Reviewerly/exHarmony huggingface dataset:
Enhancing Underwater Images: Automatic Colorization using Deep Learning and Image Enhancement Techniques

Color restoration is challenging in underwater imagery. In underwater environments, red light is easily absorbed by water, resulting in the disappearance of red light at a few meters depth. However, the rapid advancement of deep learning techniques has provided possibilities for underwater color restoration. In this study, we developed an underwater color restoration system based on two deep-learning network architectures. We enhanced the quality of underwater images and explored the use of generative adversarial networks (GANs) for automatic coloring. We compensated for the absorbed red light in underwater environments to improve the color representation of restored images. We combined the WaterNet and neural colorization networks and retrained them to achieve the goal of color restoration. We evaluated the results by observing color palettes and conducted corresponding analyses using k-means, CIE-2000, and percentage differences in color channels.
Privacy and Security in Blockchain: A Comprehensive Analysis of Techniques and Threats

With its decentralised, secure, and transparent solutions, blockchain technology has become a paradigm-shifting force in a variety of industries. This comprehensive review moves around the complex world of blockchain applications, thoroughly classifies each type, analyses current issues, and looks ahead to potential future applications. The study, which spans industries including finance, healthcare, supply chain, and more, highlights how blockchain has the power to completely transform data management, transparency, and trust. The public, private, consortium, hybrid blockchain types are all systematically categorised in the study and each is suited to a different set of needs. The assessment highlights how blockchain's architecture can transform sectors that previously relied on middlemen by emphasising decentralised consensus methods. Key considerations include issues with interoperability, scalability, data privacy, and regulatory compliance. Based upon current literature in the field, the study emphasises the urgent necessity for comprehensive research and innovative approaches in order to address these gaps and facilitate the appropriate implementation of blockchain technology. This analysis gives a complete summary of the uses, problems, and future trajectories of this omnipotent technology. It serves as a necessary guide for researchers, practitioners, and policymakers, as they navigate the growing influence of blockchain.
Enhancing Sign Language Recognition Using Deep Convolutional Neural Networks

The advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays a vital role in the lives of people with hearing and speaking disabilities. This research aims to explore digital image processing and machine learning methods for efficiently building a sign language dataset and creating a sign language interface system. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze and interpret hand gestures and poses, converting them into natural language. The developed CNN model specifically focuses on improving the accuracy of predicting the American Sign Language alphabet. Despite variations in dataset conditions and size, the model achieved an exceptional accuracy rate of 98.73%. Additionally, it demonstrated a low loss value of 0.0539, indicating its robust performance.
Facial Image Reconstruction and its Influence to Face Recognition

This paper focuses on reconstructing damaged facial images using GAN neural networks. In addition, the effect of generating the missing part of the face on face recognition is investigated. The main objective of this work is to observe whether it is possible to increase the accuracy of face recognition by generating missing parts while maintaining a low false accept rate (FAR). A new model for generating the missing parts of a face has been proposed. For face-based recognition, state-of-the-art solutions from the DeepFace library and the QMagFace solution have been used.
No-Reference Image Sharpness Assessment Based on Perceptually-Weighted Image Gradients

The fact that human visual perception is highly spe-cialized for extracting structural information has been efficiently exploited in full-reference image quality assessment algorithms; additionally, it has been shown that the human perception of sharpness depends on the local image contrast. Since the image gradients can measure effectively the image structure, we propose a training-free no-reference objective image sharpness assessment method based on the statistical analysis of perceptually-weighted normalized -gradients of relevant pixels in the input image. Results over six subject-rated publicly available databases show that the proposed no-reference sharpness assessment algorithm correlates well with perceived sharpness and provides competitive performance when compared with state-of-the-art algorithms despite its low computational burden.
Blockchain Technology for Secure Smart Grid Access Control

The use of smart grid technologies, in the energy sector has brought about upgrade, such as high efficiency, lower operating costs and better energy management. However, it has also established security aspects. Safeguarding the smart grid is of importance due to its role as essential infrastructure. The innovative potential of blockchain holds promise in enhancing the security of the grid. This paper aims to explore how blockchain technology can be utilized for protecting the grid. It delves into the security issues associated with grids and investigates how blockchain can address them. Additionally, it provides an analysis of studies on blockchain and its application, in grid protection while considering the pros and cons of utilizing blockchain based solutions.
Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity

Abstract Symbolic event recognition systems detect event occurrences using first-order logic rules. Although existing online structure learning approaches ease the discovery of such rules in noisy data streams, they assume the existence of fully labelled training data. Splice is a recent online graph-based approach that estimates the labels of unlabelled data and makes it possible to learn such rules from semi-supervised training sequences of logical interpretations. However, Splice labelling depends significantly on the metric used to compute the distances of unlabelled examples to their labelled counterparts. Moreover, there is no guarantee about the quality of the labelling found in the local graphs that are built as the data stream in. In this paper, we propose a new online learning method, which includes an enhanced hybrid measure that combines an optimised structural distance, and a data-driven one. The former is guided by feature selection targeted to k NN classification, while the latter is a mass-based dissimilarity. Additionally, the enhanced Splice method, improves the graph construction process, by storing a synopsis of the past, in order to achieve more informed labelling on the local graphs. We evaluate our approach by learning Event Calculus theories for the tasks of human activity recognition, maritime monitoring, and fleet management. The evaluation suggests that our approach outperforms its predecessor, in terms of inferring the missing labels and improving the predictive accuracy of the underlying structure learning system.
An Efficient Self-Supervised Cross-View Training For Sentence Embedding

Abstract Self-supervised sentence representation learning is the task of constructing an embedding space for sentences without relying on human annotation efforts. One straightforward approach is to finetune a pretrained language model (PLM) with a representation learning method such as contrastive learning. While this approach achieves impressive performance on larger PLMs, the performance rapidly degrades as the number of parameters decreases. In this paper, we propose a framework called Self-supervised Cross-View Training (SCT) to narrow the performance gap between large and small PLMs. To evaluate the effectiveness of SCT, we compare it to 5 baseline and state-of-the-art competitors on seven Semantic Textual Similarity (STS) benchmarks using 5 PLMs with the number of parameters ranging from 4M to 340M. The experimental results show that STC outperforms the competitors for PLMs with less than 100M parameters in 18 of 21 cases.1
Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations

Abstract One-hot labels are commonly employed as ground truth in Emotion Recognition in Conversations (ERC). However, this approach may not fully encompass all the emotions conveyed in a single utterance, leading to suboptimal performance. Regrettably, current ERC datasets lack comprehensive emotionally distributed labels. To address this issue, we propose the Emotion Label Refinement (EmoLR) method, which utilizes context- and speaker-sensitive information to infer mixed emotional labels. EmoLR comprises an Emotion Predictor (EP) module and a Label Refinement (LR) module. The EP module recognizes emotions and provides context/speaker states for the LR module. Subsequently, the LR module calculates the similarity between these states and ground-truth labels, generating a refined label distribution (RLD). The RLD captures a more comprehensive range of emotions than the original one-hot labels. These refined labels are then used for model training in place of the one-hot labels. Experimental results on three public conversational datasets demonstrate that our EmoLR achieves state-of-the-art performance.
Discover, Explain, Improve: An Automatic Slice Detection Benchmark for Natural Language Processing

Abstract Pretrained natural language processing (NLP) models have achieved high overall performance, but they still make systematic errors. Instead of manual error analysis, research on slice detection models (SDMs), which automatically identify underperforming groups of datapoints, has caught escalated attention in Computer Vision for both understanding model behaviors and providing insights for future model training and designing. However, little research on SDMs and quantitative evaluation of their effectiveness have been conducted on NLP tasks. Our paper fills the gap by proposing a benchmark named “Discover, Explain, Improve (DEIm)” for classification NLP tasks along with a new SDM Edisa. Edisa discovers coherent and underperforming groups of datapoints; DEIm then unites them under human-understandable concepts and provides comprehensive evaluation tasks and corresponding quantitative metrics. The evaluation in DEIm shows that Edisa can accurately select error-prone datapoints with informative semantic features that summarize error patterns. Detecting difficult datapoints directly boosts model performance without tuning any original model parameters, showing that discovered slices are actionable for users.1
General then Personal: Decoupling and Pre-training for Personalized Headline Generation

Abstract Personalized Headline Generation aims to generate unique headlines tailored to users’ browsing history. In this task, understanding user preferences from click history and incorporating them into headline generation pose challenges. Existing approaches typically rely on predefined styles as control codes, but personal style lacks explicit definition or enumeration, making it difficult to leverage traditional techniques. To tackle these challenges, we propose General Then Personal (GTP), a novel framework comprising user modeling, headline generation, and customization. We train the framework using tailored designs that emphasize two central ideas: (a) task decoupling and (b) model pre-training. With the decoupling mechanism separating the task into generation and customization, two mechanisms, i.e., information self-boosting and mask user modeling, are further introduced to facilitate the training and text control. Additionally, we introduce a new evaluation metric to address existing limitations. Extensive experiments conducted on the PENS dataset, considering both zero-shot and few-shot scenarios, demonstrate that GTP outperforms state-of-the-art methods. Furthermore, ablation studies and analysis emphasize the significance of decoupling and pre-training. Finally, the human evaluation validates the effectiveness of our approaches.1
Twitter Data Analysis Using BERT and Graph-Based Convolution Neural Network

Twitter Data Analysis in Social media plays an essential role in spreading information during disasters and needy situations. May it be for seeking help or sharing and updating the seriousness of the situation or communicating the status or response to the public in need, social media plays a major role in case of emergencies. This research focuses on generating word embedding vectors using DistillBERT and generating a similarity matrix to construct the Graph-based Convolution Network model to classify text sequences and to analyse the performance of the DistillBERT with GCN model in Text classification. To implement this, contextual word embeddings are introduced for generating word vectors. The contextual word embeddings and the concept of graph neural networks has gained importance in capturing contextual relationship among the text to improve the performance of classification. This semi supervised model is used to detect and classify the need and availability of resource tweets with an accuracy of 96% as compared with state-of-the-art approaches.
SCARS: Suturing wounds due to conflicts between non‐functional requirements in autonomous and robotic systems

Abstract In autonomous and robotic systems, the functional requirements (FRs) and non‐functional requirements (NFRs) are gathered from multiple stakeholders. The different stakeholder requirements are associated with different components of the robotic system and with the contexts in which the system may operate. This aggregation of requirements from different sources (multiple stakeholders) often results in inconsistent or conflicting sets of requirements. Conflicts among NFRs for robotic systems heavily depend on features of actual execution contexts. It is essential to analyze the inconsistencies and conflicts among the requirements in the early planning phase to design the robotic systems in a systematic manner. In this work, we design and experimentally evaluate a framework, called SCARS, providing: (a) a domain‐specific language extending the ROS2 Domain Specific Language (DSL) concepts by considering the different environmental contexts in which the system has to operate, (b) support to analyze their impact on NFRs, and (c) the computation of the optimal degree of NFR satisfaction that can be achieved within different system configurations. The effectiveness of SCARS has been validated on the iRobot Create3 robot using Gazebo simulation.
CNN Architecture Design Impact in DL-Based Channel Estimation Algorithms

This paper presents a trade-off study concerning the impact of convolutional neural network (CNN) architectures design on the performance of deep learning (DL) based channel estimation under orthogonal frequency-division multiplexing (OFDM) systems. Although CNNs have demonstrated remarkable performance in channel estimation, concerns arise regarding hardware implementation due to computation and power consumption limitations, particularly when dealing with limited hardware resources on the network edge. This study investigates the trade-off between the number of convolutional layers, the size and the number of convolutional filters, and the obtained CNN-based channel estimation performance, measured by the mean square error (MSE), error vector magnitude (EVM) and bit error rate (BER). The resource overhead is also analyzed considering the learnable parameters, memory usage estimation, and number of multiply-accumulate (MAC) operations.
Enhancing Smart Building Security: Decentralized App Implementation with Blockchain Technology

With the recent advancements in the field of internet-of-things (IoT) and artificial intelligence, smart buildings are gaining popularity. Smart buildings play an essential part in offering an upscale atmosphere with lower energy usage and environmental concern in today's modern living. However, with the increased in consumer interaction with the building through IoT devices, it has open various ports for the intruders or unwanted malicious players to enter into the smart building system. In view of this the paper proposes integration of blockchain technology into smart building for enhancing the security. The paper focuses on the illumination/lighting and video surveillance systems by developing a blockchain-based decentralized application (DApp) with smart contracts and a blockchain ledger. The proposed DApp ensures the system security by authentication process with help of private key verification and thus avoids unwanted intruders into the system. For testing the effectiveness of proposed DApp a comparative analysis in terms of gas fees, transaction prioritization, and transaction speeds is presented which offers the users flexibility in balancing cost and efficiency. From the analysis it can be emphasize that the blockchain provides a resilient solution to the challenges posed by the current system.
Heterogeneous Ensemble for Multi-Step Solar Irradiance Forecasting in Brazilian's Sites

Multi-step solar irradiance forecast is an important part of a successful power grid management. However that forecast can be difficult due to nonlinear patterns and random fluctuation. This work applies statistical and machine learning methods to forecast solar irradiance time series from three different locations of Brazil. The applied models are: Autoregressive Integrated Moving Average (ARIMA), Deep Belief Network (DBN), Extreme Learning Machine (ELM), Multi Layer Perceptron neural network (MLP), Random Forest (RF), and Support Vector Regression (SVR). Taking into account these methods, an ensemble of them is proposed, which has the objective of reducing: the uncertainty of selecting a single model, overfitting, and underfitting. Compared with single models and based on two performance metrics, the ensemble found good overall results when applied in the time series with horizons of 6, 12 and 24 hours ahead.
Processing of Experimental Results Using Databases on the Example of the Batch Process of Isoprene Polymerization

This article presents an approach to organizing a database for storing and processing the results of experiments on the polymerization of isoprene using microheterogeneous catalytic systems in a batch mode. The proposed approach can be applied both to work with production data and with the results of computational experiments. The implementation of this approach will automate the process of recording data into a database, which will greatly facilitate the structuring of information. As an example, the work presents the results of a computational experiment on the polymerization of isoprene in a reactor in the presence of a titanium-containing catalyst system.
Neighborhood Models for Hierarchical Algorithms

Neighborhood models (static or dynamic systems on digraphs) are usually used in problems of modeling complex technologies distributed in space and time. Another application of neighborhood models is the interpretation of algorithms that use directed or undirected graphs (not to be confused with "graph algorithms"). Examples of such algorithms are the finite difference method, finite element method, neural networks, regression trees and others. Briefly, two directions of application of neighborhood models can be defined as "technological" and "algorithmic". The most relevant is, of course, the technological direction, that is, the task of modeling real processes. In the algorithmic direction we are talking only about interpretation, which, of course, is not a necessary component of the algorithm. However, the neighborhood models of algorithms are sometimes useful and deserve attention. In this article, we analyze, on the language of neighborhood models, two "hierarchical" algorithms that use directed trees, namely, the well-known CART (Classification and regression trees) algorithm and the LRT-algorithm ("Lebesgue regression trees") for recurrent hierarchical identification, developed by the authors.
Comparison of Wavelet-Based Image Processing Methods Before Neural Network Training

The problem of image classification using neural networks is considered. Various methods of image preprocessing based on wavelet analysis are used to optimize neural network training. Among these methods are the Haar wavelet transformation, the Dobeshi wavelet transformation, as well as combined methods that include wavelet transformations and moments of color.A Python script is developed for the experiments. During the work methods are compared with each other to identify the most effective among others.
Modal Controllers Design of Interval Systems With Aperiodic Degree of Stability Based on the Method of Dominant Poles

The article deals with the problem of determining the parameterizations of a linear controller that provides aperiodic transient processes in a system with interval parameters. To cope with the above mentioned issue, a decomposition approach is proposed. It is based on dividing the characteristic polynomial of the system into a dominant one, which determines the dominant real pole, and a free one, which specifies the location of the remaining poles. In order to arrange the free poles at the maximum distance from the dominant one, the conditions for the maximum degree of dominance are specified. In this paper, a technique is described that has been developed for the modal design of linear controllers. Its application is considered through a numerical example.
Spread Spectrum Steganography In Audio Containers

In this paper, attention is paid to audio containers and the possibilities of using them as carriers of steganographic information. One of the most proven methods for increasing the reliability of stegoalgorithms is distributed embedding. An embedding method based on the use of a pseudo-random sequence generator and an algorithm for its implementation are proposed. The proposed method is investigated and the results presented show that it meets standard reliability metrics and is fully suitable for use in real-world settings.
AI Authorship and Ownership of Intellectual Property in Industrial Power and Control Systems

Artificial intelligence (AI) systems are increasingly capable of autonomous generation of creative and inventive works that potentially warrant intellectual property (IP) protections. This paper analyzes the implications of granting AI systems legal authorship and ownership status over output like patents and copyrights. Benefits, including appropriately incentivizing AI innovation and allowing compensation for human contributors, are discussed. However, risks around over-concentration of AI rights, economic disruption, algorithmic bias, and human dignity also arise. Analysis proposes solutions encompassing IP policy reforms, transparency requirements, and oversight mechanisms to ensure responsible implementation. But significant legal ambiguities and coordination challenges remain regarding AI taking on creative roles comparable to humans. This paper aims to spur discourse on adapting IP frameworks and economic models to an AI future characterized by autonomous generation of inventions and works.