Reviewerly/exHarmony huggingface dataset:
Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment

Machine Learning (ML) algorithms process input data making it possible to recognize and extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide knowledge in a Federated Learning (FL) environment, sharing parameters without compromising their raw data. However, FL suffers with non-independent and identically distributed (non-iid) data, which means it is heterogeneos data and has biased input data, such as in smartphone data sources. This bias causes low convergence for ML algorithms, high energy and bandwidth consumption. This work proposes a method that mitigates non-iid data through a FedAvg-BE algorithm that provides Federated Learning with the border entropy evaluation to select good input from a non-iid data environment. Extensive experiments were performed using publicly available datasets to train deep neural networks. The experiment result evaluation demonstrates that execution time saves up to 22% for the MNIST dataset and 26% for the CIFAR-10 dataset, with the proposed model in Federated Learning settings. The results demonstrate the feasibility of the proposed model to mitigate non-iid data impact.
Functional Cyber-Resilience - Extending the Cybersecurity Paradigm in Critical Infrastructures

The digitalization and connectivity is growing fast in many areas of society. This is also the case in critical infrastructures like Air Traffic Management systems. This will inevitably lead to an progressing technology dependency. Over the past years, the awareness has been improved, leading to an increasing attention for cyber-security challenges. In this paper, I will explain why this will not be sufficient in the long run to ensure business continuity. In addition to the risk coming from an increasing dependency, a concentration of technical infrastructures and a decrease of diversity can be observed. On the non-technical side the risk from geo-political differences are increasing as well. To address this, new, risk landscape, we need a different way of looking at system-wide risks. The risks and mitigation should be considered at a, generic, functional level rather than individual, technical, human and organisational levels.
Hierarchical multimodal transformers for Multipage DocVQA

Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure.
Effective child well-being practices, barriers and priority actions: survey findings from service providers and policymakers in 22 countries during COVID-19

Purpose The purpose of this study was to explore 232 service providers’ and policymakers’ experiences of supporting children’s well-being during the pandemic, across sectors, in 22 countries – including Kenya, the Philippines, South Africa, India, Scotland, Sweden, Canada and the USA, in the last quarter of 2020. Design/methodology/approach A smartphone survey delivered via a custom-built app containing mostly open-ended questions was used. Respondents were recruited via professional networks, newsletters and social media. Qualitative content analysis was used. Findings The findings reveal numerous system-level challenges to supporting children’s well-being, particularly virus containment measures, resource deficiencies and inadequate governance and stakeholder coordination. Those challenges compounded preexisting inequalities and poorly affected the quality, effectiveness and reach of services. As a result, children’s rights to an adequate standard of living; protection from violence; education; play; and right to be heard were impinged upon. Concurrently, the findings illustrate a range of adaptive and innovative practices in humanitarian and subsistence support; child protection; capacity-building; advocacy; digitalisation; and psychosocial and educational support. Respondents identified several priority areas – increasing service capacity and equity; expanding technology use; mobilising cross-sectoral partnerships; involving children in decision-making; and ensuring more effective child protection mechanisms. Practical implications This study seeks to inform resilience-enabling policies and practices that foster equity, child and community empowerment and organisational resilience and innovation, particularly in anticipation of future crises. Originality/value Using a novel approach to gather in-the-moment insights remotely, this study offers a unique international and multi-sectoral perspective, particularly from low- and middle-income countries.
An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization

White blood cells (WBCs) are crucial components of the immune system that play a vital role in defending the body against infections and diseases. The identification of WBCs subtypes is useful in the detection of various diseases, such as infections, leukemia, and other hematological malignancies. The manual screening of blood films is time-consuming and subjective, leading to inconsistencies and errors. Convolutional neural networks (CNN)-based models can automate such classification processes, but are incapable of capturing long-range dependencies and global context. This paper proposes an explainable Vision Transformer (ViT) model for automatic WBCs detection from blood films. The proposed model uses a self-attention mechanism to extract features from input images. Our proposed model was trained and validated on a public dataset of 16,633 samples containing five different types of WBCs. As a result of experiments on the classification of five different types of WBCs, our model achieved an accuracy of 99.40%. Moreover, the model's examination of misclassified test samples revealed a correlation between incorrect predictions and the presence or absence of granules in the cell samples. To validate this observation, we divided the dataset into two classes, Granulocytes and Agranulocytes, and conducted a secondary training process. The resulting ViT model, trained for binary classification, achieved impressive performance metrics during the test phase, including an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43%. To ensure the reliability of the ViT model's, we employed the Score-CAM algorithm to visualize the pixel areas on which the model focuses during its predictions. Our proposed method is suitable for clinical use due to its explainable structure as well as its superior performance compared to similar studies in the literature. The classification and localization of WBCs with this model can facilitate the detection and reporting process for the pathologist.
Application of TOPSIS model in active tectonic prioritization: Madeira watershed, South America

The Andes orogenic belt is one of the most tectonically active zones located between the convergent margins of the South American plate and the Pacific plate. From the dissected western belt to the expanded alluvial plain of Amazon basin, the Madeira watershed bears recent tectonic imprints. The tectonic signature is well documented by the spatial and linear characteristics of the five sub watersheds along with the Madeira trunk river and channels. The level of tectonic activeness has been assessed on the sub watersheds of the Madeira River, which is one of the main right-hand tributaries of the Amazon River. On the five defined sub-watersheds and the main channel watershed of Madeira, several morphometric indices, viz., asymmetry factor (AF), tilt angle (β), sinuosity index (SI), channel concavity (ɵ), elongation ratio (Re), circularity ratio (Rc), transverse topographic symmetry factor (T) and basin shape index (Bs), have been used. To rank the watersheds based on tectonic activity, we applied the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) The active tectonics was also tested by a comparative study of linear indicators as Hack profiles, longitudinal profiles and segment-wise stream gradient index (SL) of watersheds 2 (rank 1) and 3 (rank 6). The results reveal that the most tectonically active watershed 2 is located in a high seismic magnitude zone with several deep earthquake epicenters. The least tectonically active watershed 6 is located in a low seismic zone.
Explainable autonomous robots in continuous state space based on graph-structured world model

Autonomous robots using deep reinforcement learning have demonstrated superior capabilities on relatively simple specific tasks, but they often lack high-level, abstract planning capabilities when faced with complex, long-horizon tasks. Even if the autonomous robot successfully achieves long-horizon goals, users find it difficult to trust their decision-making process. To increase user trust in the decision-making process when an autonomous robot executes a long-horizon task, this paper proposes an algorithm that empowers the autonomous agent to explain to users the transition from the current state to the target state in a continuous state space, as well as to explain the errors in user estimates. A framework that uses a graph-based world model is proposed to identify important nodes and reachability between nodes in the decision-making process; based on these nodes and reachability, the model generates the required explanations. To validate our proposed method's ability to generate long-horizon plans and explanations, we conducted experiments in PointMaze environments. Our simulation results confirm the effectiveness of our approach in generating reliable world models for long-horizon tasks. Moreover, our explanations, based on these world models, significantly enhance the user's understanding of the autonomous robotic decision-making process and the system's capabilities and limitations.
How Generative Ai Turns Copyright Law on its Head

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Hybrid knowledge distillation from intermediate layers for efficient Single Image Super-Resolution

Convolutional and Transformer models have achieved remarkable results for Single Image Super-Resolution (SISR). However, the tremendous memory and computation consumption of these models restricts their usage in resource-limited scenarios. Knowledge distillation, as an effective model compression technique, has received great research focus on the SISR task. In this paper, we propose a novel efficient SISR method via hybrid knowledge distillation from intermediate layers, termed HKDSR, which leverages the knowledge from frequency information into that RGB information. To accomplish this, we first pre-train the teacher with multiple intermediate upsampling layers to generate the intermediate SR outputs. We then construct two kinds of intermediate knowledge from the Frequency Similarity Matrix (FSM) and Adaptive Channel Fusion (ACF). FSM aims to mine the relationship of frequency similarity between the Ground-truth (GT) HR image, and the intermediate SR outputs of teacher and student by Discrete Wavelet Transformation. ACF merges the intermediate SR output of the teacher and GT HR image in a channel dimension to adaptively align the intermediate SR output of the student. Finally, we leverage the knowledge from FSM and ACF into reconstruction loss to effectively improve student performance. Extensive experiments demonstrate the effectiveness of HKDSR on different benchmark datasets and network architectures.
Augmented Reality Based Distant Maintenance Approach

This paper focuses on the implementation of Industry 4.0-based maintenance approaches using AR technology. The primary objective is to highlight the benefits of AR in maintenance and provide a framework for implementing AR-based maintenance systems. AR can assist maintenance personnel in performing maintenance tasks more efficiently by providing real-time instructions and information. By overlaying virtual information on real equipment, AR can guide maintenance personnel through maintenance procedures, reducing human errors and improving the quality of the work. AR can also provide remote assistance to maintenance personnel, allowing experts to remotely guide less experienced personnel through complex maintenance procedures. The proposed application provides motorcycle service assistance using AR and telepresence technologies. It enables remote monitoring of the service flow and performs an automated identification of parts. The system provides instructions for disassembling more complex mechanical components, provides a 3D model of the object, and enables ordering of the necessary parts.
Open Access Literature in Libraries: Principles and Practices

"Open Access Literature in Libraries: Principles and Practices." Journal of Hospital Librarianship, 23(3), pp. 222–223
Building Legal Knowledge Map Repository with NLP Toolkits

Today, the legal document system is increasingly strict with different levels of influence and affects activities in many different fields. The increasing number of legal documents interwoven with each other also leads to difficulties in searching and applying in practice. The construction of knowledge maps that involve one or a group of legal documents is an effective approach to represent actual knowledge domains. A legal knowledge graph constructed from laws and legal documents can enable a number of applications, such as question answering, document similarity, and search. In this paper, we describe the process of building a system of knowledge maps for the Vietnamese legal system from the source of about 325,000 legal documents that span all fields of social life. This study also proposes an integrated ontology to represent the legal knowledge from legal documents. This model integrates the ontology of relational knowledge and the graph of key phrases and entities in the form of a concept graph. It can express the semantics of the content of a given legal document. In addition, this study also describes the process of building and exploiting natural language processing tools to build a VLegalKMaps system, which is a repository of Vietnamese legal knowledge maps. We also highlight open challenges in the realization of knowledge graphs in a technical legal system that enables this approach at scale.
Question answering models for human–machine interaction in the manufacturing industry

This paper presents a question answering (QA) system that will enable workers from the manufacturing industry to ’hands-free’ request information. This kind of systems, that are broadly used in household context, have started to gain popularity in industrial environments. To develop the system, PDF-based industrial manuals in Spanish have been processed, annotated by experts and used to train the different components of the architecture, i.e. a question classifier and two QA systems. Different metrics have been applied and developed in order to test the performance of the system. The developed architecture obtains high classification and correct response results, demonstrating the viability of these systems in the industry.
Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.
Echo State Network With Probabilistic Regularization for Time Series Prediction

Recent decades have witnessed a trend that the echo state network (ESN) is widely utilized in field of time series prediction due to its powerful computational abilities. However, most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise, which lacks robustness or even fails to solve real-world tasks. This work handles this issue by proposing a probabilistic regularized ESN (PRESN) with robustness guaranteed. Specifically, we design a novel objective function for minimizing both the mean and variance of modeling error, and then a scheme is derived for getting output weights of the PRESN. Furthermore, generalization performance, robustness, and unbiased estimation abilities of the PRESN are revealed by theoretical analyses. Finally, experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN. The source code is publicly available at https://github.com/LongJin-lab/probabilistic-regularized-echo-state-network.
Real-Time Krylov Theory for Quantum Computing Algorithms

Quantum computers provide new avenues to access ground and excited state properties of systems otherwise difficult to simulate on classical hardware. New approaches using subspaces generated by real-time evolution have shown efficiency in extracting eigenstate information, but the full capabilities of such approaches are still not understood. In recent work, we developed the variational quantum phase estimation (VQPE) method, a compact and efficient real-time algorithm to extract eigenvalues on quantum hardware. Here we build on that work by theoretically and numerically exploring a generalized Krylov scheme where the Krylov subspace is constructed through a parametrized real-time evolution, which applies to the VQPE algorithm as well as others. We establish an error bound that justifies the fast convergence of our spectral approximation. We also derive how the overlap with high energy eigenstates becomes suppressed from real-time subspace diagonalization and we visualize the process that shows the signature phase cancellations at specific eigenenergies. We investigate various algorithm implementations and consider performance when stochasticity is added to the target Hamiltonian in the form of spectral statistics. To demonstrate the practicality of such real-time evolution, we discuss its application to fundamental problems in quantum computation such as electronic structure predictions for strongly correlated systems.
Utilizing Bio Metric System for Enhancing Cyber Security in Banking Sector: A Systematic Analysis

Biometric authentication is gaining the interest of private, public, consumer electronics and corporate security systems. For the protection of cyberspace from hackers and other harmful people, biometric security is growing more and more popular among organizations, individuals and enterprises. The word “cyber security” refers to the procedures, techniques, and tools used to safeguard data, network system, computer networks and software from potential attacks online. Online financial service delivery is referred to as “cyber banking.” As the trend of exchanging things has changed, internet banking has grown. Despite the benefits, there have been instances of security threat-related issues with Internet banking. To identify persons, biometric security verifies their physical attributes and behavioral traits. For identification verification, it is the most reliable and effective physical security method. According to biometric authentication, people can be recognized precisely based on their innate behavioral or physical traits. Numerous security measures have been implemented throughout the entire Internet banking service to address these issues. Globally, cybercrime has deep roots and poses a significant threat to the occurrence of criminal or terrorist behavior. Without being addressed by a single authority, these risks can compromise security on the inside as well as the outside. If the cybercrime goes unnoticed, both money and personal data are lost. Internet services and information infrastructure have previously been targeted in assaults. Online fraud and hacker attacks are only two examples of the daily computer-related crimes that take place. The Internet of Things (IoT) is the most reliable foundation for facilitating high-quality, comfortable human living. IoT has had a substantial impact across a range of application domains. Smart gadgets are more vulnerable to hackers because of their rapid development and trust in wireless mechanics for data transport. As a result, the rate of cybercrime is rising daily. Artificial Intelligence (AI)-based cybersecurity emerges because of technological advancement and poses a risk to public safety, personal property rights, and privacy protection for people. The study elaborates on the key features of biometrics system in conventional and Islamic banking to counter the risk of cybersecurity and provide high safety and security to the banking industry. For this systematic literature review, the most suitable and most relevant 101 articles from the reputed online libraries are selected. This analysis absorbed four research questions and pertinent keywords from the period of 2009 to 2022 (a part of 2023 was included).
Reversible PRNU anonymity for device privacy protection based on data hiding

In this paper, we propose a method of protecting a device from being identified with the images captured by it. This method can suppress the device-related fingerprint, i.e., PRNU, in an image via modifying it in the domain of integer wavelet transform. In addition, we embed the suppressed PRNU into the image using reversible data hiding to achieve a perfect recovery of the original image. This is beneficial to solve the problems of device privacy leakage and terminal server identity authentication. Extensive experiments on MICHE-I and VISION datasets show that the proposed method makes up for the shortcomings of existing techniques and has strong scalability.