Reviewerly/exHarmony huggingface dataset:
Cryptic Fusion: Advancing Text Steganography via Pixel Manipulation &amp; ASCII Integration

Unlocking Secrets in Plain Sight: A Cutting-Edge Text Steganography Approach Exploiting Pixel Manipulation and ASCII Fusion. Steganography, an ancient art of concealed communication, has evolved into a science of imperceptible messaging, hiding data within seemingly innocent messages. In this groundbreaking paper, we delve into a novel technique of concealing text messages within images, wielding the power of pixel manipulation and the binary form of ASCII values. Our innovative methodology empowers us to encode individual characters seamlessly into single pixels, leading to the creation of encrypted stego-images. These masterfully crafted images have undergone a series of transformative and quality measurement techniques, meeting all the essential criteria of steganography. The prime goal of our research is to develop a robust steganography method that ensures unparalleled confidentiality during communication, while simultaneously maximizing payload capacity. By enabling the transmission of vast amounts of confidential data through compact cover pictures, our approach not only accelerates communication but also optimizes bandwidth utilization, revolutionizing the way secrets are safeguarded and exchanged.
An Efficient Distributed Framework for Secured Multimedia Content Using Cloud Storage

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift's open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds' multimedia with low computational costs as shown in the experimental results.
Realizing Fine-Grained Inference of AS Path With a Generative Measurable Process

In the global Internet, the paths between two autonomous systems (ASes), which are used for the exchange of traffic, are essential for understanding the behavior of the Internet routing system and they can help improve the performance of many applications of the Internet. Popular approaches to obtain the AS path between an AS pair (AP) are measurement based (e.g., Traceroute), but considering the size of the modern Internet and the limitations of measurement resources, only paths between a very small portion of APs can be measured. In recent years, a large body of path inference approaches has been proposed to bridge the gap in measurement resources. However, as we show with experiments, they perform poorly in accuracy and coverage. We propose a generative measurable path inference (GMPI) framework for AS-level path measurement, which performs well in accuracy and coverage. GMPI addresses two limitations of previous approaches: 1) Information incompleteness due to unrevealed real-world AS-level routing policies and insufficient measuring resources. 2) Knowledge isolation caused by distributed AS knowledge with different sources and inconsistent forms. To overcome these challenges, the data-driven GMPI framework invents heuristic path generation to address incompleteness and a dual-attention network to integrate the isolated knowledge. GMPI does not perform any measurement or impose any burden on the network. Our performance evaluation shows that our framework GMPI outperforms state-of-the-art approaches in terms of accuracy and coverage. In particular, compared to the state-of-the-art stitching-based baseline, GMPI provides a 42.45% improvement in coverage and a 39.97% improvement in accuracy. The experimental results demonstrate that GMPI can accurately infer paths for nearly arbitrary APs.
Towards Developing a Lightweight Neural Network for Liver CT Segmentation

Image segmentation is crucial during the diagnosis and treatment planning of various liver diseases, especially hepatocellular carcinoma (hcc). We present a new neural network, Res-PAC-UNet, employing Pyramid Atrous Convolutions and a fixed-width residual UNet backbone resulting in low parameter count and of course, good liver CT segmentation. We use medical segmentation decathlon dataset to train the network. The resulting segmentation gives a Dice similarity coefficient of 0.958±0.015 with less than 0.5 million parameters with 1.2 million parameters.
Privacy-preserving fuzzy commitment schemes for secure IoT device authentication

Privacy-Preserving Fuzzy Commitment Schemes (PPFCS) have emerged as a promising solution for secure Internet of Things (IoT) device authentication, addressing the critical need for privacy and security in the rapidly growing IoT ecosystem. This paper presents a novel PPFCS-based authentication mechanism that protects sensitive user data and ensures secure communication between IoT devices. The proposed scheme leverages error-correcting codes (ECC) and cryptographic hash functions to achieve reliable and efficient authentication. The PPFCS framework allows IoT devices to authenticate themselves without revealing their true identity, preventing unauthorized access and preserving users’ privacy. Furthermore, our PPFCS-based authentication mechanism is resilient against various attacks, such as replay, man-in-the-middle, and brute-force attacks, by incorporating secure random nonce generation and timely key updates. We provide extensive experimental results and comparative analysis, demonstrating that the proposed PPFCS significantly outperforms existing authentication schemes in terms of security, privacy, and computational efficiency. As a result, the PPFCS offers a viable and effective solution for ensuring secure and privacy-preserving IoT device authentication, mitigating the risks associated with unauthorized access and potential data breaches in the IoT ecosystem.
Towards Automatic Principles of Persuasion Detection Using Machine Learning Approach

Persuasion is a human activity of influence. In marketing, persuasion can help customers find solutions to their problems, make informed choices, or convince someone to buy a useful (or useless) product or service. In computer crimes, persuasion can trick users into revealing sensitive information, or even performing actions that benefit attackers. Phishing is one of the most common and dangerous forms of persuasion-based attacks, as it exploits human vulnerabilities rather than technical ones. Therefore, an intelligent system capable of detecting and classifying persuasion attempts might be useful in protecting users. In this work, an approach that uses Machine Learning to analyze messages based on principles of persuasion and different data representations is presented. The aim of this research is to detect which data representation and which classification algorithm obtain the best results in detecting each principle of persuasion as a prior step to detecting phishing attacks. The results obtained indicate that among the combinations tested, there is one combination of data representation and classification algorithm that performs best. The related classification models obtained can detect the principles of persuasion at a rate that varies between 0.78 and 0.86 of AUC-ROC.
Quantum maximal correlation for Gaussian states

We compute the quantum maximal correlation for bipartite Gaussian states of continuous-variable systems. Quantum maximal correlation is a measure of correlation with the monotonicity and tensorization properties that can be used to study whether an arbitrary number of copies of a resource state can be locally transformed into a target state without classical communication, known as the local state transformation problem. We show that the required optimization for computing the quantum maximal correlation of Gaussian states can be restricted to local operators that are linear in terms of phase-space quadrature operators. This allows us to derive a closed-form expression for the quantum maximal correlation in terms of the covariance matrix of Gaussian states. Moreover, we define Gaussian maximal correlation based on considering the class of local Hermitian operators that are linear in terms of phase-space quadrature operators associated with local homodyne measurements. This measure satisfies the tensorization property and can be used for the Gaussian version of the local state transformation problem when both resource and target states are Gaussian. We also generalize these measures to the multipartite case. Specifically, we define the quantum maximal correlation ribbon and then characterize it for multipartite Gaussian states.
Quantum error correction with an Ising machine under circuit-level noise

Efficient decoding to estimate error locations from outcomes of syndrome measurements is the prerequisite for quantum error correction. Decoding in the presence of circuit-level noise including measurement errors should be considered in case of actual quantum computing devices. In this paper, we develop a decoder for circuit-level noise that solves the error estimation problems as Ising-type optimization problems. We confirm that the threshold theorem in the surface code under circuit-level noise is reproduced with an error threshold of approximately 0.4%. We also demonstrate the advantage of the decoder through which the $Y$ error detection rate can be improved compared with other matching-based decoders. Our results reveal that a lower logical error rate can be obtained using our algorithm compared with that of the minimum-weight perfect matching algorithm.
Evaluation of XAI Methods in a FinTech Context

As humans, we automate more and more critical areas of our lives while using machine learning algorithms to make autonomous decisions. For example, these algorithms may approve or reject job applications/loans. To ensure the fairness and reliability of the decision-making process, a validation is required. The solution for explaining the decision process of ML models is Explainable Artificial Intelligence (XAI). In this paper, we evaluate four different XAI approaches - LIME, SHAP, CIU, and Integrated Gradients (IG) - based on the similarity of their explanations. We compare their feature importance values (FIV) and rank the approaches from the most trustworthy to the least trustworthy. This ranking can serve as a specific fidelity measure of the explanations provided by the XAI methods.
Online hate in the Philippines: the role of narratives and networks

Online hate is a global problem, affecting diverse social conflicts around the world. Common techniques to understand online hate focus primarily on measuring the volume of hateful messages and identifying individual perpetrators. However, these approaches fail to capture how online hate draws on wider social narratives to attack its targets, and how online hate is spread and organized in networked groups. Our work posits that assessing online hate through the lens of narratives and networks facilitates a deeper understanding of its dynamics in specific contexts and points to more holistic directions for mitigating its potential manipulation. We demonstrate the utility of our framework across two case studies in the Philippines: (a) the 2020 outbreak of the COVID-19 pandemic, and (b) the 2022 presidential elections. Through a computational pipeline of social cyber-security tools, we characterize the online hate narratives used to attack gendered, political, and racial identities in these events, and the network structures of the online hate groups involved. We additionally quantify manipulation of online hate narratives and networks by bots and trolls. These findings offer insights into the design of effective counter-narratives and building more resilient communities against online hate.
Weighted t-Distributed Stochastic Neighbor Embedding for Projection-Based Clustering

This paper presents a projection-based clustering method for visualizing high-dimensional data points in lower-dimensional spaces while preserving the data’s structural properties. The proposed method modifies the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm by adding a weight function that adjusts the dissimilarity between high-dimensional data points to obtain more realistic lower-dimensional representations. In our algorithm, the centroids obtained with a prototype-based clustering algorithm attract high-dimensional data points allocated to their respective clusters, while repelling those points assigned to other clusters. The simulations using real-world datasets show that the Weighted t-SNE produces better projections than similar algorithms without the need for any previous dimensionality reduction step.
Good Negative Sampling for Triple Classification

Knowledge graphs are large and useful sources widely used for natural question answering, Web search and data analytics. They describe facts about a certain domain of interest by representing them using entities interconnected via relations in the way of triples. However, due to the fact that they are created under the Open World Assumption, they are incomplete. Knowledge graph completion includes the triple classification task, for discerning correct from incorrect triples. In this regard, knowledge graph embedding models have been proposed for the knowledge graph completion tasks. However, knowledge graphs include only positive triples and training models with only positive triples over generalize, therefore, these models require negative examples. A random negative sampling generates low-quality negative triples which give rise to the zero loss problem during training. In this work, Good Negative Sampling, which is a negative sampling strategy that aims to improve the negative generation process by using background ontological knowledge is put forward. We prove our strategy on a state-of-the-art embedding model - KG-BERT for the triple classification task - on a benchmark dataset - FB13. As result, we demonstrate that the Good Negative Sampling strategy overcomes other state-of-the-art negative strategies, with significant differences.
Local contexts as alternative knowing spaces for design fields

Lately, the notion of a single, unified form of knowledge has been questioned, and there has been increasing recognition of the importance of context-specific, varied forms of knowledge. This shift is reflected in the field of design studies, where there is a growing movement away from dominant models and strategies and towards contextually based methodologies and a pluralistic knowledge space of design. This paper explores the idea of diverse and equitable ‘ecologies of knowledge’ to understand the interplay between forms of knowledge and the customs and values that either facilitate or hinder their co-creation. Respectively, local contexts and knowledge are proposed as alternative spaces in which pluralistic design knowledge can flourish. By bringing together the latest efforts in design towards plurality with the ecologies of knowledge framework, this paper provides insights into the methods and processes involved in collaboration and co-creation of new knowledge within design research. These guidelines can aid designers and researchers in formulating methods that are contextually relevant and mutually agreed upon when engaging with local communities and in forging more accountable and epistemically imaginative links with local contexts.
Drone Navigation and License Place Detection for Vehicle Location in Indoor Spaces

Millions of vehicles are transported every year, tightly parked in vessels or boats. To reduce the risks of associated safety issues like fires, knowing the location of vehicles is essential, since different vehicles may need different mitigation measures, e.g. electric cars. This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates. We do so via a wall-following algorithm, and a CNN trained to detect license plates. All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map with the position of the plates. Our solution is capable of reading all plates across eight test cases (with several rows of plates, different drone speeds, or low light) by aggregation of measurements across several drone journeys.
A Knowledge-Based User Feedback Classification Approach for Software Support

The analysis of the textual content of user opinions on social networks about software applications in use can provide valuable information to the development and support teams, in terms of errors, dissatisfactions, new functional requirements, among others. The paper presents a solution based on intelligent technologies to automatically classify whether or not the content of a review is relevant to a software support team. This solution combines machine learning algorithms, with the use of a domain-specific glossary for feature selection, in predicting the relevance of reviews. The proposed solution was evaluated experimentally with three datasets, specifically Facebook, Tapfish and SwiftKey, and the results obtained were very promising.
SECONDARY SCHOOL MATURITA READING LISTS AS A MOTIVATION TO SUPPORT READING

Reading motivation is incredibly important for both formal and informal lifelong learning. According to research studies such as PISA (2018) (Programme for International Student Assessment), PIRLS (2021) (Progress in International Reading Literacy Study), educating active readers is a key priority. A study was conducted using the content analysis method of documents "School Maturita reading lists" to determine the actual content of the current school reading lists of artistic texts in the reality of technically oriented secondary schools in the Moravian-Silesian and Olomouc regions of the Czech Republic. In addition, the researchers aimed to discover what kinds of non-cognitive aspects influence reading among students at technical secondary schools using the method of semi-structured interviews with 12 students. One of the most exciting findings of this research was that more than half (75%) of respondents believed that motivational reading recommendations from parents, family spending free time reading, and awareness of the value of reading for life were among the solid non-cognitive factors influencing their interest in reading, the joy of talking about what they have read, experience sharing. External and internal factors can shape pupils' attitudes and motivation to read. The majority (90%) of the respondents considered school Maturita reading lists a formal study obligation.
Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.
Long-Term Behaviour in an Exactly Solvable Model of Pure Decoherence and the Problem of Markovian Embedding

We consider a well-known, exactly solvable model of an open quantum system with pure decoherence. The aim of this paper is twofold. Firstly, decoherence is a property of open quantum systems important for both quantum technologies and the fundamental question of the quantum–classical transition. It is worth studying how the long-term rate of decoherence depends on the spectral density characterising the system–bath interaction in this exactly solvable model. Secondly, we address a more general problem of the Markovian embedding of non-Markovian open system dynamics. It is often assumed that a non-Markovian open quantum system can be embedded into a larger Markovian system. However, we show that such embedding is possible only for Ohmic spectral densities (for the case of a positive bath temperature) and is impossible for both sub- and super-Ohmic spectral densities. On the other hand, for Ohmic spectral densities, an asymptotic large-time Markovianity (in terms of the quantum regression formula) takes place.
How graph features from message passing affect graph classification and regression?

Graph neural networks (GNNs) have been applied to various graph domains. However, GNNs based on the message passing scheme, which iteratively aggregates information from neighboring nodes, have difficulty learning to represent larger subgraph structures because of the nature of the scheme. We investigate the prediction performance of GNNs when the number of message passing iteration increases to capture larger subgraph structures on classification and regression tasks using various real-world graph datasets. Our empirical results show that the averaged features over nodes obtained by the message passing scheme in GNNs are likely to converge to a certain value, which significantly deteriorates the resulting prediction performance. This is in contrast to the state-of-the-art Weisfeiler–Lehman graph kernel, which has been used actively in machine learning for graphs, as it can comparably learn the large subgraph structures and its performance does not usually drop significantly drop from the first couple of rounds of iterations. Moreover, we report that when we apply node features obtained via GNNs to SVMs, the performance of the Weisfeiler-Lehman kernel can be superior to that of the graph convolutional model, which is a typically employed approach in GNNs.