Reviewerly/exHarmony huggingface dataset:
Taxonomy of SQL Injection: ML Trends &amp; Open Challenges

SQL injections are a significant and ever-present threat to web applications and database security. During these attacks, malicious SQL statements are injected into input fields of data-driven systems, leading to unauthorized access and data breaches. Consequently, a need is generated to understand the nature of the attacks, detection, and effective prevention techniques. This research paper focuses on providing a taxonomy and comprehensive survey of SQL injection attacks, detection, and prevention, including their various types and techniques. Additionally, it explores the current state-of-the-art and evaluation for attacks, detection, and prevention techniques. This research paper also discusses and provides a taxonomy of current machine learning (ML) trends (Taxonomy) and their open challenges for detection purposes. Finally, this paper ends with a discussion aiming to equip system administrators, researchers, scientists and practitioners with the knowledge and strategies to mitigate the risks associated with SQL injection attacks effectively. Eventually, this will help to enhance the security and resilience of web applications and databases in the face of this significant threat.
Deep Learning Analysis of Histopathology Images for Breast Cancer Detection: A Comparative Study of ResNet and VGG Architectures

Medical image analysis has undergone significant advancements with the emergence of deep learning techniques, offering great promise in improving diagnostic precision and expediting patient care. This research investigates the effectiveness of ResNet and VGG architectures in detecting breast cancer through the analysis of histopathology images. By meticulously fine-tuning hyperparameters and optimizers, we establish robust and accurate deep learning models. Our findings reveal that the ResNet model with the SGD optimizer excels, surpassing the performance of VGG in terms of accuracy and F1-score. However, employing transfer learning with pre-trained VGG16 and ResNet50 networks does not yield competitive results, potentially due to disparities in input image size and data distribution. The primary focus of this study is to address the critical challenge of early breast cancer detection, ultimately leading to enhanced patient outcomes. By exploring state-of-the-art deep learning architectures and methodologies, we contribute to the growing body of research aimed at leveraging artificial intelligence for medical diagnosis.
A Dataset of Hotel Reviews for Aspect-Based Sentiment Analysis and Topic Modeling

While topic modelling is a contemporary method for analysing large sets of texts and documents, including short texts like tweets and reviews, very few special datasets have been released to benchmark the performance of topic modelling algorithms. These algorithms are usually evaluated based on human assessment and topic coherence. In this paper we present the HRAST (Hotel Reviews: Aspects, Sentiments and Topics) dataset, consisting of 23114 sentences extracted from hotel reviews. These sentences were manually classified into 21 different (but overlapping) topics (hotel aspects) while their sentiment (positive, neutral, negative) was also manually assessed. A subset of 5812 sentences express aspects in a contradicting manner, i.e., different aspects assessed via contradicting sentiments, and can be used for benchmarking aspect-based sentiment analysis methods. Some initial experiments for both sentiment analysis and topic modelling, to explain the use of the dataset, have been conducted using state-of-the-art algorithms.
A User-Driven Platform for Biodiversity Conservation and Promotion

The biodiversity-oriented platform, with a focus on promoting biodiversity and ecological importance, uses cutting-edge technologies such as augmented reality (AR) and web semantics to provide immersive and educational experiences for both on-site visitors and remote users accessing the content via the website and its mobile app. By integrating multimedia content and utilizing open source technology, the platform enables users to explore diverse ecosystems, interact with endangered species and engage with the ecology of Greece, promoting active participation and content contribution. Stakeholders benefit from the collection of meaningful metrics and feedback, which facilitates effective conservation efforts and raises awareness of the paramount importance of biodiversity and environmental conservation.
ShipQuest: A Bottom-Up Approach for Building a Modern Greek Maritime Ontology

The ShipQuest Ontology is a pioneering legal maritime ontology designed for modeling and integrating legal information in the Greek language. This standardized representation of legal concepts within the maritime domain offers a structured approach to managing legal data. By formalizing legal information in Greek, the ShipQuest Ontology ensures semantic interoperability and seamless integration of diverse legal data sources. It encompasses essential classes, subclasses, properties, and relationships pertaining to legal aspects of maritime activities, including legal entities, contracts, regulations, liabilities, and more. The ShipQuest Ontology plays a critical role in stream-lining legal operations within the maritime industry, offering a unified and comprehensive knowledge base for legal practitioners, researchers, and decision-makers. Its adoption fosters collaboration and knowledge exchange within the maritime community, promoting standardized legal terminology and practices. This paper delves into the construction and utilization of the ShipQuest Ontology, detailing its architecture, development process, and implementation. Furthermore, we showcase its practical benefits by illustrating its application in real-world scenarios. By addressing the challenges of legal data heterogeneity and facilitating semantic consistency, the ShipQuest Ontology sets a precedent for enhanced efficiency and effectiveness in the legal domain of maritime activities.
Securing blockchain-based timed data release against adversarial attacks1

Timed data release refers to protecting sensitive data that can be accessed only after a pre-determined amount of time has passed. While blockchain-based solutions for timed data release provide a promising approach for decentralizing the process, designing an attack-resilient timed-release service that is resilient to malicious adversaries in a blockchain network is inherently challenging. A timed-release service on a blockchain network is inevitably exposed to the risk of post-facto attacks where adversaries may launch attacks after the data is released in the blockchain network. Existing incentive-based solutions for timed data release in Ethereum blockchains guarantee protection under the assumption of a fully rational adversarial environment in which every peer acts rationally. However, these schemes fail invariably when even a single participating peer node in the protocol starts acting maliciously and deviates from the rational behavior. In this paper, we propose a systematic solution for attack-resilient and practical blockchain-based timed data release in a mixed adversarial environment, where both malicious adversaries and rational adversaries exist. We first propose an effective uncertainty-aware reputation measure to capture the behaviors of the peer involved in timed data release activities in the network. In light of such a measure, we present the design of a basic protocol that consists of two critical ingredients, namely reputation-aware peer recruitment and verifiable enforcement protocols. The former, prior to the start of the enforcement protocols, performs peer recruitment based on the reputation measure to make the design probabilistically attack-resilient to the post-facto attacks. The latter is responsible for contractually guarding the recruited peers at runtime by transparently reporting observed adversarial behaviors. However, the basic recruitment design is only aware of the reputation of the peers and it does not consider the working time schedule of the participating peers and as a result, it results in lower attack-resilience. To enhance the attack resilience further without impacting the verifiable enforcement protocols, we propose a temporal graph-based reputation-aware peer recruitment algorithm that carefully determines the peer recruitment plan to make the service more attack-resilient. In our proposed approach, we formally capture the timed data release service as a temporal graph and we develop a novel maximal attack-resilient path-finding algorithm on the temporal graph for the participating peers. We implement a prototype of the proposed approach using Smart Contracts and deploy it on the Ethereum official test network, Rinkeby. For extensively evaluating the proposed techniques, we perform simulation experiments to validate the effectiveness of the reputation-aware timed data release protocols as well as our proposed temporal-graph-based improvements. The results demonstrate the effectiveness and strong attack resilience of the proposed mechanisms and our approach incurs only a modest gas cost.
A new intelligent modelling two-stage of hybrid fuzzy prediction approach by using computation software

With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model’s predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer.
PaperMill Detection in Scientific Content

Researchers often come under pressure when facing the ever-increasing demand to produce a progressive number of publications, resorting to hiring the services of paper mills. These are unofficial, and often illegitimate, organizations providing ready-made questionable research components and services, posing a threat to the research integrity, scientific ecosystem, and publishers. Identifying paper mill material is a challenging and laborious process, while the increasing number of Artificial Intelligence services generating human-like text obstructs this process. The purpose of this paper is to contribute to the research integrity domain by proposing the PaperMill Detection manuscript screening framework. By leveraging contextual signals, it measures the probability of a document being the result of a paper mill organization or generated by Artificial Intelligence. The combination of these signals can facilitate the detection of questionable scientific content. Our evaluation has revealed that the proposed approach outperforms other open-source and commercial solutions in all examined evaluation metrics, achieving an F1 score of 0.97.
Integrating Stereotype User Models for Adaptive Scenarios in Game Playing within Immersive Virtual Environments

Stereotypes constitute a widely used technique for creating user models. This paper explores the potential of stereotype-based models in virtual environments in order to enhance user engagement and learning outcomes. The focus is on an application, named Beekeeper World, which employs adaptive scenarios that respond to user actions. These scenarios, referred to as stereotype user models, represent predefined behavior patterns that Beekeeper World uses to respond to specific user actions. In the game context, these stereotypes are triggered by particular player interactions involving the ecological dynamics of bees and spiders. The first one challenges the player when s/he fails to protect a bee by strengthening the enemy spider. The second stereotype is a way of helping the player by pausing the spiders' spawning when s/he cannot defend the bees. These stereotypes can be instrumental in creating predictable, consistent behavior in Beekeeper World. They provide a framework for the system to respond to various user inputs in a consistent manner. This consistency can be crucial in maintaining the desired user engagement, as it ensures that the system's responses align with the user's expectations based on their previous interactions. Furthermore, these stereotypes can contribute to the complexity and challenge of the game, as they require the player to understand and adapt to these patterns in order to succeed, thereby promoting strategic thinking and problem-solving skills.
Flexible Variational Bayes Based on a Copula of a Mixture

Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimisation. Variational approximation is useful when exact inference is intractable or very costly. Our article develops a flexible variational approximation based on a copula of a mixture, which is implemented by combining boosting, natural gradient, and a variance reduction method. The efficacy of the approach is illustrated by using simulated and real datasets to approximate multimodal, skewed and heavy-tailed posterior distributions, including an application to Bayesian deep feedforward neural network regression models. Supplementary materials, including appendices and computer code for this article, are available online.
Watermarking Audio Signals: Analysis of Noise Effect and Error Characteristics

The possibility of embedding watermarks robust against compression in musical audio files has been investigated in the paper. The process of embedding and retrieving a watermark can be regarded as a binary communication channel. In this paper, we propose a method for embedding watermarks based on a combination of key-dependable dither modulation and Haar wavelet transform. We investigate its statistics to give recommendation how to choose proper embedding parameters such as quantizer step, the level of the Haar reconstruction and in which sub-bands to embed as well as what error correcting codes to be used. The described study covers the case of 128 Kbps AAC compression. We aim after a comprehensive analysis of the capacity and robustness of the embedding technique to propose a method of choosing the parameters so that to achieve acceptable probability of errors (according to characteristics of the watermark) without perceptible loss of the quality of the audio file. The described work is a continuation and an extension of our previous investigations in [3].
A classification method based on a cloud of spheres

In this article we propose a binary classification model to distinguish a specific class that corresponds to a characteristic that we intend to identify (fraud, spam, disease). The classification model is based on a cloud of spheres that circumscribes the points of the class to be identified. It is intended to build a model based on a cloud and not on a disjoint set of clouds, establishing this condition on the connectivity of a graph induced by the spheres. To solve the problem, designed by a Cloud of Connected Spheres, a quadratic model with continuous and binary variables (MINLP) is proposed with the minimization of the number of spheres. The issue of connectivity implies in many models the imposition of an exponential number of constraints. However, because of the specific conditions of the problem under study, connectivity is enforced with linear constraints that scale quadratically with K, which serves as an upper bound on the number of spheres. This classification model is effective when the structure of the class to be identified is highly non-linear and non-convex, also adapting to the case of linear separation. Unlike neural networks, the classification model is transparent, with the structure perfectly identified. No kernel functions are used and it is not necessary to use meta-parameters unless it is intended also to maximize the separation margin as it is done in SVM. Finding the global optima for large instances is quite challenging, and to address this, a heuristic is proposed. The heuristic demonstrates nice results on a set of frequently tested real problems when compared to state-of-the-art algorithms.
Towards human-compatible XAI: Explaining data differentials with concept induction over background knowledge

Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer. Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge. To make the explanations easily understandable for non-specialists, the complex description logic explanations generated by our concept induction system (ECII) were presented as a word list consisting of the concept names occurring in the highest rated system responses.
Static and dynamic orderings on Dungean argumentation frameworks - An overview

The article is devoted to the issue of comparing argumentation frameworks (AFs) which has received increasing attention in recent years. Knowing whether a given framework F possesses less information than an AF G or whether the latter is at least reachable from F in a certain future scenario is an important issue for several applications. The question what we precisely mean by information will depend on the particular context in which they are being used. Our analysis does not express any preferences here. However, if AFs are applied to fields like criminal justice, finance, and healthcare, where it is necessary to exercise caution, skeptical accepted arguments may be the most appropriate form of reasoning. The presented overview considers classical Dung semantics including the stable, admissible, complete, grounded and preferred one. The article is divided into two main axes: the distinction between extension-based and kernel-based orderings and the analysis of their static and dynamic versions. The results show that there are various relationships between these concepts, indicating that some orderings are stronger or weaker than others. The considered orderings are so-called preorders, i.e. they satisfy reflexivity and transitivity. We analysed them w.r.t. bounds, meets and joins and also considered the induced equivalences. The latter often coincide with well-known equivalences, namely ordinary and strong equivalence.
RelNet-MAM: Relation Network with Multilevel Attention Mechanism for Image Captioning

Text present in an image contains rich semantic information which is crucial for the understanding of an image. For example, a signboard having the text “deep water” conveys the danger involved in the image. The current image captioning models do not effectively utilize this useful semantic information due to their limited representation capabilities of scene-text tokens. Our work presents a novel image captioning model called RelNet-MAM, which utilizes a multilevel attention mechanism and relation network. To improve the appearance feature representation, RelNet-MAM uses multilevel attention which consists of spatial attention, channel-wise attention, and semantic attention. For representing the scene-text token effectively, RelNet-MAM uses appearance, FastText, location, and PHOC features for each token. Further, the proposed RelNet-MAM uses the relation network to establish the relationships between the objects and scene-text tokens. Finally, the transformer model together with dynamic pointer networks is used as a decoder in the caption generation process. The proposed RelNet-MAM model outperforms the state-of-the-art models on TextCaps, Flickr30k, and MS COCO datasets. TextCaps requires models to read and reason about the texts in an image for caption generation. MSCOCO and Flickr30k contain diverse images; persons, animals, automobiles, indoor and outdoor scenes. Remarkably, the proposed RelNet-MAM model outperforms the current best model by 2.3% on B-4, 1.8% on METEOR, 2.2% on ROUGE-L, 2.0% on CIDEr-D and 3.0% on SPICE metric scores on TextCaps dataset.
A Pruning Method Based on Feature Map Similarity Score

As the number of layers of deep learning models increases, the number of parameters and computation increases, making it difficult to deploy on edge devices. Pruning has the potential to significantly reduce the number of parameters and computations in a deep learning model. Existing pruning methods frequently require a specific distribution of network parameters to achieve good results when measuring filter importance. As a result, a feature map similarity score-based pruning method is proposed. We calculate the similarity score of each feature map to measure the importance of the filter and guide filter pruning using the similarity between the filter output feature maps to measure the redundancy of the corresponding filter. Pruning experiments on ResNet-56 and ResNet-110 networks on Cifar-10 datasets can compress the model by more than 70% while maintaining a higher compression ratio and accuracy than traditional methods.
Enhancing strength and range of atom-atom interaction in a coupled-cavity array via parametric drives

Coherent long-range interactions between atoms are a prerequisite for numerous applications in the field of quantum information science. Here we present an appealing method to dramatically enhance the long-range atom-atom interaction mediated by a coupled-cavity array that is subjected to two-photon (parametric) drives. Our method allows one to greatly amplify both the localization length of the single-photon bound-state wave function and the effective atom-photon coupling strength, resulting in a significant improvement of photon-mediated coherent interaction between two distant atoms. Additionally, we illustrate this effect by analyzing how it facilitates the transfer of information and the creation of entanglement between the atoms.
Índice de dificultad y aprendizaje activo en Educación 4.0 aplicada a formación universitaria

The development of this research was aimed at knowing, using a methodology based on Education 4.0, if that model allows an increase in the percentage of participation of students with an adequate difficulty index in the evaluations. For this, a quantitative - qualitative mixed method was used with a case study implemented in 138 students form the University of Aconcagua (Chile) from September 2022 to July 2023. The instruments to collect the information were checklists of grades and participations (pretest and postest), as well as a complementary survey for the students. The results indicate that the percentage of activities completed increased its mean from 45.1% to 71.3% (p&lt;0.001) with a Cohen’s indicator of 0.89, highlighting a much larger effect size than typical; likewise, the difficulty index in both cases remained in the range of “adequate difficulty” varying slightly form 0.639 to 0.621 with a perception of the students that finally integrates the previous results. It is concluded that the effects of the application of Education 4.0 are positive by promoting active learning and the proper implementation of challenges for the achievement of 21st century skills.