm. Based on the results, the proposed algorithm has a distinct advantage over other state-of-the-art template-matching algorithms.
Indefinite causality in quantum mechanics and its thermodynamic applications

Abstract The nature of causality remains one of the key puzzles in science. In quantum theory, the causal structure is not subject to quantum uncertainty and plays rather a background role. One can ask whether the background causal structure can be dropped, for example, by respecting causality only locally. Such scenarios of local validity of quantum theory while relaxing the global definite causal order of operations can be described via the machinery of process matrices. An important example of scenarios of this kind is quantum SWITCH, a process realizing a quantum superposition of causal orders of operations. Looking for the possible applications of quantum SWITCH has been the subject of growing interest in the scientific community as it could provide communication and computational resources not realizable via standard quantum theory. In the last few years, the benefits potentially offered by quantum SWITCH for thermodynamic tasks have appeared in the spotlight. This contribution aims at reviewing the recent proposals of thermodynamic applications of quantum SWITCH and draw the perspectives.
Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

Technical debt refers to decisions made during the design and development of software that postpone the resolution of technical problems or the enhancement of the software’s features to a later date. If not properly managed, technical debt can put long-term software quality and maintainability at risk. Self-admitted technical debt is defined as the addition of specific comments to source code as a result of conscious and deliberate decisions to accumulate technical debt. In this paper, we will look at the presence of self-admitted technical debt in open-source blockchain projects, which are characterized by the use of a relatively novel technology and the need to generate trust. The self-admitted technical debt was analyzed using NLP techniques for the classification of comments extracted from the source code of ten projects chosen based on capitalization and popularity. The analysis of self-admitted technical debt in blockchain projects was compared with the results of previous non-blockchain open-source project analyses. The findings show that self-admitted design technical debt outnumbers requirement technical debt in blockchain projects. The analysis discovered that some projects had a low percentage of self-admitted technical debt in the comments but a high percentage of source code files with debt. In addition, self-admitted technical debt is on average more prevalent in blockchain projects and more equally distributed than in reference Java projects.If not managed, the relatively high presence of detected technical debt in blockchain projects could represent a threat to the needed trust between the blockchain system and the users. Blockchain projects development teams could benefit from self-admitted technical debt detection for targeted technical debt management.
HeteEdgeWalk: A Heterogeneous Edge Memory Random Walk for Heterogeneous Information Network Embedding

Most Heterogeneous Information Network (HIN) embedding methods use meta-paths to guide random walks to sample from HIN and perform representation learning in order to overcome the bias of traditional random walks that are more biased towards high-order nodes. Their performance depends on the suitability of the generated meta-paths for the current HIN. The definition of meta-paths requires domain expertise, which makes the results overly dependent on the meta-paths. Moreover, it is difficult to represent the structure of complex HIN with a single meta-path. In a meta-path guided random walk, some of the heterogeneous structures (e.g., node type(s)) are not among the node types specified by the meta-path, making this heterogeneous information ignored. In this paper, HeteEdgeWalk, a solution method that does not involve meta-paths, is proposed. We design a dynamically adjusted bidirectional edge-sampling walk strategy. Specifically, edge sampling and the storage of recently selected edge types are used to better sample the network structure in a more balanced and comprehensive way. Finally, node classification and clustering experiments are performed on four real HINs with in-depth analysis. The results show a maximum performance improvement of 2% in node classification and at least 0.6% in clustering compared to baselines. This demonstrates the superiority of the method to effectively capture semantic information from HINs.
On Combining Wavefront and Tile Parallelism with a Novel GPU-Friendly Fast Search

As the necessity of supporting ever-increasing demands in video resolution leads to new video coding standards, the challenge of harnessing their computational overhead becomes important. Such overhead stems not only from the increased image data due to higher resolutions but also from the coding techniques per se that are introduced by each standard to improve compression. All modern standards in the field of video coding offer high compression efficiency, but this is achieved by increasing the computational complexity of the encoding part. Ultra-High-Definition (UHD) videos, bring new encoding implementation schemes that are being recommended for CPU and GPU parallelization. Therefore, several works are published to achieve better performance and reduce encoding complexity. Following this idea, we proposed and evaluated a hybrid encoding scheme that utilizes the constant growth of the CPU power with the massive GPU popularity in parallel. Taking advantage of the encoding schemes from the leading video coding standards, such as High-Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC), which support parallel processing thru Wavefront or Tiling, in our work, we combined both of them at the same time as a whole, and in addition, we introduced a GPU-friendly fast search algorithm that is highly parallel and alternative to the default non-parallel TZ-Search. Through an experimental evaluation with common test sequences, the proposed GPU Fast Motion Estimation with our previous Wavefront per Tile Parallelism (WTP) was shown to provide valid trade-off between speedup and video coding efficiency, effectively combining the best of two worlds, i.e., WTP using CPUs and parallel Motion Estimation with GPUs.
BERT (Bidirectional Encoder Representations from Transformers) for Missing Data Imputation in Solar Irradiance Time Series

The availability of solar irradiance time series without missing data is an ideal scenario for researchers in the field. However, it is not achievable for a variety of reasons, such as measurement errors, sampling gaps, or other factors. Time series imputation methods can be a solution to the lack of data and, in this paper, we study the applicability of Bidirectional Encoder Representations from Transformers (BERT) as an irradiance time series imputation solution. In this regard, a BERT model was trained from scratch for the masked language modelling (MLM) task, and the quality of the imputation was evaluated according to the number of missing values and the position within the series. The experiments were conducted over a dataset of 165 stations, captured by meteorological stations distributed over the Spanish regions of Galicia, Castile, and León. In the evaluation process, an average coefficient of determination (R2 score) of 0.89% was obtained, the maximum result being 0.95%.
A Hybrid Machine Learning Model for Solar Power Forecasting

The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy.
Simple multiple kernel k-means with kernel weight regularization

Multiple kernel clustering (MKC) aims to determine the optimal kernel from several pre-computed basic kernels. Most of MKC algorithms follow a common assumption that the optimal kernel is linearly combined by basic kernels. Based on a min–max framework, a newly proposed MKC method termed simple multiple kernel k-means can acquire a high-quality unified kernel. Although it has achieved promising clustering performance, we have observed that it cannot benefit from any regularization or prior knowledge, thus the learned kernel weight coefficients may be seriously sparse or over-selected. To tackle this issue, we have developed a new algorithm termed simple multiple kernel k-means with kernel weight regularization (SMKKM-KWR), where we introduce average coefficients to avoid too sparse kernel weights. Specially, we add the average kernel coefficients as a regularization term to prevent the learned weight coefficients being far from the average values. After that, an efficient optimization strategy is proposed to solve the new resultant problem. By this way, the unified partition can learn clustering structure from fusion information of all the kernel views, with the goal to reach better clustering performance. Extensive experiments on nine benchmark datasets and four large-scale datasets have demonstrated the effectiveness and efficiency of the proposed algorithm.
Hybrid intelligent deep learning model for solar radiation forecasting using optimal variational mode decomposition and evolutionary deep belief network - Online sequential extreme learning machine

Accurate prediction of solar radiation is of great significance to improve the utilization of solar energy for photovoltaic power generation on the roofs of urban buildings. Therefore, a hybrid model, namely the OVMD-PACF-ISSA-DBN-OSELM model, is being proposed in this study for solar radiation prediction. Firstly, the data is decomposed by optimal variational mode decomposition (OVMD) technology. Secondly, the partial autocorrelation function (PACF) technology selected features for components decomposed by OVMD technology. Thirdly, a deep belief network-online sequential extreme learning machine (DBN-OSELM) prediction model is constructed, and sparrow search algorithm (SSA) is utilized to optimize the model hyperparameters. Aiming at the shortcomings of SSA algorithm, Tent map and differential evolution strategy are introduced to improve the SSA algorithm. Finally, the model is used to predict each component, and the prediction results of each component are summarized to obtain the prediction results of solar radiation. To verify that the proposed method can obtain accurate prediction results, experiments are conducted using solar radiation data for January, May and October 2022, and compared with other models. Taking January as an example, compared with shallow model BP, the RMSE, MAE and R of the proposed model are increased by 66.83%, 58.65% and 5.33%, respectively. Moreover, the experiments of the proposed model in other months have excellent results. The current findings demonstrate that the suggested model is a strong contender in the arena of solar radiation prediction, and can consequently provide valuable support in enhancing the efficiency of photovoltaic equipment installed on urban building roofs.
Model-driven self-aware self-training framework for label noise-tolerant medical image segmentation

The label noise-tolerant deep learning methods have been exploited extensively in pattern classification tasks, but still less studied in image segmentation tasks. Especially, the segmentation annotations are prone to suffering from inter-rater variability and subjectivity in medical image segmentation tasks. The study of the label noise-tolerant learning with corrupted label is an important and active topic. In this paper, we propose a model-driven self-aware self-training segmentation framework for label noise-tolerant learning. “Model-driven” means that we propose a model state estimation method to discover the early learning time from noisy labels in different segmentation tasks. Second, we propose a pseudo label generation method to modify the training process with noisy annotations. The pseudo label generation is based on the prototypical feature learning without extra parameters. This method exploits the similarity and consistency from the pixel-wise feature to the global prototypes, which can effectively remedy the incorrect labels, thus called “self-aware”. Further, we introduce the self-training with the generated pseudo label to boost robustness against noisy labels. Extensive experiments on three public datasets demonstrate that our method outperforms other popular anti-noise segmentation methods, and has shown effectiveness and stability when facing with different kinds of label noises. Our method improves the direct training by 5.80% Dsc on PROMISE12 dataset under annotations with 75% noise ratio.
Cluster weighted model based on TSNE algorithm for high-dimensional data

Cluster-weighted models (CWMs) are an important class of machine learning models that are commonly used for modelling complex datasets. However, they are known to suffer from reduced computing efficiency and estimator accuracy when dealing with high-dimensional data. Previous work has proposed a parsimonious technique that can improve CWMs’ performance in the high-dimensional data paradigm. However, this method has a setback for very high-dimensional data, where the dimensionality is greater than 100. In this paper, we propose a new hybridised method that incorporates a dimensionality reduction technique called T-distributed stochastic neighbour embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Additionally, we introduce a novel heuristic for detecting the hidden components of the underlying mixture model, which can be used with the popular R package FlexCWM. We evaluated the performance of the proposed method using two real datasets and found that it improves clustering power when compared to both the parsimony methods and the TSNE methods combined with CWMs in the high-dimensional data setting. Our results suggest that the proposed method can improve the efficiency and accuracy of CWMs in dealing with high-dimensional data, making it a valuable tool for data scientists and statisticians.
Study the charging process of moving quantum batteries inside cavity

In quantum mechanics, quantum batteries are devices that can store energy by utilizing the principles of quantum mechanics. While quantum batteries has been investigated largely theoretical, recent research indicates that it may be possible to implement such a device using existing technologies. The environment plays an important role in the charging of quantum batteries. If a strong coupling exists between the environment and the battery, then battery can be charged properly. It has also been demonstrated that quantum battery can be charged even in weak coupling regime just by choosing a suitable initial state for battery and charger. In this study, we investigate the charging process of open quantum batteries mediated by a common dissipative environment. We will consider a wireless-like charging scenario, where there is no external power and direct interaction between charger and battery. Moreover, we consider the case in which the battery and charger move inside the environment with a particular speed. Our results demonstrate that the movement of the quantum battery inside the environment has a negative effect on the performance of the quantum batteries during the charging process. It is also shown that the non-Markovian environment has a positive effect on improving battery performance.
Methods of Machine Learning

Machine learning is a kind of artificial intelligence that allows computers to teach themselves new skills and improve their performance based on what they've seen before. Machine learning is a collection of algorithms designed to process massive amounts of information. Algorithms are trained using data, and then use that training to create a model and carry out a task.
Variants of Genetic Algorithms and Their Applications

The objective of this chapter is to present a clear understanding of Genetic Algorithms and their application in advancing intelligent systems. It offers insight into the methodology of genetic algorithms and how it is employed to solve numerous engineering problems. It establishes a connection to eliminate the gap between textbooks outlining genetic algorithm methodology and more traditional books focused on genetic algorithm research. Genetic algorithm (GA), which is a subclass of the larger class of evolutionary algorithms (EA) in computer science and operations research, is a metaheuristic that takes its cues from the process of natural selection. Genetic algorithms frequently employ biologically influenced operators such as selection, mutation, and crossover to generate high-quality solutions to solve optimization and search problems. Hence, these algorithms can successfully generate solutions to extremely complex problems and it's important to analyze the behavior of such problems to understand the importance of Genetic Algorithms. Several real-world uses for genetic algorithms in industries include manufacturing, engineering design, financial marketing, wireless sensor networks, and medical imaging. Such complex algorithms attract keen interest from people belonging to fields such as engineers, mathematicians, and computer scientists as well as niche fields such as biomedical engineering. Understanding Genetic Algorithms will provide insights into the working of algorithms in general along with explaining how solutions are obtained by these algorithms.