Reviewerly/exHarmony huggingface dataset:
Image Manipulation Detection Using Man Tra-Net

We've created ManTraNet., a unified deep neural architecture, to battle real-world picture forgeries., which commonly entail several types and coupled modifications. ManTra-Net., in contrast to numerous other current systems., which performs both localization and detection without any further preprocessing., is an end-to-end network. ManTra-Net supports images of any resolution and a wide range of common forgery techniques., such as splices., copy-moves., deletions., and additions., as well as previously unrecognised types may be a completely convolutional network. In order to distinguish reliable picture forgery traces from other types of forgeries., we tend to design a simplistic yet efficient self-supervised learning task. Additionally., we provide a Z-score feature for native anomaly detection., frame the forgery localization disadvantage as an area anomaly detection drawback., and suggest a special memory resolution for native anomaly analysis.
Role of calibration in uncertainty-based referral for deep learning

The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical evaluation of the role of model calibration in uncertainty-based referral, an approach that prioritizes referral of observations based on the magnitude of a measure of uncertainty. We consider several configurations of network architecture, methods for uncertainty estimation, and training data size. We identify a strong relationship between the effectiveness of uncertainty-based referral and having a well-calibrated model. This is especially relevant as complex deep neural networks tend to have high calibration errors. Finally, we show that post-calibration of the neural network helps uncertainty-based referral with identifying hard-to-classify observations.
Informatization and digitalization of teacher education: problems and prospects of development

Problem statement . Modern teacher education is designed to meet the needs of the state and the requests of society related to the global information and digital transformation. This strengthens the tendency to modernize the system of teacher training at the state, regional, local and subject levels. Based on the analysis of the concepts of “digitalization”, “informatization”, “pedagogical education”, “pedagogical activity”, the filling of the parameters of digital education, depending on its functions to ensure the quality of pedagogical education at all levels is determined. The interconnection and interdependence of the components of the digital educational environment and the structural and functional constructs of teacher education are considered. Methodology. The definition of problems and prospects for the development of informatization and digitalization of teacher education is based on a terminological and content-functional analysis of the concepts of digitalization and informatization of education, the digital educational environment. A structural and functional analysis of the positive domestic experience in the formation of information and digital educational environments in the universities of the Russia was carried out. The criteria for the directions of development of informatization and digitalization of teacher education was specified, based on the requirements of the state reflected in regulatory documents, the requests of regions, determined on the basis of an analysis of strategies for their development, modernization of the structure, content and requirements for training at the institutional level. Results. The theoretical aspects of the conjugation of the terms of informatization and digitalization, influencing the modernization of the sphere of teacher training, have been identified and substantiated. The problems of the system of teacher education are considered, which indicate the need for its reform on the basis of intensification of information and digital transformation of this sphere. Conclusion. The requirements and directions of modernization of teacher education in the conditions of digital transformation, which are associated with a change in approaches to the training of teachers, to the need for integrative consideration of the functions of pedagogical activity and the digital educational environment are defined.
Conceptual Foundations of Human-Centric AI

This tutorial does not focus on specific techniques or applications but on the conceptual foundations of human-centric AI. It discusses a number of fundamental questions: What is needed to make AI more human-centered or ‘humane’? Why do we need to combine reactive and deliberative intelligence for human-centric AI? What is the nature of meaning and understanding in human intelligence? Why is emulating understanding in artificial systems necessary but hard? What is the role of narratives in understanding? What are some of the open issues for realizing human-centric AI capable of narrative-based understanding?
Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models

In generalized linear models (GLMs), measures of lack of fit are typically defined as the deviance between two nested models, and a deviance-based R2 is commonly used to evaluate the fit. In this paper, we extend deviance measures to mixtures of GLMs, whose parameters are estimated by maximum likelihood (ML) via the EM algorithm. Such measures are defined both locally, i.e., at cluster-level, and globally, i.e., with reference to the whole sample. At the cluster-level, we propose a normalized two-term decomposition of the local deviance into explained, and unexplained local deviances. At the sample-level, we introduce an additive normalized decomposition of the total deviance into three terms, where each evaluates a different aspect of the fitted model: (1) the cluster separation on the dependent variable, (2) the proportion of the total deviance explained by the fitted model, and (3) the proportion of the total deviance which remains unexplained. We use both local and global decompositions to define, respectively, local and overall deviance R2 measures for mixtures of GLMs, which we illustrate-for Gaussian, Poisson and binomial responses-by means of a simulation study. The proposed fit measures are then used to assess, and interpret clusters of COVID-19 spread in Italy in two time points.
Image Encryption Using RSA and Advanced Caesar Cipher Method

In the field of Computer Science, proper storage and analysis of data is one of the most trending topics. The development of modern tools and techniques, has come down as a blessing for this. But along with the blessing, comes the curse of being easily attacked and the privacy of the data being vulnerable. The attackers and the hackers are becoming very smart and with their modern equipment, they can break into any system. For this several methods have been developed to save the privacy of our data. Out of these methods, the symmetric key method and the asymmetric key method are the most popular ones. Though both of the methods are powerful enough, they come with their own set of disadvantages. In this research work, an approach has been taken, where the combination of both the above spoken methods are used, making use of the advantages of both the methods, where both of them are also cancelling out each others disadvantages. The results confirmed that the accuracy, efficiency and the security of the algorithms are good enough, so that it can be used to deal with real world problems.
grafzahl: fine-tuning Transformers for text data from within R

This paper introduces grafzahl, an R package for fine-tuning Transformers for text data from within R. The package is used in this paper to reproduce the analyses in other papers. Very significant improvement in model accuracy over traditional machine learning approaches such as Convolutional Neural Network is observed.
Cryptocurrencies and central banks' monetary policy roles

The advent of cryptocurrencies has tremendously revolutionised the global financial system, facilitating borderless and seamless financial transactions. However, the high volatility in the value of cryptocurrencies, its decentralised network and lack of formal regulation pose severe threats to the monetary policy role of central banks. Hence, this study explored the dynamics of cryptocurrencies and how it affects the monetary policy roles of central banks. The study showed that cryptocurrencies help foster the actualisation of cashless economies and seamless international transactions but its lack of international coordination has continued to undermine tapping optimally into its inherent benefits. Therefore, this study emphasised the imperativeness of central bank digital currencies (CBDCs) for effective financial sector regulation and protection of users of this financial innovation. This would broaden the central banks' horizon and expand their capacities to harness the potential of this novel financial innovation for the effective administration of conventional and unconventional monetary policies.
Boosting Poultry Farm Profits Through Blockchain Technologies, AI, IoT, and Machine Learning

The foundation of a trustworthy brand has been extensively researched in the FMCG industry. Establishing the proper culture and discipline and conducting a continuous cycle of improvement based on continuous feedback are critical success factors. Increasing agricultural output is one of several projects supported by nearly every administration. The creation of a sustainable methodology for managing demand and supply is a difficult but necessary obligation for these brands. The government's regulations and policies serve to balance its citizens' health risks. Many businesses determine their target customer approach by evaluating customer categories based on their income and spending capacity. These businesses maintain credibility by adhering to international quality standards for chicken products, measured by designated lab test results, customer experience, and brand campaign marketing and publicity. Higher prices for producing high-quality farm products, and uncertainty around quantity, are two of the most significant challenges in the industry of consumable farm products.
Detecting Human Behavior from a Silhouette Using Convolutional Neural Networks

Human action recognition (HAR) in films has grown rapidly as an academic field in the past few decades. Robotics, HCI, intelligent video surveillance, and sports video analysis are just some of the real-world applications of action recognition. Despite extensive study, there are still many issues to be settled in this area. Multiple factors, such as the subject's position, speed, illumination, occlusion, viewpoint, and backdrop clutter, contribute to the challenge of the task. An efficient HAR system is able to account for these variants and rapidly identify the human action class. The primary steps in HAR systems are typically foreground segmentation, feature extraction, effective vector representation, and classification. This article proposes a novel method that employs Convolution Neural Network (CNN) to improve the HAR system's classification accuracy. The proposed activity representation and classification approach is evaluated by using public datasets from Weizmann, KTH, and the Ballet Movement. The examination of competing methods shows that our suggested approach provides higher recognition accuracy than existing methods. For the Weizmann dataset, the proposed technique offers 98 percent accuracy, and for the KTH dataset, it offers 95.6% accuracy.
Pre-Trained Language Model-Based Deep Learning for Sentiment Classification of Vietnamese Feedback

In recent years, with the strong and outstanding development of the Internet, the need to refer to the feedback of previous customers when shopping online is increasing. Therefore, websites are developed to allow users to share experiences, reviews, comments and feedback about the services and products of businesses and organizations. The organizations also collect user feedback about their products and services to give better directions. However, with a large amount of user feedback about certain services and products, it is difficult for users, businesses, and organizations to pay attention to them all. Thus, an automatic system is necessary to analyze the sentiment of a customer feedback. Recently, the well-known pre-trained language models for Vietnamese (PhoBERT) achieved high performance in comparison with other approaches. However, this method may not focus on the local information in the text like phrases or fragments. In this paper, we propose a Convolutional Neural Network (CNN) model based on PhoBERT for sentiment classification. The output of contextualized embeddings of the PhoBERT’s last four layers is fed into the CNN. This makes the network capable of obtaining more local information from the sentiment. Besides, the PhoBERT output is also given to the transformer encoder layers in order to employ the self-attention technique, and this also makes the model more focused on the important information of the sentiment segments. The experimental results demonstrate that the proposed approach gives competitive performance compared to the existing studies on three public datasets with the opinions of Vietnamese people.
An evolutionary framework for designing adaptive convolutional neural network

The Convolutional Neural Network (CNN) is a complex architecture that performs magnificently in image classification and segmentation problems. Still, selecting an effective architecture is typically hindered by several parameters. Empirically, evolutionary algorithms (EA) have been found adequate in parameter selection and automated neural network search. However, the huge computational requirements imposed by evolutionary search make its applicability unexplored. Consequently, the idea of a CNN architecture selection based on EA is challenging as comparing complex candidate architectures towards their fitness would involve massive computations. In this work, we propose a novel framework using an adapted Genetic Algorithm (GA) that automatically evolves an effective CNN architecture. We rectify the GA by devising an effective encoding scheme, an approach to initialize the input population, and a diversified offspring generation method. We also suggest an optimized fitness function that makes the convergence faster, avoiding the local optima. The method is validated with the benchmark MNIST, Fashion_MNIST, and CIFAR-10 datasets. The results are comparable to the best manual and automatic state-of-the-art architectures regarding accuracy, convergence rate, and consumed computation resources.
Transport in reservoir computing

Reservoir computing systems are essentially dynamical systems influenced by an exogenous input. Such systems are extensively used in biologically inspired information processing, and are the state-of-the art techniques for several machine learning tasks. If the statistics of the response or output of the system depends discontinuously on the distribution of the inputs, a fundamental challenge arises in applications where inherent changes in the input stochastic source or noise are expected. This problem can be experimentally demonstrated by showing that altering input statistics can drastically affect the statistics of the response. We solve this instability problem by providing sufficient conditions under which both the marginals and the joint distributions of the response depend continuously on that of the input. To prove our results, we establish the existence of an invariant measure and show that its dependence on the input process is continuous when the processes are endowed with the Wasserstein distance. The main tool in these developments is the characterization of those invariant measures as fixed points of naturally defined Foias operators that appear in this context and which are examined extensively in the paper. These fixed points are obtained by imposing a newly introduced stochastic state contractivity on the driven system that is readily verifiable in examples. Stochastic state contractivity can be satisfied by systems that are not state-contractive, which is a need typically evoked to guarantee the echo state property in reservoir computing. As a result, it may actually be satisfied even if the echo state property is missing.
Hardware/Software Co-Assurance for the Rust Programming Language Applied to Zero Trust Architecture Development

Zero Trust Architecture requirements are of increasing importance in critical systems development. Zero trust tenets hold that no implicit trust be granted to assets based on their physical or network location. Zero Trust development focuses on authentication, authorization, and shrinking implicit trust zones to the most granular level possible, while maintaining availability and minimizing authentication latency. Performant, high-assurance cryptographic primitives are thus central to successfully realizing a Zero Trust Architecture. The Rust programming language has garnered significant interest and use as a modern, type-safe, memory-safe, and potentially formally analyzable programming language. Our interest in Rust particularly stems from its potential as a hardware/software co-assurance language for developing Zero Trust Architectures. We describe a novel environment enabling Rust to be used as a High-Level Synthesis (HLS) language, suitable for secure and performant Zero Trust application development. Many incumbent HLS languages are a subset of C, and inherit many of the well-known security shortcomings of that language. A Rust-based HLS brings a single modern, type-safe, memory-safe, high-assurance development language for both hardware and software. To study the benefits of this approach, we crafted a Rust HLS subset, and developed a frontend to the hardware/software co-assurance toolchain due to Russinoff and colleagues at Arm, used primarily for floating-point hardware formal verification. This allows us to leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe our Rust subset, detail our prototype toolchain, and describe the implementation, performance analysis, formal verification and validation of representative Zero Trust algorithms and data structures written in Rust, emphasizing cryptographic primitives and common data structures.
An Experimental Analysis of Investor Sentiment

We use an experiment with a sample of professional investors to study the impact of text and emojis on investment proportion. We find that texts—provided as a supplementary information—have a statistically significant on investors’ decisions. However, the magnitude of the impact is too small (around 1%) to conclude that investor sentiment has an economically significant impact on investment decisions. We also find that emojis have no impact on investment decisions. Overall, our results are consistent with the efficient market hypothesis: in an experimental setting where the payoff and the probability of each decision are known, investment decisions of sophisticated traders are driven mostly by the type of asset, the level of risks, and the associated return of each investment and not by investor sentiment.
Combined intrinsic decomposition and histogram matching for color migration

To address the problem of insufficient detail retention in traditional color transfer, a color migration combining intrinsic decomposition and histogram matching is proposed in the paper. Firstly, the source image and the reference image are intrinsically decomposed to obtain their respective reflectance and irradiance maps, and the new reflectance maps after color transfer are obtained by histogram matching using the decomposed reflectance maps of the source and reference images. Secondly, in order to better preserve the texture details of the resultant image, the new reflectance map is intrinsically synthesized with the irradiance map of the source image enhanced by the global change regulariser to obtain the resultant map. The experimental results show that the new method proposed in the paper better preserves the texture structure of the result image, and the resulting result image has better visual effects and performs better in both subjective and objective evaluations.
Synthetic speech attribution using self supervised audio spectrogram transformer

The ability to synthesize convincing human speech has become easier due to the availability of speech generation tools. This necessitates the development of forensics methods that can authenticate and attribute speech signals. In this paper, we examine a speech attribution task, which identifies the origin of a speech signal. Our proposed method known as Synthetic Speech Attribution Transformer (SSAT) converts speech signals into mel spectrograms and uses a self-supervised pretrained transformer for attribution. This transformer is pretrained on two large publicly available audio datasets: Audio Set and LibriSpeech. We finetune the pretrained transformer on three speech attribution datasets: the DARPA SemaFor Audio Attribution dataset, the ASVspoof2019 dataset, and the 2022 IEEE SP Cup dataset. SSAT achieves high closed-set accuracy on all datasets (99.8% on ASVspoof2019 dataset, 96.3% on SP Cup dataset, and 93.4% on DARPA SemaFor Audio Attribution dataset). We also investigate the method&#xE2;&#x20AC;&#x2122;s ability to generalize to unknown speech generation methods (open-set scenario). SSAT has high performance, achieving an open-set accuracy of 90.2% on the ASVspoof2019 dataset and 88.45% on DARPA SemaFor Audio Attribution dataset. Finally, we show that our approach is robust to typical compression rates used by YouTube for speech signals.