Reviewerly/exHarmony huggingface dataset:
A Dynamic Hybrid Cryptosystem Using Chaos and Diffie–Hellman Protocol: An Image Encryption Application

Images with sensitive content require encryption for storage and transmission. Symmetric schemes can cipher them, while an asymmetric cryptosystem can distribute the secret key safely. For this reason, we propose a dynamic hybrid cryptosystem, which ciphers images and transfers its private keys. It has a symmetric algorithm that applies the Lorenz equations for generating different boxes and permutations in every encryption process and round. Since the secret key concatenates two private numbers, an asymmetric algorithm is included for its key distribution. The proposal uses the Diffie–Hellman protocol with ElGamal for obtaining a seed and building 128 strings. Then, the SHA-512 is applied in each of them a number of times associated with the secret key value in its blockchain representation. The resultant strings are concatenated to conform to the public key. Finally, the tests indicate that the cryptosystem resists differential, linear, algebraic, and brute-force attacks. Its cipher quality is high according to the entropy, correlation, DFT, NPCR, UACI, AC, texture analysis, and goodness of fit test. Additionally, occlusion, additive, multiplicative, and the proposed χ2 noise attacks are simulated on encrypted images. Finally, the sharpness loss is measured with the Similarity Parameter and improved with a filter 5 × 5.
CNN based color balancing and denoising technique for underwater images: CNN-CBDT

Underwater image deteriorates due to the scattering effect of light in the water. The challenging factor in underwater image analysis is the blurring of the image and color distortion. Various techniques were explored to offer solutions for underwater image restoration. But, still, it has some regression exists in these techniques. This proposal introduces the deep CNN-based color balancing and denoising technique (CNN-CBDT) to enhance underwater images. One of the advantages of underwater characteristics is color, mostly green and blue. Due to its low color contrast, the image exists in fuzzy nature. CNN-based CBDT restores the image with the help ReLU unit in the CNN. Lastly, the suggested method's cutting-edge performance is validated by comparing experimental findings to GLNet, Histeq, and ACE algorithms in conditions of structural similarity (SSIM), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), UCIQE, and UIQM. The suggested approach eliminates the impact of underwater elements, enhancing the color of the picture. It enhances PSNR by 17% having the highest value of 19.580 and SSIM by 15% having a value of 0.952. To make it applicable to real robots, the computation speed is calculated. As a result, the proposed method achieved a computation speed of 9.868 frames per second.
Multi-level correlation mining framework with self-supervised label generation for multimodal sentiment analysis

Fusion and co-learning are major challenges in multimodal sentiment analysis. Most existing methods either ignore the basic relationships among modalities or fail to maximize their potential correlations. They also do not leverage the knowledge from resource-rich modalities in the analysis of resource-poor modalities. To address these challenges, we propose a multimodal sentiment analysis method based on multilevel correlation mining and self-supervised multi-task learning. First, we propose a unimodal feature fusion- and linguistics-guided Transformer-based framework, multi-level correlation mining framework, to overcome the difficulty of multimodal information fusion. The module exploits the correlation information between modalities from low to high levels. Second, we divided the multimodal sentiment analysis task into one multimodal task and three unimodal tasks (linguistic, acoustic, and visual tasks), and designed a self-supervised label generation module (SLGM) to generate sentiment labels for unimodal tasks. SLGM-based multi-task learning overcomes the lack of unimodal labels in co-learning. Through extensive experiments on the CMU-MOSI and CMU-MOSEI datasets, we demonstrated the superiority of the proposed multi-level correlation mining framework to state-of-the-art methods.
The use of Artificial Intelligence in the Banking Industry

The banking industry is rapidly adopting artificial intelligence (AI)-enabled technologies to improve efficiency, reduce costs, and enhance customer experience. The paper utilizes case studies and data from various research papers to analyze the use of AI in different areas of the banking industry. The paper highlights that AI-enabled technologies can be applied in various areas of the banking industry, with significant potential for improving decision making, reducing the risk of fraud, and enhancing customer experience. The study also provides examples of AI implementations in various banking domains, such as risk assessment, credit approval process, investment/portfolio management, and others. The use of AI in fraud detection, personalized financial advisory services, and automated customer support is discussed in detail, including examples from major financial institutions. Additionally, the paper discusses how AI can be used in claims management, wealth management, and loan and credit management. Several research studies have been reviewed that propose AI-based credit scoring models and loan underwriting systems to enhance the accuracy and efficiency of loan management processes. Overall, this paper provides a comprehensive survey of the opportunities and challenges associated with the use of AI in the banking industry, highlighting the potential for AI to transform the way banks operate and serve their customers.
Brain-inspired nanophotonic spike computing: challenges and prospects

Abstract Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III–V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.
Social Cohesion in Interactive Digital Heritage Experiences

Presently, social cohesion (SC) is a priority at different levels. Cultural heritage is an ideal context to promote SC through interactive digital technologies designed to engage groups of visitors. The purpose of the present study is to identify how to design digital heritage applications for SC and how to measure it. The results are based on the design of a cultural probe kit used to identify the design elements on top of which a collaborative and hybrid prototype, the Brancacci POV, was developed. Here, we analysed the results of this prototype, which included 107 visitors with respective groups of 5 participants and guided by an expert. From this analysis, the possibility of strengthening SC when collaborative tasks are included emerged. Additionally, it appeared to be possible to shorten the distance between citizens and cultural institutions if “mediated dialogue” approaches were adopted and if focus, motivation, trust and “in-group” perception of inclusion emerge when digital heritage experiences were set in intimate and quiet environments.
Deep learning for diffusion in porous media

We adopt convolutional neural networks (CNN) to predict the basic properties of the porous media. Two different media types are considered: one mimics the sand packings, and the other mimics the systems derived from the extracellular space of biological tissues. The Lattice Boltzmann Method is used to obtain the labeled data necessary for performing supervised learning. We distinguish two tasks. In the first, networks based on the analysis of the system's geometry predict porosity and effective diffusion coefficient. In the second, networks reconstruct the concentration map. In the first task, we propose two types of CNN models: the C-Net and the encoder part of the U-Net. Both networks are modified by adding a self-normalization module [Graczyk et al. in Sci Rep 12, 10583 (2022)]. The models predict with reasonable accuracy but only within the data type, they are trained on. For instance, the model trained on sand packings-like samples overshoots or undershoots for biological-like samples. In the second task, we propose the usage of the U-Net architecture. It accurately reconstructs the concentration fields. In contrast to the first task, the network trained on one data type works well for the other. For instance, the model trained on sand packings-like samples works perfectly on biological-like samples. Eventually, for both types of the data, we fit exponents in the Archie's law to find tortuosity that is used to describe the dependence of the effective diffusion on porosity.
Autonomous Navigation of Robots: Optimization with DQN

In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots.
Entity Linking Method for Chinese Short Texts with Multiple Embedded Representations

Entity linking, a crucial task in the realm of natural language processing, aims to link entity mentions in a text to their corresponding entities in the knowledge base. While long documents provide abundant contextual information, facilitating feature extraction for entity identification and disambiguation, entity linking in Chinese short texts presents significant challenges. This study introduces an innovative approach to entity linking within Chinese short texts, combining multiple embedding representations. It integrates embedding representations from both entities and relations in the knowledge graph triples, as well as embedding representations from the descriptive text of entities and relations, to enhance the performance of entity linking. The method also incorporates external semantic supplements to strengthen the model’s feature learning capabilities. The Multi-Embedding Representation–Bidirectional Encoder Representation from Transformers–Bidirectional Gated Recurrent Unit (MER-BERT-BiGRU) neural network model is employed for embedding learning. The precision, recall, and F1 scores reached 89.73%, 92.18%, and 90.94% respectively, demonstrating the effectiveness of our approach.
Implementation of Content-Based Image Retrieval Using Artificial Neural Networks

CBIR (Content Based Image Retrieval) has become a critical domain in the previous decade, owing to the rising demand for image retrieval from multimedia databases. Typically, we take low-level (colour, texture and shape) or high-level (when machine learning techniques are used) features out of the photos. In our research, we examine the CBIR system utilising three machine learning methods, namely SVM (Support Vector Machine), KNN (K Nearest Neighbours), and CNN (Convolution Neural Networks), using Corel 1K, 5K, and 10K databases, by splitting the data into 80% train data and 20% test data. Moreover, compare each algorithm’s accuracy and efficiency when a specific task of image retrieval is given to it. The final outcome of this project will provide us with a clear vision of how effective deep learning, KNN and CNN algorithms are to finish the task of image retrieval.
A Novel Evidence Combination Method Based on Improved Pignistic Probability

Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%.
Applications of Virtual and Augmented Reality Technology to Teaching and Research in Construction and Its Graphic Expression

Immersive virtual reality (VR) technology is constantly evolving and is used in various fields of work in our daily lives. However, traditional methodologies are still mostly used in education. There is a disconnect between education and the world of work, and future professionals need to be updated to new working methods in order to be able to compete in the labour market. The main objective of this study is based on testing the effectiveness of digital didactic resources in the teaching–learning process, as well as providing students with the digital competences to use these tools. The methodology generated by the research team in the development of architectural projects has been applied in teaching workshops with experimental and motivating strategies for students using accessible digital teaching resources that allow autonomous learning. With this we have proven the effectiveness of the method and the opportunities it offers us in education. The results obtained have been twofold: on the one hand we have increased the interest and motivation of the students by making them participants in their own training, and on the other hand we have started a fruitful path in the generation of repositories with virtual didactic content that allows us to provide greater accessibility to knowledge.
Management and disclosure of quality issues in forensic science: A survey of current practice in Australia and New Zealand

The investigation of quality issues detected within the forensic process is a critical feature in robust quality management systems to provide assurance of the validity of reported results and inform strategies for continuous improvement and innovation. A survey was conducted to gain insight into the current state of practice in the management and handling of quality issues amongst the government service provider agencies of Australia and New Zealand. The results demonstrate the value of standardised quality system structures for the recording and management of quality issues, but also areas where inconsistent reporting increases the risk of overlooking important data to inform continuous improvement. With new international changes requiring mandatory reporting of quality issues, this highlights compliance challenges that agencies will face. This study reinforces the need for further research into the standardisation of systems underpinning the management of quality issues in forensic science to support transparent and reliable justice outcomes.
Assessment of Supporting Visual Learning Technologies in the Immersive VET Cyber-Physical Learning Model

Humanity faces diverse technological, societal, and sociological challenges. Digitalization is being integrated into every aspect of our lives. Technologies are developing rapidly and the ways in which we live and learn are changing. Young people are acquiring information and learning in a different way than in the recent past. Education systems are no longer keeping up with the development of technology. Education systems need to adapt and introduce technologies that motivate students and ultimately contribute to higher learning goals. To this end, we need to develop modern learning models that support education and technological development. In previous research, we developed and evaluated a state-of-the-art learning model, the CPLM. We built on this with a new study, in which we assessed the difference between the cognitive activities of attention and meditation in students during the viewing of a classical educational video, a 360° video, and an AR app on a screen. We found that the 360° video had the greatest impact on students’ attention and is consequently suitable for initially motivating students in the proposed learning model. We made a proposal for a modern educational model and possibilities for further research.
TFC-GCN: Lightweight Temporal Feature Cross-Extraction Graph Convolutional Network for Skeleton-Based Action Recognition

For skeleton-based action recognition, graph convolutional networks (GCN) have absolute advantages. Existing state-of-the-art (SOTA) methods tended to focus on extracting and identifying features from all bones and joints. However, they ignored many new input features which could be discovered. Moreover, many GCN-based action recognition models did not pay sufficient attention to the extraction of temporal features. In addition, most models had swollen structures due to too many parameters. In order to solve the problems mentioned above, a temporal feature cross-extraction graph convolutional network (TFC-GCN) is proposed, which has a small number of parameters. Firstly, we propose the feature extraction strategy of the relative displacements of joints, which is fitted for the relative displacement between its previous and subsequent frames. Then, TFC-GCN uses a temporal feature cross-extraction block with gated information filtering to excavate high-level representations for human actions. Finally, we propose a stitching spatial-temporal attention (SST-Att) block for different joints to be given different weights so as to obtain favorable results for classification. FLOPs and the number of parameters of TFC-GCN reach 1.90 G and 0.18 M, respectively. The superiority has been verified on three large-scale public datasets, namely NTU RGB + D60, NTU RGB + D120 and UAV-Human.
An invitation to distributed quantum neural networks

Abstract Deep neural networks have established themselves as one of the most promising machine learning techniques. Training such models at large scales is often parallelized, giving rise to the concept of distributed deep learning. Distributed techniques are often employed in training large models or large datasets either out of necessity or simply for speed. Quantum machine learning, on the other hand, is the interplay between machine learning and quantum computing. It seeks to understand the advantages of employing quantum devices in developing new learning algorithms as well as improving the existing ones. A set of architectures that are heavily explored in quantum machine learning are quantum neural networks. In this review, we consider ideas from distributed deep learning as they apply to quantum neural networks. We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models, though the unique aspects of quantum data introduce new vulnerabilities to both approaches. We review the current state of the art in distributed quantum neural networks, including recent numerical experiments and the concept of circuit-cutting .
Readability across Time and Languages: The Case of Matthew’s Gospel Translations

We have studied how the readability of a text can change in translation by considering Matthew’s Gospel, written in Greek, translated into Latin and 35 modern languages. We have found that the deep-language parameters CP (characters per word), PF (words per sentence), IP (words per interpunctions), MF (interpunctions per sentence) and a universal readability index GU of each translation are so diverse from language to language, and even within a given language for which there are many versions of Matthew—such as in English and Spanish—that the resulting texts mathematically seem to be diverse. The several tens of versions of Matthew’s Gospel studied appear to address very diverse audiences. If a reader could understand all of them well, he/she would have the impression of reading texts written by diverse authors, although all of them tell the same story.
SCOUT: Security by computing OUTliers on activity logs

The current increase in cybercrime is demanding more effective and efficient data exploration and analysis solutions that can help analysts to detect cyberattacks. However, the huge amount of data generated continuously confronts a number of technological difficulties and classical algorithms must be often redesigned to be able to deal with this seemingly endless stream of information coming from past activity logs and real-time data. In this paper, we propose a novel methodology able to identify security threats in activity logs. The contribution of the paper is twofold: we propose an encoding technique, based on prime numbers, that can be used to represent in a compact way a set of activities, we then describe an outlier detection algorithm which, based on the encoded activities, is able to detect malicious behavior. The extensive experimental analysis proved the effectiveness of the proposed methodology.