Reviewerly/exHarmony huggingface dataset:
Federated Heuristic Optimization Based on Fuzzy Clustering and Red Fox Optimization Algorithm

Heuristic algorithms are dependent on many coefficients like the number of iterations or individuals. However, quite often these algorithms move individuals toward the best in the population. Based on this observation, we propose the idea of federated heuristics. The proposed idea is to initially distribute individuals into certain intervals. Then, it performs a specified number of iterations of the algorithm to identify the potentially best intervals. Sorted intervals (in relation to the best-adapted individual) make it possible to separate the appropriate size of the population in each of them. Moreover, these clusters are merged by a fuzzy algorithm due to a decrease in their numbers. The more significant the interval, the greater the number of individuals and iterations allocated in these areas. As a consequence, several instances of the selected heuristic algorithm are triggered, which can divide the best individual. The proposed technique was described using the red fox algorithm and tested at a classic set of functions with different parameters of the used heuristic.
Incremental Cluster Validity Index for Predicting Early Signs of Change in Data Streams

In this paper, we propose an incremental version of the SD cluster validity index for streaming data monitoring and predicting early signs of change. The proposed incremental SD (iSD) is used to monitor the data stream along with the MU Streaming Clustering (MUSC) algorithm. We investigate the use of iSD for detecting early signs of changes in multiple data streams arriving at the same time. Synthetic and real-life datasets are used in the analysis to demonstrate the effectiveness of the proposed index in detecting early signs of changes in the data stream. Valuable information about the streaming data can be directly captured from the index values such as the appearance of new patterns, and cluster size based on the analysis of outliers. The performance of iSD is compared with the incremental Davies-Boudin index (iDB). iSD has larger values which makes it more robust in monitoring large data streams compared to iDB which tends to flatten over time and approaches zero.
An Application for Federated Learning of XAI Models in Edge Computing Environments

The next generation of wireless networks will feature an increasing number of connected devices, which will produce an unprecedented volume of data. Knowledge extraction from decentralized data imposes the exploitation of computing and learning paradigms able to tame the complexity of the network and meet the growing requirement of trustworthiness. In this regard, edge computing overcomes the limitations of cloud computing by moving virtualized computing and storage resources closer to data sources. Furthermore, Federated Learning has been recently proposed as a means to let multiple parties collaboratively train an ML model without disclosing private data. In this paper, we propose an application that enables Federated Learning of eXplainable AI models (Fed-XAI) in an edge computing environment. The proposal represents a step forward towards the adoption of trustworthy AI in next generation wireless networks, ensuring both privacy preservation and explainability. The application components are described, along with the workflow for the training and inference stages. Finally, we discuss the application deployment, in a simulated setting, for addressing a task of video streaming Quality of Experience forecasting in a vehicular network case study.
The Invisible Solutions of the Rubik’s Cube

The center cubies on the Rubik's Cube can change orientation when the puzzle is brought from one solved state to another. The set of all possible reorientations of the center cubies creates what we call the invisible solutions group. We investigate the size and structure of the invisible solutions group for the Rubik's Cube, Rubik's Revenge, and the Professor's Cube.
A Sample-Aware Database Tuning System With Deep Reinforcement Learning

Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the recommended parameters of the algorithm. Meanwhile, they propose an improved closed-loop distributed comprehensive training architecture of online and offline training to quickly obtain high-quality samples and improve the efficiency of parameter tuning. Experimental results show that the configuration parameters can make the performance of the database system better and shorten the tuning time.
Generative AI in Finance: Risks and Potential Solutions

Generative Artificial Intelligence (AI) captured the attention of academics, policymakers, the private sector and some regulators in 2022 after the launch of ChatGPT and its widespread adoption worldwide. Then, in 2023, Microsoft Chairman and Chief Executive Officer Satya Nadella said during the World Economic Forum session that the ‘golden age’ of AI is underway and generative AI plays a big role in it. Accordingly, OpenAI, the company behind the launch of ChatGPT, received multibillion dollar investments from Microsoft. Additionally, Google and Meta created their generative AI models. Given the multiplicity of potential use cases of generative AI models, not only the tech industry is interested in this development. Many industries were caught in the hype of generative AI including the financial sector. Indeed, financial institutions worldwide are deploying generative AI and are starting with internal uses. However, along with that comes potential pitfalls and challenges. This paper argues that addressing the risks and challenges of generative AI requires a context and sector-specific debate. The challenges of using AI highly depend on the context where the models are implemented and used. Namely, generating content to provide financial advice has different implications for society than generating the image of a turtle. Hence, in the current debate about AI governance, and specifically in the generative AI space, it is important to analyse the risks of generative AI in finance. This paper is a first attempt to map the ethical and legal risks of generative AI in the financial sector given than previous scholarship and most policy discussions have focused on the risks of predictive AI in the financial sector. Based on this analysis and following a functional approach, the paper also proposes an avenue to deal with the risks created by generative AI in finance.
Underwater image recovery based on water type estimation and adaptive color correction

In underwater environments, light propagation is affected by scattering and absorption, leading to color distortion and quality degradation of underwater images. In addition, the presence of a color cast in the image and variations in the attenuation coefficients across various water bodies bring great challenges for underwater image restoration. In this paper, an underwater image restoration method based on water body type estimation and adaptive color correction is proposed. Initially, the underwater images are categorized into color casts and non-color casts according to their hue, and a water body type estimation method based on image color and blurriness is introduced for improving the accuracy of transmission map estimation. Following this, we performed adaptive color correction on the image using a nonlinear transformation, which effectively eliminates color cast. Then the background light position is corrected using the degree of color cast of the image to restore the hue and brightness of the image more naturally. Ultimately, the acquired background light and transmission map are utilized to generate clear underwater images using the image formation model (IFM). Experiments on the widely used UIEB benchmark and SUID datasets show that our method effectively solves the problems of image color distortion and quality degradation, generating satisfactory visual effects.
Radar Translation Network Between Sunny and Rainy Domains by Combination of KP-Convolution and CycleGAN

Recently, research on autonomous driving has focused on the advent of various deep learning algorithms. The main sensors for autonomous driving include cameras, LiDAR, and radar, but these algorithms primarily focus on image and LiDAR data. This is because radar data is limited compared to image and LiDAR data. To address the lack of data problem, GAN-based translation methods have been proposed. However, these methods also focus only on image and LiDAR data, such as day-to-night translation or sunny-to-adverse weather translation. Since radar data differs depending on radar sensors and radar points are too sparse to learn patterns compared to LiDAR, translation with radar data is a challenging task. Radar is usually utilized as a sensor that is nearly unaffected by the weather. However, it has been confirmed through JARI data collected by us that rain has a negative effect. CycleGAN is useful for data translation in traffic scenes where pair data is difficult to acquire, since CycleGAN is a network specialized in style translation. KP-Convolution is a module specialized in feature extraction of points while maintaining location information. Therefore, we propose a radar translation network between sunny and rainy domains by combining KP-Convolution and CycleGAN. In this process, we address the adverse effects of radar data by rain, establishing the training format of radar data, KP-Convolution which can learn patterns despite a small number of points, and CycleGAN which is the basis of the translation method.
FABNet: Frequency-Aware Binarized Network for Single Image Super-Resolution

Remarkable achievements have been obtained with binary neural networks (BNN) in real-time and energy-efficient single-image super-resolution (SISR) methods. However, existing approaches often adopt the Sign function to quantize image features while ignoring the influence of image spatial frequency. We argue that we can minimize the quantization error by considering different spatial frequency components. To achieve this, we propose a frequency-aware binarized network (FABNet) for single image super-resolution. First, we leverage the wavelet transformation to decompose the features into low-frequency and high-frequency components and then employ a "divide-and-conquer" strategy to separately process them with well-designed binary network structures. Additionally, we introduce a dynamic binarization process that incorporates learned-threshold binarization during forward propagation and dynamic approximation during backward propagation, effectively addressing the diverse spatial frequency information. Compared to existing methods, our approach is effective in reducing quantization error and recovering image textures. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed methods could surpass state-of-the-art approaches in terms of PSNR and visual quality with significantly reduced computational costs. Our codes are available at https://github.com/xrjiang527/FABNet-PyTorch.
POCE: Pose-Controllable Expression Editing

Facial expression editing has attracted increasing attention with the advance of deep neural networks in recent years. However, most existing methods suffer from compromised editing fidelity and limited usability as they either ignore pose variations (unrealistic editing) or require paired training data (not easy to collect) for pose controls. This paper presents POCE, an innovative pose-controllable expression editing network that can generate realistic facial expressions and head poses simultaneously with just unpaired training images. POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately. POCE has two novel designs. The first is self-supervised UV completion that allows to complete UV maps sampled under different head poses, which often suffer from self-occlusions and missing facial texture. The second is weakly-supervised UV editing that allows to generate new facial expressions with minimal modification of facial identity, where the synthesized expression could be controlled by either an expression label or directly transplanted from a reference UV map via feature transfer. Extensive experiments show that POCE can learn from unpaired face images effectively, and the learned model can generate realistic and high-fidelity facial expressions under various new poses.
A Trainable Approach to Zero-Delay Smoothing Spline Interpolation

The task of reconstructing smooth signals from streamed data in the form of signal samples arises in various applications. This work addresses such a task subject to a zero-delay response; that is, the smooth signal must be reconstructed sequentially as soon as a data sample is available and without having access to subsequent data. State-of-the-art approaches solve this problem by interpolating consecutive data samples using splines. Here, each interpolation step yields a piece that ensures a smooth signal reconstruction while minimizing a cost metric, typically a weighted sum between the squared residual and a derivative-based measure of smoothness. As a result, a zero-delay interpolation is achieved in exchange for an almost certainly higher cumulative cost as compared to interpolating all data samples together. This paper presents a novel approach to further reduce this cumulative cost on average. First, we formulate a zero-delay smoothing spline interpolation problem from a sequential decision-making perspective, allowing us to model the future impact of each interpolated piece on the average cumulative cost. Then, an interpolation method is proposed to exploit the temporal dependencies between the streamed data samples. Our method is assisted by a recurrent neural network and accordingly trained to reduce the accumulated cost on average over a set of example data samples collected from the same signal source generating the signal to be reconstructed. Finally, we present extensive experimental results for synthetic and real data showing how our approach outperforms the abovementioned state-of-the-art.
A Real-Time Image Encryption Algorithm for Distributed Energy System based on the 13D Complex Chaotic Sequence

In distribution energy systems (DESs), the information security plays an essential role. Real-time encryption techniques can guarantee the transmission rate and confidentiality simultaneously. We propose a novel real-time encryption algorithm for DESs. First, we construct a 13D chaotic system based on quaternion, Chua’s circuit and complex variables, and discuss the existence of the attractors and other chaotic characteristics. Secondly, based on the proposed complex chaotic system, a simple encryption algorithm is proposed to ensure the confidentiality of DESs. Finally, our algorithm is verified by the standard test image and monitoring pictures in DESs. The execution time of the proposed algorithm is 0.0035s and 0.0021s respectively. It is only one percent of other algorithms. At the same time, our other security performances are competitive with other algorithms. And it has the ability to resist the malicious attack and avoid unnecessary equipment shutdown in DESs.
Artificial Intelligence Cannibalism and the Law

Lawyers are already using - and misusing - large language models like ChatGPT in their daily lives as they practice law. Despite recent headlines pointing out the very real downsides of misuse of the technology, it is all but certain that lawyers will use LLMs with increasing frequency more in the coming years.4 Indeed, many law schools, recognizing that lawyers need to understand LLMs, are scrambling to train students on best practices. But LLMs are racing toward a cliff that could severely undercut their usefulness to lawyers, and potentially even stifle the development of law itself.As news articles, blog posts, and even works of fiction generated by AI make up more and more of the internet, those outputs will form an ever larger share of the data training sets of future LLMs. Recent studies suggest this is potentially catastrophic for the model’s stability and could result in more misinformation and increasing “AI hallucinations.” Such a result would lessen the utility of these tools for lawyers, and could even have profound impacts on the development of law. Further, if lawyers are overly reliant on generative AI in their practice, they risk undermining their own creative processes and further stunting the development of law.
Activity Theory as a Lens for Developing and Applying Personas and Scenarios in Learning Experience Design

Theoretically-informed design is a hallmark of the field of learning and instructional design and technology (LIDT). Designing digital environments for learning on the basis of theory can lead to theoretically pure and potentially effective learning interventions, yet theory alone is insufficient to consider the myriad of issues that emerge while a learner is engaged in digitally mediated learning. As the field of LIDT shifts towards more human-centered design practice, the phenomenon of learning experience design (LXD) has emerged as a novel, multidisciplinary focus area. LXD equips designers with a range of useful methods for explicitly considering the learner within the learning context. Two methods that we argue are particularly well-suited for this are personas and scenarios. The development of personas and scenarios can be informed by activity theory, which provides a lens for holistically considering the technology usage context and the learner’s role therein. The current article discusses the interplay of activity theory, personas, and scenarios, and illustrates how this can be potentially useful in learning experience design practice in two separate case examples. Implications are discussed.
Restructuring university language programs for the integration of digital literacy competence

The paper explores the possibilities of restructuring university language programs to integrate digital literacy skills in the context of rapid digital transformation. The proposed perspectives emphasize a synergy between traditional language program competencies and digital literacy skills, achievable only through the adoption of competency-based approaches, professionalization, needs analysis within the framework of instructional design, and a curriculum guided by digital analysis levels.
The language’s metaphor of anger in Toba Batak

Every human being in the world has experienced emotions. Emotions are natural reactions or feelings directed at someone. Emotions are basically closely related to a person’s perspective on thinking and doing something. Metaphor consists of a fabric of words resulting from human activity. To review human expression, metaphors also play a role in describing human mental processes. Humans conceptualize and manifest their behavior and understanding through metaphorical expressions. This study explores and explains (1) the shape or form of Toba Batak metaphors and (2) the meaning of Batak language metaphors in supporting the strengthening of the national integration process. This research is descriptive qualitative by conducting interviews on the use of angry metaphors in Batak culture in everyday life, as well as documentary and book data, aimed at describing the meaning of angry metaphors in Toba Batak language. This research was started by collecting vocabulary dealing with verbs showing anger in Toba Batak language. The data in this study are in the form of metaphorical expressions that contain the meaning of anger in Toba Batak language speech in documentaries, books, and the research results from interviews conducted. The subjects in this study were 5 parents aged 50-80 years, and one was a Paradat (a person who actively participates in traditional activities and understands customary procedures). The results of this study indicate that the meaning of the angry metaphor in the Toba Batak language above is a symbol or figure of speech that contains emotion in speaking and acting, depicting expressions of thoughts reflecting attitudes and behavior in life. Then, the angry metaphor found in the Toba Batak language can affect attitudes to life.
From the detection towards a pyramidal classification of terrorist propaganda

With over 3,81 billion users from across the world, social media platforms provide a borderless environment for people of different nationalities, races, ethnicities, and religious beliefs to interact and communicate with each other. Not only for legitimate purposes are these digital tools used, but also groups of extremists and terrorist organizations take advantage of the features of these platforms to spread radicalization, propaganda, brainwashing and for online recruitment. Recent studies, conducted in this area, are trying to face with this phenomenon, but due to the heterogeneity of the sources, the large amount of daily data generated, and especially the different levels of radicalization of the users, make it even more difficult to define effective and general countermeasures against such phenomenon. In this context, the paper provides a solution that not only aims to support the detection of terrorist propaganda (and related users), but also to support its further categorization, centered on a pyramid classification model, by analyzing the level of users’ radicalization. This approach has two fundamental complementary advantages, as on the one hand it enables the establishment of priorities in terms of intervention, and on the other hand to define and apply targeted countermeasures based on the level of user’s radicalization. The proposed model and the results obtained from its experimentation are shown and discussed in comparison to previous works.
The use of physics-informed neural network approach to image restoration via nonlinear PDE tools

In this work, we propose a physics-informed solution for a blending PDE model with application in image denoising and edge-preserving. The blending model contains isotropic diffusion (ID) model and the modified Perona-Malik model introduced by Catte et al. Then we solve this model applying a physics-informed approach. Solving the new blending model is done using DeepXDE, which is a deep learning library for solving differential equations, by considering the image as input. Error analysis for neural network approximations of the new PDE model is shown. Applying PSNR criteria for some images, it can be seen that the proposed method can compete with solving denoising PDE models using finite difference method (FDM).