Reviewerly/exHarmony huggingface dataset:
Practical Entropy Accumulation for Random Number Generators with Image Sensor-Based Quantum Noise Sources

The efficient generation of high-quality random numbers is essential in the operation of cryptographic modules. The quality of a random number generator is evaluated by the min-entropy of its entropy source. The typical method used to achieve high min-entropy of the output sequence is an entropy accumulation based on a hash function. This is grounded in the famous Leftover Hash Lemma, which guarantees a lower bound on the min-entropy of the output sequence. However, the hash function-based entropy accumulation has slow speed in general. For a practical perspective, we need a new efficient entropy accumulation with the theoretical background for the min-entropy of the output sequence. In this work, we obtain the theoretical bound for the min-entropy of the output random sequence through the very efficient entropy accumulation using only bitwise XOR operations, where the input sequences from the entropy source are independent. Moreover, we examine our theoretical results by applying them to the quantum random number generator that uses dark shot noise arising from image sensor pixels as its entropy source.
Optical and SAR Image Registration Based on Pseudo-SAR Image Generation Strategy

The registration of optical and SAR images has always been a challenging task due to the different imaging mechanisms of the corresponding sensors. To mitigate this difference, this paper proposes a registration algorithm based on a pseudo-SAR image generation strategy and an improved deep learning-based network. The method consists of two stages: a pseudo-SAR image generation strategy and an image registration network. In the pseudo-SAR image generation section, an improved Restormer network is used to convert optical images into pseudo-SAR images. An L2 loss function is adopted in the network, and the loss function fluctuates less at the optimal point, making it easier for the model to reach the fitting state. In the registration part, the ROEWA operator is used to construct the Harris scale space for pseudo-SAR and real SAR images, respectively, and each extreme point in the scale space is extracted and added to the keypoint set. The image patches around the keypoints are selected and fed into the network to obtain the feature descriptor. The pseudo-SAR and real SAR images are matched according to the descriptors, and outliers are removed by the RANSAC algorithm to obtain the final registration result. The proposed method is tested on a public dataset. The experimental analysis shows that the average value of NCM surpasses similar methods over 30%, and the average value of RMSE is lower than similar methods by more than 0.04. The results demonstrate that the proposed strategy is more robust than other state-of-the-art methods.
Poverty Traps in Online Knowledge-Based Peer-Production Communities

Online knowledge-based peer-production communities, like question and answer sites (Q&amp;A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new users to join. However, aging communities tend to build “poverty traps” that act as barriers for new users. In this paper, we present our investigation of 32 domain communities from Stack Exchange and our analysis of how different subjects impact the development of early user advantage. Our results raise important questions about the accessibility of knowledge-based peer-production communities. We consider the analysis results in the context of changing information needs and the relevance of Q&amp;A in the future. Our findings inform policy design for building more equitable knowledge-based peer-production communities and increasing the accessibility to existing ones.
CrossFormer: Multi‐scale cross‐attention for polyp segmentation

Abstract Colonoscopy is a common method for the early detection of colorectal cancer (CRC). The segmentation of colonoscopy imagery is valuable for examining the lesion. However, as colonic polyps have various sizes and shapes, and their morphological characteristics are similar to those of mucosa, it is difficult to segment them accurately. To address this, a novel neural network architecture called CrossFormer is proposed. CrossFormer combines cross‐attention and multi‐scale methods, which can achieve high‐precision automatic segmentation of the polyps. A multi‐scale cross‐attention module is proposed to enhance the ability to extract context information and learn different features. In addition, a novel channel enhancement module is used to focus on the useful channel information. The model is trained and tested on the Kvasir and CVC‐ClinicDB datasets. Experimental results show that the proposed model outperforms most existing polyps segmentation methods.
Entanglement swapping under global noise environment

Abstract We investigate the entanglement swapping under phase damping global noise and amplitude damping global noise. The two qubits the intermediate user holds during the entanglement swapping process first suffer from a global noise before the Bell measurement is carried out. Based on this assumption, the influence of global phase damping noise and global amplitude damping noise on the output entanglement of entanglement swapping is investigated, respectively. Our findings imply that the output entanglement is completely independent of the phase damping global noise and determined by the input entanglement when entanglement swapping is performed in a global phase damping noise environment. In addition, when entanglement swapping is executed in an amplitude damping environment, the output entanglement oscillates periodically with time, and the output entanglement may disappear at some points and reappear at others. In this case, we need not only to improve the input entanglement but also to control the evolution time of the amplitude damping global noise to ensure the optimal output entanglement. Our findings may help experimentalists to avoid and utilize environmental noise in entanglement swapping.
On Neural Networks Fitting, Compression, and Generalization Behavior via Information-Bottleneck-like Approaches

It is well-known that a neural network learning process-along with its connections to fitting, compression, and generalization-is not yet well understood. In this paper, we propose a novel approach to capturing such neural network dynamics using information-bottleneck-type techniques, involving the replacement of mutual information measures (which are notoriously difficult to estimate in high-dimensional spaces) by other more tractable ones, including (1) the minimum mean-squared error associated with the reconstruction of the network input data from some intermediate network representation and (2) the cross-entropy associated with a certain class label given some network representation. We then conducted an empirical study in order to ascertain how different network models, network learning algorithms, and datasets may affect the learning dynamics. Our experiments show that our proposed approach appears to be more reliable in comparison with classical information bottleneck ones in capturing network dynamics during both the training and testing phases. Our experiments also reveal that the fitting and compression phases exist regardless of the choice of activation function. Additionally, our findings suggest that model architectures, training algorithms, and datasets that lead to better generalization tend to exhibit more pronounced fitting and compression phases.
The Cryptocurrency Market and the Financial Stability

Abstract The fast development of the cryptocurrencies has brought to the attention of the authorities and researchers the importance of studying the risks associated with this category of assets. One of the main directions of analysis was the study of the correlations between the crypto-market and other traditional markets in order to assess the impact on financial stability. In the last 2-3 years, more and more studies have showed increasing correlations between the traditional markets and the crypto-market, which could generate some risks to the financial stability. We applied a novel methodology, based on TVP-VAR model, to study the correlations between Bitcoin and three other traditional assets, respectively gold, S&amp;P 500 and EUR/USD from 01/01/2015 to 01/01/2023. We proved that the correlations between traditional assets, such as equity (S&amp;P 500), respectively commodity (gold) and Bitcoin have increased significantly. However, other assets, such as the exchange rates are not correlated with cryptocurrencies and the correlations in the other way, from Bitcoin to gold, respectively S&amp;P 500 are still very low. Thus, our findings indicate that, at this moment, the crypto-market poses risks to the financial stability, but because of the fact that the correlations are still only unidirectional (from traditional assets to cryptocurrency), the crypto-market could now just amplify the risks to the financial stability originating from the traditional markets.
A text image generation model based on deep learning

The traditional text-image confrontation model utilizes a convolutional network in the discriminator to extract image features, yet this fails to involve the spatial relationship between underlying objects, resulting in a poor-quality generated image. To remedy this, a capsule network is proposed to improve the model. The convolutional network in the discriminator is replaced with a capsule network, thereby improving the robustness of the images. Through experiments on the Oxford-102 and CUB datasets, it has been found that the new model can effectively improve the quality of generated text-image. The FID value of the generated flower image decreased by 14.49%, and the FID value of the generated bird image decreased by 9.64%. Additionally, the Inception Score of images generated on the Oxford-102 and CUB datasets increased by 22.60% and 26.28%, respectively, indicating that the improved model generated richer and more meaningful image features.
Risk Management in the Electricity Market and the Transition to NET 0 via DLT (Blockchain Technology)

Abstract The Romanian energy market has undergone, since the beginning of 2017, a period of turmoil underlying a short-term price rise, simultaneously with the inability of some market traders to comply with the contractual liabilities to which they had committed. Overall, the end consumer was not affected, however a series of traders became insolvent, and losses were taken over by the system operator and the suppliers of last resort. This paper aims at exploring the manner how principles applied in the blockchain concept, which lately has become extremely popular due to virtual coins, particularly Bitcoin, may be useful to all participants of an OTC market. This new technology could allow the appearance of new solutions to monitor the energy source used to achieve the NET 0 objective. This article represents exclusively the author’s point of view and does not involve in any way the employer he is representing in the conference.
Some Emerging Trends in Cybersecurity Education and Training for Entrepreneurs

Abstract Development and intensive use of digitalization support entrepreneur work but face possible more and complex cyber-attacks on companies and private companies. Besides a sustainable cybersecurity infrastructure, an increasing entrepreneurs/employees/employers training to avoid complex cyber-attacks are necessary. In this paper, some approaches about cybersecurity strategy in companies, the contribution of suitable methods for training of entrepreneurs and an example of a project with the work of the author are given.
Counterfactuals as modal conditionals, and their probability

In this paper we propose a semantic analysis of Lewis' counterfactuals. By exploiting the structural properties of the recently introduced boolean algebras of conditionals, we show that counterfactuals can be expressed as formal combinations of a conditional object and a normal necessity modal operator. Specifically, we introduce a class of algebras that serve as modal expansions of boolean algebras of conditionals, together with their dual relational structures. Moreover, we show that Lewis' semantics based on sphere models can be reconstructed in this framework. As a consequence, we establish the soundness and completeness of a slightly stronger variant of Lewis' logic for counterfactuals with respect to our algebraic models. In the second part of the paper, we present a novel approach to the probability of counterfactuals showing that it aligns with the uncertainty degree assigned by a belief function, as per Dempster-Shafer theory, to its associated conditional formula. Furthermore, we characterize the probability of a counterfactual in terms of Gärdenfors' imaging rule for the probabilistic update.
Cartesian Coherent Differential Categories

We extend to general cartesian categories the idea of Coherent Differentiation recently introduced by Ehrhard in the setting of categorical models of Linear Logic. The first ingredient is a summability structure which induces a partial left-additive structure on the category. Additional functoriality and naturality assumptions on this summability structure implement a differential calculus which can also be presented in a formalism close to Blute, Cockett and Seely’s cartesian differential categories. We show that a simple term language equipped with a natural notion of differentiation can easily be interpreted in such a category.
Cut-Restriction: From Cuts to Analytic Cuts

Cut-elimination is the bedrock of proof theory with a multitude of applications from computational interpretations to proof analysis. It is also the starting point for important meta-theoretical investigations into decidability, complexity, disjunction property, interpolation, and more. Unfortunately cut-elimination does not hold for the sequent calculi of most non-classical logics. It is well-known that the key to applications is the subformula property (a typical consequence of cut-elimination) rather than cut-elimination itself. With this in mind, we introduce cut-restriction, a procedure to restrict arbitrary cuts to analytic cuts (when elimination is not possible). The algorithm applies to all sequent calculi satisfying language-independent and simple-to-check conditions, and it is obtained by adapting age-old cut-elimination. Our work encompasses existing results in a uniform way, subsumes Gentzen’s cut-elimination, and establishes new analytic cut properties.
A Model for Documenting Requirements Elicitation

Context: This work proposes a model to document the elicitation of requirements in the field of Requirements Engineering. Method: A systematic review of the literature was conducted to determine the validity and effectiveness of the existing models for documenting requirements elicitation. Results: By analyzing the results of this review, it was concluded that it is possible – and that is required – to take the best documented practices and add principles from logic, abstraction, and formal methods to them in order to structure a semi-formal model for documenting elicitation. Those currently proposed focus on techniques to collect information and pay little attention to documentation. In addition, these models are mainly based on natural language, which makes their interpretation difficult, and they generate re-processing in later stages of the life cycle due to ambiguities. Conclusions: This article describes a structured model, as well as its application and validation, by comparing it against five models found in the review.
Generative Adversarial Networks as a Data Augmentation Tool for Handwritten Digits

In the field of data processing, handwritten digit recognition (HDR) has proven to be of great use. However, due to the vast differences in how different people write, accurate recognition of such characters from images is a challenging job. The labelled samples necessary for supervised learning methods are not always easy to come by. For instance, a lot of labelled examples are needed to train a model in deep learning approaches, where all the feature extraction steps are learned within the artificial neural network. To get around this problem, data augmentation methods can be used to fill in the gaps using variations in an example's label that are already known. The Generative Adversarial Network (GAN) is able to generate random samples from the latent space that are statistically indistinguishable from the training set's actual examples. In this study, we leverage the powerful features of GAN to learn from the MNIST data set and produce digital images of handwriting.
Estimating the Effectiveness of CNN and Fuzzy Logic for Signature Verification

Each individual has a distinctive signature which is primarily used for personal authentication as well as the confirmation of significant papers or formal processes. A Signature, a form of bio-metric, is a method of authenticating the identity of an individual, thus, helping manage security. This reduces the possibility of forgery during the authentication process, and aids in avoiding any discrepancies during the signing process. But for a huge number of files, offline signature-based detection is ineffective and sluggish. Herein comes the need for automated signature verification systems. These systems aim at verifying whether a given signature image is real or fake. This paper provides a comprehensive survey of literature on various techniques used for handwriting verification including signatures. The respective findings of various methods have been discussed, highlighting the latest trends of research and their potential applications. The paper aims to present state-of-the-art methods for future development thus, serving as a helpful means for researchers carrying out study in signature verification and related topics.
An International Blended Learning Program Between Europe and Asia for One Year On

A year on, a post-test of TPACK is submitted to analyze the increasing use of technology integration to an international learning community. In this chapter, the authors aim to check how the participants elated to the project have consolidated the skills learned in an international blended learning project -done before COVID-19, in light of the lived pandemic experiences. The research question is: How does an international blended learning program impact teacher-educators professional development? What about the long-term impact of the program after one year on? Some statistical analyses are performed and discussed.
Sense of Self-Efficacy in Digital Skills During Teacher Training

This chapter presents the results of a study of teacher education students' sense of self-efficacy in digital skills based on two almost identical questionnaires distributed before and after lockdown imposed due to the COVID-19 pandemic. Results obtained in 2019 are compared with those of 2021-22 in order to understand how students' digital skills, digital technology representations, and digital practices have evolved during their studies at the University of Teacher Education (States of Bern, Jura and Neuchâtel), Switzerland. These results show students to have globally more positive representations of digital use, an unchanged sense of self-efficacy and a real need for digital use modelling in training to facilitate digital use in future classroom practice.
Key Vulnerabilities in SSL &amp; TLS That Needs To Be Mitigated

The cryptographic security protocols are designed to ensure that the information transmission that is taking place between two entities is secured and is not impacted while interaction occurs among them. Security protocols are a set of rules that are formulated to ensure that the intruder or third party is not able to access the information that is transmitted. In this context, one of the algorithms that could be used is the RC4 algorithm. There are many discussions about the RC4 algorithm, but as of now is only theoretically dominant. It is a fact that the theory alone cannot complete the RCA algorithm process in a detailed manner. The applications reveal the input and do not produce any output of the processes involved. This research paper will illustrate the vulnerability and possible solutions to avoid the RC4 Algorithm attack.
Cross-Domain Requirements Linking via Adversarial-based Domain Adaptation

Requirements linking is the core of software system maintenance and evolution, and it is critical to assuring software quality. In practice, however, the requirements links are frequently absent or incorrectly labeled, and reconstructing such ties is time-consuming and error-prone. Numerous learning-based approaches have been put forth to address the problem. However, these approaches will lose effectiveness for the Cold-Start projects with few labeled samples. To this end, we propose RADIATION, an adversarial-based domain adaptation approach for cross-domain requirements linking. Generally, RADIATION firstly adopts an IDF-based Masking strategy to filter the domain-specific features. Then it pre-trains a linking model in the source domain with sufficient labeled samples and adapts the model to target domains using a distance-enhanced adversarial technique without using any labeled target samples. Evaluation on five public datasets shows that RADIATION could achieve 66.4% precision, 89.2% recall, and significantly outperform state-of-the-art baselines by 13.4% -42.9% F1. In addition, the designed components, i.e., IDF-based Masking and Distance-enhanced Loss, could significantly improve performance.
Usability-Oriented Design of Liquid Types for Java

Developers want to detect bugs as early in the development lifecycle as possible, as the effort and cost to fix them increases with the incremental development of features. Ultimately, bugs that are only found in production can have catastrophic consequences. Type systems are effective at detecting many classes of bugs during development, often providing immediate feedback both at compile-time and while typing due to editor integration. Unfortunately, more powerful static and dynamic analysis tools do not have the same success due to providing false positives, not being immediate, or not being integrated into the language. Liquid Types extend the language type system with predicates, augmenting the classes of bugs that the compiler or IDE can catch compared to the simpler type systems available in mainstream programming languages. However, previous implementations of Liquid Types have not used human-centered methods for designing or evaluating their extensions. Therefore, this paper investigates how Liquid Types can be integrated into a mainstream programming language, Java, by proposing a new design that aims to lower the barriers to entry and adapts to problems that Java developers commonly encounter at runtime. Following a participatory design methodology, we conducted a developer survey to design the syntax of LiquidJava, our prototype. To evaluate if the added effort to writing Liquid Types in Java would convince users to adopt them, we conducted a user study with 30 Java developers. The results show that LiquidJava helped users detect and fix more bugs and that Liquid Types are easy to interpret and learn with few resources. At the end of the study, all users reported interest in adopting LiquidJava for their projects.