Reviewerly/exHarmony huggingface dataset:
The Development of a Model System for the Visualization of Information on Cultural Activities and Events

The use of augmented reality (AR) is the only meaningful way towards the creation and integration of information into a cultural (and not only) analogue leaflet, flyer and poster, which by their nature have limited informational material. The enrichment of information, which an analogue leaflet has, with additional descriptive information (text), multimedia (image, video, sound), 3-D models, etc., gives the end recipient/user a unique experience of approaching and understanding a music event, for example. The Digi-Orch is a research project that aims to create smart printed communication materials (e.g., posters, concert programme booklets) and an accompanying AR application to provide this enhanced audience experience to classical music lovers. The paper presents the ways in which AR can extend and enrich an analogue leaflet/flyer/poster, the features of and how to use the developed AR application, as well as the components, architecture and functions of the system. It also presents the different versions of the AR application within the project, starting with the laboratory versions, continuing with the pilot versions in real conditions (music events) and reaching the final prototype and the educational version of the AR application.
A Comprehensive Survey on Artifact Recovery from Social Media Platforms: Approaches and Future Research Directions

Social media applications have been ubiquitous in modern society, and their usage has grown exponentially over the years. With the widespread adoption of these platforms, social media has evolved into a significant origin of digital evidence in the domain of digital forensics. The increasing utilization of social media has caused an increase in the number of studies focusing on artifact (digital remnants of data) recovery from these platforms. As a result, we aim to present a comprehensive survey of the existing literature from the past 15 years on artifact recovery from social media applications in digital forensics. We analyze various approaches and techniques employed for artifact recovery, structuring our review on well-defined analysis focus categories, which are memory, disk, and network. By scrutinizing the available literature, we determine the trends and commonalities in existing research and further identify gaps in existing literature and areas of opportunity for future research in this field. The survey is expected to provide a valuable resource for academicians, digital forensics professionals, and researchers by enhancing their comprehension of the current state of the art in artifact recovery from social media applications. Additionally, it highlights the need for continued research to keep up with social media’s constantly evolving nature and its consequent impact on digital forensics.
MiAMix: Enhancing Image Classification through a Multi-Stage Augmented Mixed Sample Data Augmentation Method

Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks. While various strategies have been proposed, Mixed Sample Data Augmentation (MSDA) has shown great potential for enhancing model performance and generalization. We introduce a novel mixup method called MiAMix, which stands for Multi-stage Augmented Mixup. MiAMix integrates image augmentation into the mixup framework, utilizes multiple diversified mixing methods concurrently, and improves the mixing method by randomly selecting mixing mask augmentation methods. Recent methods utilize saliency information and the MiAMix is designed for computational efficiency as well, reducing additional overhead and offering easy integration into existing training pipelines. We comprehensively evaluate MiAMix using four image benchmarks and pitting it against current state-of-the-art mixed sample data augmentation techniques to demonstrate that MiAMix improves performance without heavy computational overhead.
Implementation of Blockchain Technology in Waste Management

Implementing blockchain technology in waste management is a novel approach to environmental sustainability and accountability challenges in our modern world. Blockchain, a technology that enables decentralized and immutable ledgers, is now being re-imagined as a tool to revolutionize waste management. This innovative approach aims to improve waste management transparency, traceability, and efficiency, resulting in significant environmental and economic benefits. In traditional waste management systems, the tracking and disposal of waste materials are not transparent and can be vulnerable to fraud, mismanagement, and inefficiency. Blockchain technology provides a secure and transparent platform for recording every step in the waste management lifecycle, from waste generation to collection, transportation, recycling, or disposal. Every transaction in the blockchain is recorded in a tamper-proof manner, enabling real-time monitoring and verification of waste-related data. This paper introduces the concept of using blockchain technology in waste management. The main goal of this work is to show the implementation of blockchain technology in an existing waste management company, using smart contracts in the recycling process to provide transparency. Also, the digital product passport was redefined in terms of circular economy and waste recycling.
Dynamische 3D-Kartensymbole für VR-Geovisualisierungen

Abstract Geovisualizations that use a virtual reality headset can have a more immersive effect on users than when only a desktop application is viewed. However, to have an overview, a VR 3D map is usually viewed from a top-down perspective. On the other hand, the first-person perspective is used when the scenario is visualized as realistically as possible, and the ultimate goal is to feel present inside the 3D city. With the help of dynamic cartographic visualization methods, such as animated symbols appearing or a dynamic change of selected or complete visualization, it is possible to convey targeted information while being inside the 3D. For this purpose, techniques and ideas from cartography and video games were utilized and combined. The combination resulted in dynamically changeable symbol objects, which can be placed automatically using tables or building information, and dynamically changeable post-processing methods. Users can interactively select a predefined set of visualization methods and show/hide dynamic cartographic symbols in the running application via an interface. The study aims to develop cartographic visualization methods that can be technically used within a game engine for VR applications to improve spatial perception.
Automated Spam Detection Using Sandpiper Optimization Algorithm-Based Feature Selection with the Machine Learning Model

The email has become an online communication tool and an important part of daily life. Spam mails take up a lot of space and bandwidth, and spam filtering algorithms have flaws that cause them to mistake legitimate emails for spam (false positives). These issues are becoming a bigger difficulty for the online world. This work proposes the use of a sandpiper optimization (SPO) algorithm, which is applied for the feature selection process which minimizes the training complexity and maximizes the classification accuracy and Radial Bias Neural Network (RBNN) for classifying emails as genuine email and spam email. The Enron email dataset and Spam Assassin datasets were used. The outcomes show that the rotation forest algorithm after feature selection with SPO accurately classifies the emails as genuine email and spam email with 3.88%, 5.75%, and 6.16% higher accuracy for Genuine Email, 2.31%, 8.47%, and 7.23% higher accuracy for spam email compared with existing Universal Spam Detection using Transfer Learning of the BERT Model (USD-TL-BERT), Hybrid Learning Approach for E-mail Spam Detection and Classification (HLA-ESDC), and the Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method (ESD-HAHD), respectively. This demonstrates that the proposed method significantly outperforms existing methods.
RGS$$^\oplus $$: RDF graph synchronization for collaborative robotics

Abstract In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge is gathered incrementally and in different ways by heterogeneous robots and humans. The purpose of this paper is to describe an RDF Graph Synchronization System called RGS $$^\oplus $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>⊕</mml:mo> </mml:msup> </mml:math> . It is assumed that a dynamic set of agents provide or retrieve knowledge stored in their local RDF Graphs which are continuously synchronized between agents. The RGS $$^\oplus $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>⊕</mml:mo> </mml:msup> </mml:math> System was designed to handle unreliable communication and does not rely on a static centralized infrastructure. It is capable of synchronizing knowledge as timely as possible and allows agents to access knowledge while it is incrementally acquired. A deeper empirical analysis of the RGS $$^\oplus $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>⊕</mml:mo> </mml:msup> </mml:math> System is provided that shows both its efficiency and efficacy.
Enhancing anomaly detectors with LatentOut

Abstract $${{\textbf{Latent}}\varvec{Out}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Latent</mml:mi> <mml:mrow> <mml:mi>Out</mml:mi> </mml:mrow> </mml:mrow> </mml:math> is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely ( Variational ) Autoencoders , GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of $${{\textbf{Latent}}\varvec{Out}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Latent</mml:mi> <mml:mrow> <mml:mi>Out</mml:mi> </mml:mrow> </mml:mrow> </mml:math> acting as a one-class classifier and we experiment the combination of $${{\textbf{Latent}}\varvec{Out}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Latent</mml:mi> <mml:mrow> <mml:mi>Out</mml:mi> </mml:mrow> </mml:mrow> </mml:math> with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by $${{\textbf{Latent}}\varvec{Out}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Latent</mml:mi> <mml:mrow> <mml:mi>Out</mml:mi> </mml:mrow> </mml:mrow> </mml:math> has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space.
Modelling Factors Influencing IoT Adoption: With a Focus on Agricultural Logistics Operations

Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations.
Explainable Artificial Intelligence Using Expressive Boolean Formulas

We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability) according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule- and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special-purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over the subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations. Therefore, using specialized or quantum hardware could lead to a significant speedup through the rapid proposal of non-local moves.
Noise‐Based Damping of Chaotic Entanglement in Pulsed Driven Two‐Qubit System

Abstract This paper investigates the entanglement dynamics of a two‐qubit system in the presence of n‐sequential ‐pulse shape. The study explores entanglement under two scenarios: when the system is completely isolated from the environment and when it interacts with one of the surrounding environments, namely the thermal environment and the common dephasing environment. The authors quantify entanglement through concurrence while systematically examining the effects of initial state preparation, qubit coupling strength, laser‐qubit interaction intensity, pulse sequences, and decohering environments. The results highlight that the entanglement of the two‐qubit system strongly depends on the initial state. Increasing the coupling strength between the qubits and the n‐sequential pulse enhances the maximum values of entanglement. Conversely, augmenting the laser‐qubits coupling or introducing the influence of the environment diminishes the entanglement.
3D Molecules Visualization with XRmol: An AR Web Tool for Mobile Devices

We present XRmol, a prototype web application, based on the WebXR standard, for visualizing 3D proteins and nucleic acids. The work represents a contribution to the investigation of the potential stemming from the AR paradigm coupled with the use of personal smartphones and features a number of solutions for improving access, portability, and use in different contexts. A first pilot study with 14 volunteers outlines its potential for integration into daily lab activities and use in educational contexts.
Generalized Fake Image Detection Method Based on Gated Hierarchical Multi-Task Learning

Recently, the abuse of image generation techniques based on artificial intelligence has posed a great threat to the integrity of digital images. However, existing detection methods are hard to provide generalized detection capability of fake images generated by unseen models. To address this issue, we propose a generalized fake image detection framework based on gated hierarchical multi-task learning, which is supervised by well-designed forensics sub-tasks. Firstly, a global artifact learning task is constructed as binary classification with region masking augmentation. Besides, a block-wise spatial correlation learning task is designed by solving jigsaw puzzle cooperated with color jitter operations, which aims to explore common artifacts of various generators. Finally, a hierarchical multi-task learning paradigm is developed with multi-gate structures, which can adjust the importance of different forensics clues and jointly enhance detection performance. Extensive experiments have been conducted to evaluate the superiority of the proposed method on the open-set scenario with unseen generators
Revisiting the impact of financial development on income inequality and poverty reduction: empirical evidence from selected sub-Saharan African countries

Purpose This paper aims to investigate the effects of financial access, financial depth, financial efficiency and financial stability pillars on income inequality and poverty among a panel of sub-Saharan African (SSA) countries. Design/methodology/approach This paper captures cross-sectional dependence among the income groups through the dynamic common correlated effect approach for a data set of 28 selected SSA countries from 2000 to 2017. Findings This study reveals that the financial development pillars exert positive and significant impacts on income inequality across the income groups. The results show that the effects of the financial development metrics on poverty are different across the income groups. The results also indicate that the pillars improve poverty reduction for low- and lower-middle-income countries. However, there is a minimal effect on poverty reduction in upper-middle-income countries. The differences among these income categories suggest the need for policymakers to account for income levels when prescribing policies that could engender financial development and poverty reduction in the region. Originality/value This paper examines the effects of financial development on both income inequality and poverty by using the newly developed World Bank financial development strategic metrics. It captures cross-sectional dependence in the full sample of selected SSA countries and their income categories.
A novel virtual-communicated evolution learning recommendation

Purpose In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in. Design/methodology/approach A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time. Findings A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items. Originality/value Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.
FedACQ: adaptive clustering quantization of model parameters in federated learning

Purpose For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round. Design/methodology/approach This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information. Findings While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly. Originality/value By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.