Reviewerly/exHarmony huggingface dataset:
Trainable English Speech Synthesis Algorithm Based on Artificial Neural Network

In the technical requirements of machine-synthesized voice, it is very important whether there is stress in phonemes in English words, which seriously affects the correctness and naturalness of pronunciation. This paper studies the trainable English speech synthesis algorithm based on ANN (artificial neural network). In the training part of the algorithm, the context-related ANN factors of sentences in the speech database are extracted, and the vocal tract spectrum parameters and fundamental frequency parameters are modeled. In the synthesis part, the problem set obtained in the training is then used to make decisions for each unit in turn, and the corresponding cluster state model is obtained, and the cluster state model sequence is formed. Finally, English speech is synthesized by a parameter synthesizer. The results show that the model in this paper is more powerful in controlling emotional style than the baseline model with single style, and the accuracy of emotional judgment can be improved by 72. 62%. The fitting degree of the latent envelope obtained by the improved RBF (Radial Basis Function) network to the target speech envelope is better than that obtained by the traditional RBF network.
A Data-Centric Approach for Rapid Dataset Generation Using Iterative Learning and Sparse Annotations

This study investigates the application of iterative sparse annotations for semantic segmentation in remote-sensing imagery, focusing on minimizing the laborious and expensive data labeling process. By leveraging Geographic Information Systems (GIS), we implemented circular polygon shapefiles to label portions of each class, attributing a value of -1 outside these polygons. The model training used the simplified BSB Aerial Dataset with eight classes. The semantic segmentation model was U-Net architecture with the Efficient-net-B7 backbone and a modified cross-entropy loss function. Our results showed promising improvement, particularly in error-prone classes, with the iterative addition of more samples. This approach suggests a quicker method for dataset creation using sparse, iteratively enhanced annotations. Future work will aim to implement further iterative rounds to approximate the results of continuous labeling, thereby enhancing the efficiency of semantic segmentation in large-scale remote-sensing images.
A Prediction Model Combining Convolutional Neural Network and LSTM Neural Network

Existing models such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) can make immediate predictions, while making long-term predictions is more difficult. In order to make better prediction, this paper proposes a hybrid model consisting of convolutional model and Long Short Term Memory model (CNN-LSTM) to make prediction.CNN-LSTM hybrid neural network firstly extracts the spatial structure features of the data by CNN. Secondly, the time series features in the data are extracted by using LSTM, and finally the prediction is effective in a short time. By comparing the experimental prediction results and evaluation indexes, it is verified that the CNN-LSTM hybrid network model has more accurate prediction accuracy and stability than some traditional prediction models, which has high reference value.
Alteration Zones Detection Using Image-Based and Spectrum-Based Image Processing Techniques to Aster Data: Sonajil Copper Deposit

The Sonajil region, renowned as a part the global copper belt, is situated within Eastern Azerbaijan province of Iran. Encircling the Sonajil copper belt are distinct halos, including potassic, phyllic, argillic, and propylitic alterations. This research employs remote sensing techniques to investigate the potential presence of porphyry mineralization in this area. Initially, the required pre-processing, including geometric and radiometric corrections, was performed on the Sonajil ASTER image. Following that, a range of alteration detection techniques was applied to the ASTER data, encompassing image-based methods such as band ratioing, as well as spectrum-based approaches including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Mixture-Tuned Matched Filtering (MTMF), Linear Spectral Unmixing (LSU), and binary coding (BE). The results of the extracted alterations from the utilized techniques are consistent and compatible with the region's rock formations, including quartz monzonite, granodiorite, and andesite. Therefore, the possible existence of a Cu-Au porphyry deposit is confirmed by remote sensing studies. For subsequent analyses, geochemical and geophysical studies are proposed for the Sonajil area to demonstrate the existence of the porphyry deposit. The research findings and the proposed approach hold promise for identifying and targeting hydrothermally altered zones in other high-potential regions worldwide, providing a valuable tool for exploration geologists in their quest to map prospective ore deposits.
Adiabatic Quantum Kitchen Sinks with Parallel Annealing for Remote Sensing Regression Problems

Kernel methods are class of Machine Learning (ML) models that have been widely employed in the literature for Earth Observation (EO) applications. The increasing development of quantum computing hardware motivates further research to improve the capabilities and the performances of data analysis algorithms. In this manuscript an implementation of Adiabatic Quantum Kitchen Sinks (AQKS) kernel estimation algorithm integrated with parallel quantum annealing is presented. Such combination with the concept of parallel quantum annealing allows for the solving of multiple problem instances in the same annealing cycle, thus reducing the number of rquired calls to the quantum annealing solver. The proposed workflow is then implemented using a D-Wave Advantage system and tested on a regression problem on a real Remote Sensing (RS) dataset. The obtained results are then analyzed and compared with those obtained by a classical kernel approximation algorithm based on Random Fourier Features.
Dual-Path Spatial Cognitive Graph Convolution for Polygonal Regularization of Segmented Buildings

Regularized vector representation of artificial buildings serves as foundation of remote sensing and GIS analysis tasks. To generate exact and regular building polygons, a graph convolutional network integrating spatial morphology cognition features is proposed. Abstract image semantic features from CNN and topological spatial-cognition features from graph reasoning module are united for node features in graph construction and dynamic convolutions. The proposed model is validated on WHU building dataset and compared with Mask R-CNN and Curve-GCN. The superior performances demonstrate that the spatial cognitive information perception strategy enhances the reasonable geometric prediction on coordinates of boundary vertices.
Testing Prisma Capability to Detect Rare Earth Element Contents from Space

The REE are among the most demanded critical raw materials in the 21st century but some potentially prospective locations have uneasy access or are remote. We tested the ability of PRISMA hyperspectral satellite to reveal possible economic REE deposits using direct REE spectral absorption features. The study focused on carbonatite occurrences in north Namibia. The multiple REE-related spectral absorption feature parameters were detected in the range 700–900 nm, whereas absorptions at 714, 744, 786 and 817 nm were found to correlate with carbonatites containing high REE abundances. Using this approach, three carbonatite areas with promising REE-bearing orebodies were identified. One site was validated using laboratory geochemical data, showing promising results for the future use of hyperspectral imaging data for exploring REE deposits.
Adversarial Discriminative Knowledge Transfer with a Multi-Class Discriminator for Robust Geoai

In geospatial image analysis applications, many factors can result in statistical differences between training and testing/deployment conditions. Domain adaptation techniques aim to reduce these disparsities with the goal of improving image analysis performance. Despite recent progress, some challenges remain, such as insufficient discriminability in the aligned space and negative transfer. We introduce a novel semi-supervised domain adaptation approach that improves the adversarial discriminative domain adaptation framework, addressing these challenges. We validate its effectiveness using two real-world hyperspectral image analysis datasets with varying acquisition conditions.
Impact of Technology-Aided Activity-Based Learning Approaches on Learning Outcomes: Experimental Evidence from Community Schools in Rural Zambia

We present experimental evidence on the impact of a multi-faceted program that integrates technology-aided instruction, teacher training and coaching, community ownership, and free primary education. Our setting is three districts in rural Zambia, where we randomly assigned the program across 30 treatment and 33 control schools. The results show that the program increased reading scores by 0.40 standard deviations or 3.5 percentage points, math scores by 0.21 standard deviations or 4.7 percentage points, Zambian achievement test scores by 0.15 standard deviations or 3.0 percentage points, and oral vocabulary scores by 0.25 standard deviations or 5.9 percentage points for all children eligible to enroll in first grade. Treatment Effects on the Treated showed substantially larger impacts on learning outcomes of students who regularly attended the schools in Grade 1. Our results indicate that multi-faceted technology-aided instruction programs can improve learning outcomes even in the poorest areas of rural sub-Saharan Africa.
Economic and security challenges as a consequence of the criminal act of document forgery

Since ancient times, when official documents have begun to be used, as well as all kinds of confidential documents, securities and other documents, society has been faced with various types of forgery and misuse of documents whose content was important and valuable. The inventiveness of social progress has conditioned the need to establish new incriminations that open up the possibility of criminal prosecution of counterfeiters. Criminal acts are introduced into criminal laws, which in their essence contain forgery as an act of execution. Initially, these acts did not have signs of forgery, but entered the zone of incrimination of criminal acts of abuse in different areas of social life, i.e. in different professions. What in the last few years in the Balkans can certainly be brought under the security challenge related to the aforementioned criminal act, is the falsification of documents, travel and personal, residence permits and PCR tests that were needed for travel in the region and in general, which is the mentioned health and it could further complicate the economic security situation. In addition to the above, the countries of the EU and the Schengen system have a very complicated situation in how to deal with the increasing misuse and falsification of personal biometric documents of the EU countries, and this certainly affected additional economic problems. As endangering the social situation caused by misuse of forged documents and economic migrations.
Possibilities in Blockchain technology application

Block chain technology is becoming more and more important as it is being applied in many fields of business. Blockchain serves as a continuing book of records enabling transactions to develop in decentralized circumstances. Although it is a relatively new technology (first theoretical work dates from 2008), developed and applied primarily in payment systems (Bitcoin), it appeared that its potentials are far beyond it. Within the period from its beginning until the present day, this technology has been considerably improved, applied algorithms perfected and accommodated to contemporary business environment. Research in the field of application concerning this technology was made on the basis of available literature and papers published in relevant journals. The paper represents a comprehensive review of possibilities in Blockchain technology application in contemporary business environment, its benefits and shortcomings as well as security problems appearing in theory and practice. This paper also deals with the analysis of possible trends in the mentioned field.
Spoofed Email Based Cyberattack Detection Using Machine Learning

ABSTRACTCyberattacks on e-mails are of different types, but the most pervasive and ubiquitous are spoofing attacks. Our approach uses memory forensics to extract e-mail headers from live memory to perform an e-mail header investigation to identify spoofing attacks. We have identified the research gaps and advanced our work to achieve better results. In this paper, we have made two significant improvements. First is URL validation module that uses a novel technique of checking each captured URL with an MX record and e-mail URL features. This scheme is fast, and reduces the total time from 35 sec to 27 sec. Second, spoofed e-mail detection is ameliorated by applying an ML model built using two novel e-mail header fields (BIMI and X-FraudScore) and four authentication header fields (SPF, DKIM, DMARC, and ARC). This enhances the spoofed e-mail detection accuracy from 96.15% to 97.57% with low false positives.KEYWORDS: Email spoofingemail attacksmemory forensicsemail forensicscyber-security Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request through email.
DPAN: A Deep Light-Weight Attention-Based Image Super Resolution Network Using Multi-Dimensional Filter Design Technique

High-frequency components are the most crucial parts of the visual signals for the task of image super resolution. The deep image super resolution networks that are able to process the high-frequency components efficiently can provide high performances. In view of this, in this paper, we develop a new residual block for image super resolution, in which the feature attention process is carried out by focusing on various high-frequency components of the feature tensors. Specifically, we design a novel multi-dimensional filter design technique for the task of image super resolution, and employ it for obtaining a finite impulse response (FIR) high-pass filter bank to be embedded in a deep super resolution network for the feature attention process. Moreover, we utilize two other feature attention processes in the proposed residual block, namely, multi-scale transformerbased and convolutional learnable feature attention mechanisms, to generate rich sets of feature maps for a deep super resolution network. The results of different experiments demonstrate the effectiveness of the various modules of the proposed residual block in enhancing the super resolution performance
Traffic Sign Classification for Autonomous Vehicles Using Split and Federated Learning Underlying 5G

By enabling self-driving capabilities, autonomous vehicles (AVs) are revolutionizing transportation. Artificial intelligence (AI) integrating with AVs improves their perception, decision-making, and control systems. Despite AVs' remarkable growth and acceleration, two significant challenges emerge, i.e., distributed data and computational overhead. To address these challenges, we proposed a novel architecture combining split and federated learning (FL) for traffic sign classification in AVs. Our method distributes deep learning (DL) models across multiple AV systems, allowing efficient DL model training and usage. To mitigate computational overhead on vehicular clients, split learning partitions have been applied between the vehicular clients and a server. Additionally, FL advances this approach by simultaneously addressing data decentralization concerns and constructing a strong traffic sign detection model. It achieves this by training the model individually on each client and on that individual client's data and transmitting only crucial weight parameters of the DL model; it also adds a layer of security by avoiding the direct transfer of location-revealing image data. To send model weight parameters in a timely manner, we employed the staggering benefits of a 5G network, which improves the accuracy of applied DL models in each communication round. The proposed architecture offers efficient and real-time model training and utilization for accurate traffic sign identification in AVs by harnessing the capabilities of split learning, FL, and 5G networks. The experimental findings show that our architecture is effective, with a maximum accuracy of 99.54 % on the German Traffic Sign Research Benchmark (GTSRB) dataset with five client architectures, contributing to improving AV technology and developing intelligent transportation systems. Further, we analyze the performance of a 5G network in terms of phase noise and modulation; it is depicted from the experiment that the 5G network is showing noteworthy performance than the 4G network.
WordPlay: An Interactive Story Creation Artwork for Public Spaces

This paper details the project, WordPlay, an interactive story creation platform meant for installation in public spaces. Using a similar method of the popular children's game "Mad Libs", WordPlay invites the public to choose from a selection of "nouns", "verbs", "adjectives", and "adverbs" printed on RFID (Radio Frequency Identification) cards and add them to the system. WordPlay collects and visualizes each word by remixing a selection of text-based scientific facts and quotes from prominent scientists. Once completed, the final story or passage is displayed on the screen, allowing the public to read their stories. The resulting text is time-stamped, printed locally, encoded into a unique URL, and uploaded to the project's website, where the public can read online what was created inside the exhibition space in real time.
An Interactive System Design Based on Biofeedback and Breathing Exercise

As society evolves, more and more individuals recognize that chronic tension is one of the primary causes of mental health issues. In tandem with the maturation of biofeedback research, biofeedback technology has been applied to relaxation training and stress management. The purpose of the study was to use the design thinking methodology to create an interactive system design that incorporates biofeedback techniques with traditional breathing relaxation techniques to reduce stress and enhance concentration. In this paper, we evaluated and reflected on the prototype design through user testing. The results indicate that the design of this interactive system is not only acceptable and promising, but also capable of producing a novel calming experience.
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets

We introduce the Collection Space Navigator (CSN), a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts that are associated with multidimensional data, such as vector embeddings or tables of metadata. Media objects such as images are often encoded as numerical vectors, based on metadata or using machine learning embeddings. Yet it remains a challenge to explore, analyze, and understand the resulting multidimensional spaces. Dimensionality reduction techniques such as t-SNE or UMAP often serve to project high-dimensional data into low dimensional visualizations, but require interpretation themselves given their typically abstract dimensions. The Collection Space Navigator provides a customizable interface that combines two-dimensional projections with an array of configurable multifunctional filters and navigation controls. The user is able to view and investigate collections by zooming and scaling, transforming between projections, and filtering dimensions via range sliders and text filters. Insights gained through these interactions can be used to augment original data via easy to use export capabilities. This paper comes with a functional online demo showcasing a large digitized collection of classical Western art. Users can reconfigure the interface to fit their own data and research needs, including projections and filter controls. This open source tool is intended to be applicable in a broad range of use cases, types of collections and across diverse disciplines.
Work vs. Leisure – Differences in Avatar Characteristics Depending on Social Situations

User avatars are a critical component in collaborative Virtual Environments. A multitude of tools exist, employing various avatar types for self-representation and the representation of others. However, the optimal appearance for these avatars remains unclear. Consumer applications predominantly utilize stylized avatars, which are less prevalent in enterprise sectors. To investigate users’ avatar preferences, we conducted three user studies in both non-immersive and immersive environments, exploring whether social contexts influence virtual self-avatar selection. We generated individual avatars based on photographs of 91 participants, comprising 71 employees in the automotive industry and 20 individuals external to the company. Our findings indicate that work situations, irrespective of the occupational domain, significantly impact self-avatar choices. Conversely, we observed no correlation between the display medium or personality dimensions and avatar selection. Drawing upon our results, we provide recommendations for future avatar representation in work environments.
Interactive NeuroKnitting: Knitting with Your Brain

Interactive NeuroKnitting: Knitting with Your Brain stems from our previous work about knitting and brain data. A self-made circular knitting machine Circular Knitic is activated on the user’s EEG data in real-time. It means that knitting happens only when a spectator wears BCI (brain-computer interface). The audience’s meditation and attention levels are translated to the knitting machine’s speed and pattern. Hence, the autonomous knitting machine is turned into a brain-data-driven mechanism that enables the audience to experience their brain activity through the knitting process, leaving the pattern in the garment behind. The growing collective brain-knitting installation is accompanied by the on-screen personal EEG data visualization overlaying AI-generated videos. A multimodal EEG data experience enables the viewers to understand their brain activity in novel and artistic ways.
Development of an immersive simulator for improving student chemistry learning efficiency

Virtual reality (VR) technology has been used for educational purposes in different learning contents during teaching and training. VR could improve users’ learning efficiency and motivation to study abstract concepts. This work designed a VR environment for chemistry education to support computer-mediated hands-on exercises, including Self-propagating high-temperature synthesis (SHS) and Electrode sheet fabrication (ESF). In our evaluation with 39 participants who wore heart beat measurement wearables, we compared the students’ performances in hands-on chemistry tasks, either with or without score-keeping and time-sensitive conditions. Accordingly, we designed questionnaires reflecting sixteen qualitative aspects (e.g., content, perspicuity, and interaction) and perceived user workloads. The experimental results indicate participants’ preferences and attitudes in terms of efficiency and sense of safety. 94.87% of participants reported that the learning simulator could improve learning efficiency, and 92.31% of the participants indicated that it can improve their sense of safety. The results of the data analysis show that the different learning scenarios we simulated have positive significance. Our findings shed light on the quality and learning performance of operational skills for chemistry education.