Reviewerly/exHarmony huggingface dataset:
Exploring quantum learning in the smart grid through the evolution of noisy finite fourier series

The decentralization and softwarization of modern industrial control systems such as the electric grid has resulted in greater efficiency, stability and reliability but these advantages come at a price of higher likelihood of cyberattacks due to the resulting increase in cyberattack surface. Traditional cyberattack detection techniques such as rule-based anomaly detection have an important role to play in first response. However, given the data-rich environment of the modern electric grid, current research thrusts are focused on integrating data-driven machine learning techniques that automatically learn to detect anomalous modes of operation and predict the presence of new attacks. Quantum machine learning (QML) is a subset of machine learning that aims to leverage quantum computers to obtain a learning advantage by means of a training speed-up, data-efficiency, or other form of performance benefit. Questions remain regarding the practical advantages of QML, with the vast majority of existing literature pointing to its greater utility when applied to quantum data rather than classical data, which within a smart grid environment include TCP/IP packets or telemetry measurements. In this paper, we explore a scenario where quantum data may arise in the smart grid, and exploit a quantum algorithmic primitive previously proposed in the literature to demonstrate that in the best-case, QML can provide accuracy advantages of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>&gt;</mml:mo><mml:mn>25</mml:mn></mml:math> percentage points when predicting anomalies.
Asparagus: Automated Synthesis of Parametric Gas Upper-Bounds for Smart Contracts

Modern programmable blockchains have built-in support for smart contracts, i.e. ‍programs that are stored on the blockchain and whose state is subject to consensus. After a smart contract is deployed on the blockchain, anyone on the network can interact with it and call its functions by creating transactions. The blockchain protocol is then used to reach a consensus about the order of the transactions and, as a direct corollary, the state of every smart contract. Reaching such consensus necessarily requires every node on the network to execute all function calls. Thus, an attacker can perform DoS by creating expensive transactions and function calls that use considerable or even possibly infinite time and space. To avoid this, following Ethereum, virtually all programmable blockchains have introduced the concept of “gas”. A fixed hard-coded gas cost is assigned to every atomic operation and the user who calls a function has to pay for its total gas usage. This technique ensures that the protocol is not vulnerable to DoS attacks, but it has also had significant unintended consequences. Out-of-gas errors, i.e. ‍when a user misunderestimates the gas usage of their function call and does not allocate enough gas, are a major source of security vulnerabilities in Ethereum. We focus on the well-studied problem of automatically finding upper-bounds on the gas usage of a smart contract. This is a classical problem in the blockchain community and has also been extensively studied by researchers in programming languages and verification. In this work, we provide a novel approach using theorems from polyhedral geometry and real algebraic geometry, namely Farkas’ Lemma, Handelman’s Theorem, and Putinar’s Positivstellensatz, to automatically synthesize linear and polynomial parametric bounds for the gas usage of smart contracts. Our approach is the first to provide completeness guarantees for the synthesis of such parametric upper-bounds. Moreover, our theoretical results are independent of the underlying consensus protocol and can be applied to smart contracts written in any language and run on any blockchain. As a proof of concept, we also provide a tool, called “Asparagus” that implements our algorithms for Ethereum contracts written in Solidity. Finally, we provide extensive experimental results over 24,188 real-world smart contracts that are currently deployed on the Ethereum blockchain. We compare Asparagus against GASTAP, which is the only previous tool that could provide parametric bounds, and show that our method significantly outperforms it, both in terms of applicability and the tightness of the resulting bounds. More specifically, our approach can handle 80.56% of the functions (126,269 out of 156,735) in comparison with GASTAP’s 58.62%. Additionally, even on the benchmarks where both approaches successfully synthesize a bound, our bound is tighter in 97.85% of the cases.
A Balanced Design of Digital Tools in Primary Education

This chapter consisted of the production, implementation, and evaluation of digital teaching resources for integrating information and communication technologies (ICT) in primary education in Spain. Once the relationship between ICT and education was explained, balanced digital educational materials were designed, taking as a reference the legislative framework of the LOMCE and Royal Decree 157/2022, which establishes the basic curriculum for primary education. The digital resources have covered the different areas of primary education and have been characterised by fostering a balanced digital competence enabling ICT work in a cross-curricular manner in different subjects. They have been put into practice and evaluated to analyse their suitability for primary education. The conclusions drawn show the importance of adequate teacher training to be able to include these resources at educational centres in a balanced way. The main objective of this work is to design balanced digital teaching resources that make it possible to integrate ICT in the different subjects of primary education.
User Experience Analysis on Camp404 Academy E- Learning System

This research aims to understand the user experience and define the problems users face while accessing the Camp404 Academy online learning website. The study is based on the high percentage of participants who still need to complete the Bootcamp activities through the available online learning website, reaching 83% of all participants. Of the participants who participated in the survey, 71% stated that they still need to complete the Bootcamp activities due to difficulties using the online learning website. This research includes four processes on the Camp404 Academy online learning website: the registration process, program access process, class access process, and learning material access process. This research uses the design thinking method, which consists of five stages: Empathize, Define, Ideate, Prototype, and Test. This research produces a recommendation design based on the needs of Camp404 Academy users. The recommendation design then undergoes a testing process. It is found that the success rate of the recommendation design compared to the current design increases from 63.25% to 93.83% of questions being able to be answered by respondents (answers can be found) as well as a decrease in the number of questions that cannot be answered by respondents (responses not found) from 36.75% to 6.17%. PT Digital Innovation Campus can consider the results of this research in developing the user experience of the Camp404 Academy online learning website.
Comparative Analysis of Deep Learning Models for Web Defacement Detection Based on Textual Context

Web defacement refers to the unauthorized alteration of the appearance or content of a website, which can have detrimental effects on its operations and financial standing. Deep learning techniques have shown promise in detecting web defacement incidents. This research explores the application of three deep learning models, namely Bi-LSTM, GRU, and BERT, for web defacement detection. The study collects data from various sources, including defaced and regular websites, followed by preprocessing steps such as case folding. The selected models are then employed to build the detection models. Evaluation metrics such as accuracy, precision, and F1 score are used to compare the performance of the models. The findings reveal that the Bi-LSTM model outperforms the GRU and BERT models in terms of accuracy (0.990), precision (0.984), and F1 score (0.953) for classifying web defacement based on textual content. This superiority can be attributed to the Bi-LSTM model’s ability to process sequential information and capture contextual dependencies using the recurrent neural network architecture. It leverages bidirectional information flow, enabling better understanding and representation of the text. However, it is essential to consider other factors impacting model performance, such as dataset characteristics, data preprocessing techniques, and model configurations. The results of this research provide valuable insights into utilizing deep learning models, particularly Bi-LSTM, for effective web defacement detection.
Federated News Recommendation Model Learning

The objective of the News Recommendation System (NRS) is to match news representations with the interests of users, which are learned from their past interactions with news. However, current NRS methods primarily focus on storing user behavior data, which raises concerns regarding privacy. In this study, we present an approach for safeguarding the privacy of news recommendations using federated learning. This method avoids the centralized storage of user behavior data by storing it locally on each user’s device, reducing privacy risks. By leveraging collaborative learning, we can train accurate news recommendation models using a large number of user devices. The actions performed by users on their devices provide valuable insights, and the model gradient is computed based on these local actions. The gradient computed for the chosen user is transmitted to the server for the purpose of updating the global model. This updated global model is then sent back to each user for them to perform local model updates. To enhance user privacy, we incorporate local differential privacy, which randomizes the gradient before uploading it to the server. Empirical findings based on real-world datasets illustrate the effectiveness of our approach in achieving a harmonious balance between recommendation performance and user privacy.
Investigating the Impact on Data Recovery in Computer Forensics

Defragmentation can potentially be employed as a tactic by perpetrators to conceal, misrepresent, or eliminate digital evidence. This study explores the effects of minor defragmentation, a potential method to conceal digital evidence, on recovering file system data in digital forensics. Our investigation sought to determine the influence of minor defragmentation on the effectiveness of data recovery and to identify methods that can augment the success rate post-defragmentation. We limited the scope of this study to defragmentation in Hard Disk Drives (HDDs), solid-state drives (SSDs), and USB drives. A mixed-method approach employs a literature review, case studies, and controlled experiments. Comparative analysis was used as the main data analysis technique to investigate its impact. Preliminary findings suggest that minor defragmentation hampers data recovery; however, certain strategies can augment success rates. These results can significantly influence the development of data recovery policies, particularly those of digital forensic analysts and law enforcement. The primary objective of this study is to bolster the efficiency and dependability of file system data recovery post-defragmentation while upholding ethical and legal standards.
Stacking Neuro-Neo-Fuzzy System for Pattern Recognition and Its Online Learning

A hybrid system of computational intelligence that is designed to solve image recognition tasks is proposed. This system is a stacking hybrid of an adaptive neuro-fuzzy system and neo-fuzzy neuron that provides improved abilities. The distinctive feature of the system under consideration is that synaptic weights are replaced with nonlinear synapses that are universal approximators of the separating hypersurfaces of the complex form. The system is designed to solve pattern recognition tasks in two modes, solving Data Stream Mining tasks, when information is fed sequentially to the system online for processing, and in batch mode as well. This system is characterized by simple computational complexity and high speed which is proved by the computational experiment results.
The Method of Structural Identification of Interval Nonlinear Models with the Selection of Structural Elements based on the Gradient of the Objective Function

There is proposed a new method of structural identification of interval nonlinear models in the paper, which is based on the selection of structural elements for the synthesis of the model structure using the analysis of the gradient of the objective function. The necessary and sufficient conditions for the completeness or optimality of a set of structural elements are defined and substantiated based on the analysis of the gradient of the objective function, and the basic rules for the selection of structural elements are formulated. A structural identification algorithm was implemented and a study of its convergence was carried out.
An improved small target traffic sign detection algorithm for YOLOv5s

The recognition of traffic signs is a significant challenge for the development of intelligent transportation systems. The small target characteristics of traffic signs during automobile driving make them difficult to recognize, leading to easily missed detection and other issues. To address small target traffic signs, we have made corresponding improvements based on the YOLOv5s algorithm model. Firstly, we enhance the Mosaic online data augmentation in the YOLOv5s algorithm and remove incomplete samples during the training process by setting up a threshold. Secondly, we introduce a small target detection head containing more shallow features, removing the detection head used for detecting large targets. Within the feature pyramid, multi-scale attention feature fusion replaces the original concat features fusion, reducing conflicts in features fusion at different levels. Finally, considering the sensitivity to location deviation when calculating the loss for small targets, we combine the Normalized Wasserstein Distance loss and the Focal EIOU loss as location loss. Experiment findings on the CCTSDB-2021 dataset reveal that our improvements have the most obvious enhancement effect on small target detection, increasing mAP@0.5 (mean Average Precision) by 3.5%.
ICT for Integrating the Project-Based Learning Method in Bulgarian Education

The introduction of information and communication technologies in education is transforming the way of teaching, learning and managing the educational process. This has been particularly noticeable over the past two decades, seeing not only the digitalization of the educational process, but also the emergence of new teaching and learning methods and approaches. These methods serve as a link between the traditional and the innovative, between the goals and results of modern education. In this paper, ICT for integrating project-based learning as a method linking traditions with innovation and technology in education are explored. An experiment was conducted with three groups of students who were trained by traditional and project-based methods during their full-time studies. At the end of the experiment, students were evaluated against the teaching method. The results show a better success of the group with project-based learning, which confirms the main advantages of the method for achieving the goals of 21st century education.
Quantum Advantage Applied – Research on Probability of Success

Quantum computing is promised to have the potential to revolutionize many industries, providing faster and more accurate solutions to complex problems such as financial sector examples portfolio management, wealth management, risk analysis, and market predictions. However, the adoption of quantum computing faces several challenges such as technological limitations, integration with current IT systems, and potential risks to encryption methods. In this paper, we review recent research on quantum computing, discussing potential use cases, benefits, risks, and issues. We also highlight the need for further research to address these challenges before widespread adoption of quantum computing can occur. As financial sector appears to present most promising results it is considered as a key topic of this article.
The Tacotron2-based IPA-to-Speech speech synthesis system

To help language learners better understanding the pronunciation of one language, in this paper, we proposed an IPA-to-Speech speech synthesis system which aims to generate high quality human speech from written language in IPA format. There are mainly two parts in our system: a Transformer-based G2P converter and a Tacotron2-based speech synthesis system. The purpose of the G2P converter is to build the training data, all the English sentences in LJSpeech can be converted into their IPA formats by this converter, and the speech synthesis module intend to generate the speech from IPA sentences. The word error rate and phoneme error rate were utilized to evaluate the G2P converter and the mean opinion score was used to evaluate the performance of the speech synthesis. Also, this work inspired us to use the IPA format represent the dialects, in the future work, we will continue this research on the dialect recognition and generation.
Best practice for sampling in automated parallel synthesizers

The current sampling method for automated parallel synthesizers is reviewed with special focus on the sealing of sampling vessels and new best practice for automated sampling for, e.g. , SEC or GC is established.
Waste-YOLO: towards high accuracy real-time abnormal waste detection in waste-to-energy power plant for production safety

Abstract Due to the danger of explosive, oversize and poison-induced abnormal waste and the complex conditions in waste-to-energy power plants (WtEPPs), the manual inspection and existing waste detection algorithms are incapable to meet the requirement of both high accuracy and efficiency. To address the issues, we propose the Waste-YOLO framework by introducing the coordinate attention, convolutional block attention module, content-aware reassembly of features, improved bidirectional feature pyramid network and SCYLLA- intersection over union loss function based on YOLOv5s for high accuracy real-time abnormal waste detection. Through video acquisition, frame-splitting, manual annotation and data augmentation, we develop an abnormal waste image dataset with the four most common types (i.e. gas cans, mattresses, wood and iron sheets) to evaluate the proposed Waste-YOLO. Extensive experimental results demonstrate the superiority of Waste-YOLO to several state-of-the-art algorithms in waste detection effectiveness and efficiency to ensure production safety in WtEPPs.
Research on Optimization of Grey Wolf Algorithm with Multiple Improved Strategies

The Grey Wolf Optimizer (GWO) boasts remarkable optimization characteristics and is already widely applied across various domains. This paper provides an overview of the enhancements applied to the GWO, meticulously selecting nine of the most representative strategies for improvement. Subsequently, these strategies were subjected to numerical experiments using benchmark functions. The experimental results showed that: the improved grey wolf optimizer (ODGWO) with novel opposition-learning and differential mutation, exhibited superior capabilities in exploration and convergence; The strategy of amalgamating the Grey Wolf Optimizer with Particle Swarm Optimization (PSO) in the PSOGWO approach, and the integration with the Whale Optimization Algorithm (WOA) in the WOAGWO approach, notably enhanced the algorithm’s search performance. Of particular note is the fusion with the Whale Optimization Algorithm (WOAGWO), which effectively balanced the trade-off between exploration, exploitation, and the ability to evade local optima, particularly in solving fixed-dimension multimodal benchmark functions.
Topic Classification Using the Long Short-Term Memory (LSTM) Method with FastText Feature Expansion on Twitter

Twitter serves as a social network where people can connect by sending short messages, known as tweets. The limited character count often leads to variations in wording, making it challenging to understand tweets without proper classification. In this research, feature expansion is employed to solve these issues. The main goal of this research is to evaluate the impact of FastText feature expansion in the context of classifying Indonesian-language tweets based on their topic classification. This research utilized FastText to decrease vocabulary mismatches in topic classification by calculating the similarity between words in the corpus. As a result, the anticipated outcome of this research is that FastText could increase the performance of topic classification. The stage of feature extraction involves word weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) must be carried out before testing the feature expansion. This research also utilized Long Short-Term Memory (LSTM) as a classification method because it works better using memory cells than recurrent neural networks in general. The best result from this research achieves an accuracy of 95.30% using the LSTM method and feature expansion with FastText based on top similarity 5 by constructing the News corpus. These experiments prove that the LSTM method using FastText could increase the model’s accuracy value of topic classification on Twitter.
Language Guidance Generation Using Aesthetic Attribute Comparison for Human Photography and AIGC

With the proliferation of mobile photography technology, leading mobile phone manufacturers are racing to enhance the shooting capabilities of their equipment and the photo beautification algorithm of their software. However, the development of intelligent equipment and algorithms cannot supplant human subjective photography techniques. Simultaneously, with the rapid advancement of AIGC technology, AI simulation shooting has become an integral part of people's daily lives. If it were possible to assist human photography and AIGC with language guidance, this would be a significant step forward in subjectively improving the aesthetic quality of photographic images. In this paper, we propose Aesthetic Language Guidance of Image (ALG) and present a series of language guidance rules (ALG Rules). ALG is divided into ALG-T and ALG-I based on whether the guiding rules are derived from photography templates or reference images, respectively. ALG-T and ALG-I both provide guidance for photography based on three attributes of color, light, and composition of images. ALG-T and ALG-I provide aesthetic language guidance for two types of input images, landscape and portrait images. We employ two methods to conduct confirmatory experiments, human photography, and AIGC imitation shooting. In the experiments, by comparing the aesthetic scores of original and modified images, the results show that our proposed guidance scheme significantly improves the aesthetic quality of photos in terms of color, composition, and lighting attributes.