Reviewerly/exHarmony huggingface dataset:
Are TikTok Algorithms Influencing Users’ Self-Perceived Identities and Personal Values? A Mini Review

The use of TikTok is more widespread now than ever, and it has a big impact on users’ daily lives, with self-perceived identity and personal values being topics of interest in light of the algorithmically curated content. This mini-review summarizes current findings related to the TikTok algorithm, and the impact it has on self-perceived identity, personal values, or related concepts of the Self. We pass through the contents of algorithmic literacy and emphasize its importance along with users’ attitudes toward algorithms. In the first part of our results, we show conceptual models of algorithms like the crystal framework, platform spirit, and collective imaginaries. In the second part, we talk about the degree of impact a social media algorithm may exert over an individual’s sense of self, understanding how the algorithmized self and domesticated algorithm are trying to sum up the dual development of this relationship. In the end, with the concept of Personal Engagement and the role of cognitive biases, we summarize the current findings and discuss the questions that still need to be addressed. Performing research on the topic of social media, especially TikTok, poses ethical, cultural, and regulatory challenges for researchers. That is why we will discuss the main theoretical frameworks that were published with their attached current studies and their impact on the current theoretical models as well as the limitations within these studies. Finally, we discuss further topics of interest related to the subject and possible perspectives, as well as recommendations regarding future research in areas like impact on personal values and identity, cognitive biases, and algorithmic literacy.
“Pure Rat Country” – Reflections on Case Decision 2022-001-FB-UA of Facebook Oversight Board (Knin Cartoon Case)

Objective : the present paper aims to analyse Case Decision 2022-001-FB-UA of Facebook Oversight Board, also known as the Knin cartoon case and attempts to put the case as well as its procedure in a historical and cultural context to set out a critical approach concerning Facebook’s content moderation. Methods : the paper uses desk research as the primary source of method. The paper’s resource background builds upon comparative case studies and case analysis as well. The paper uses resources from various disciplines: legal philosophy, international law, media law, platform regulation, history. Results : the paper presents the context of the Knin cartoon case as well as the key findings of the Oversight Board and the reasoning behind its decision. Furthermore, this paper aims to reflect on the idea of hate speech as interpreted by the Oversight Board and makes a tentative to contextualise and introduce the main problems and possible solutions regarding Meta’s content moderation in the scope of the present case. Scientific novelty : the Knin case has not been analysed in such historical and contextual depth before as the case decision was issued in 2022. Only a few analyses from merely legal standpoints were published thus far. Practical significance : the findings regarding the Knin case may be of importance of three main aspects: (1) it could be used for further critical analyses on Facebook’s content moderation, (2) it could serve as a recommendation regarding platform regulation and guideline development and (3) it presents the paramount relevance and significance of the holistic interpretational perspectives when determining hate speech. As for the latter the present paper argues that the historical, cultural, societal and symbolic interpretation and understanding of hate speech determination is not only instrumental, but the only viable method to understand, determine and judge upon alleged hate speech cases.
Health Data Sharing towards Knowledge Creation

Data sharing and service reuse in the health sector pose significant privacy and security challenges. The European Commission recognizes health data as a unique and cost-effective resource for research, while the OECD emphasizes the need for privacy-protecting data governance systems. In this paper, we propose a novel approach to health data access in a hospital environment, leveraging homomorphic encryption to ensure privacy and secure sharing of medical data among healthcare entities. Our framework establishes a secure environment that enforces GDPR adoption. We present an Information Sharing Infrastructure (ISI) framework that seamlessly integrates artificial intelligence (AI) capabilities for data analysis. Through our implementation, we demonstrate the ease of applying AI algorithms to treated health data within the ISI environment. Evaluating machine learning models, we achieve high accuracies of 96.88% with logistic regression and 97.62% with random forest. To address privacy concerns, our framework incorporates Data Sharing Agreements (DSAs). Data producers and consumers (prosumers) have the flexibility to express their prefearences for sharing and analytics operations. Data-centric policy enforcement mechanisms ensure compliance and privacy preservation. In summary, our comprehensive framework combines homomorphic encryption, secure data sharing, and AI-driven analytics. By fostering collaboration and knowledge creation in a secure environment, our approach contributes to the advancement of medical research and improves healthcare outcomes. A real case application was implemented between Portuguese hospitals and universities for this data sharing.
Machine Learning-Based Text Classification Comparison: Turkish Language Context

The growth in textual data associated with the increased usage of online services and the simplicity of having access to these data has resulted in a rise in the number of text classification research papers. Text classification has a significant influence on several domains such as news categorization, the detection of spam content, and sentiment analysis. The classification of Turkish text is the focus of this work since only a few studies have been conducted in this context. We utilize data obtained from customers’ inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries specified in the institution’s internal procedures. The Support Vector Machine, Naïve Bayes, Long Term-Short Memory, Random Forest, and Logistic Regression algorithms were used to classify the data. The performance of the various techniques was then analyzed after and before data preparation, and the results were compared. The Long Term-Short Memory technique demonstrated superior effectiveness in terms of accuracy, achieving an 84% accuracy rate, surpassing the best accuracy record of traditional techniques, which was 78% accuracy for the Support Vector Machine technique. The techniques performed better once the number of categories in the dataset was reduced. Moreover, the findings show that data preparation and coherence between the classes’ number and the number of training sets are significant variables influencing the techniques’ performance. The findings of this study and the text classification technique utilized may be applied to data in dialects other than Turkish.
Information security objectives and the output legitimacy of ISO/IEC 27001: stakeholders’ perspective on expectations in private organizations in Sweden

Abstract Organizations use the ISO/IEC 27001 standard to establish an information security management system (ISMS). This standard outlines specific security measures and requirements that organizations can implement to effectively manage their information assets. However, the effectiveness of the standard’s problem-solving capabilities has raised some questions. Consequently, there is a continuous development of new governance methods that demand fresh approaches to validate security operations and measures. In light of this, research is being conducted to examine the application and impact of ISO/IEC 27001, as well as to analyze the challenges and knowledge gaps through theoretical perspectives. By employing stakeholder theory, the focus shifts towards integrating business and social issues and exploring how non-business pressures can influence stakeholder motivations in implementing standards. Additionally, it investigates the impact of these standards on an organization’s reputation, performance, and operations. Therefore, the objective of this study is to investigate the output legitimacy of ISO/IEC 27001 from the perspective of stakeholder expectations. To accomplish this, an interview-based study was conducted, involving relevant stakeholders engaged in information security management within private organizations in Sweden. The findings reveal eight key information security objectives. The results indicate that the level of output legitimacy of the standard varies across these objectives, ranging from high to medium to low. To achieve a high level of output legitimacy for ISO/IEC 27001, stakeholders must understand that the standard is not solely a technical document. Furthermore, stakeholders need to possess the appropriate knowledge and skills in information security to effectively navigate their work while leveraging the support provided by the standard.
A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.
A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method.
Neural Networks in the Book Publishing Industry

The article deals with artificial intelligence (AI) technologies in the publishing industry. Some modern generative neural network (NN) models are able to create different types of content: from text generation (GPT-3, Balaboba) to its visualization (Dall-E 2, Midjourney, Stable Diffusion, Russian DALL-E, Dream, etc.). Both the early cases of NN applications in book publishing and the latest ones due to the release in 2022 of new AI models that provoked a discussion about the creative revolution are discussed. Special attention is paid to the problems of authorship and copyright protection.
Scalable incomplete multi-view clustering with adaptive data completion

Existing multi-view clustering algorithms are typically built on the prior assumption that data collected from different sources are complete without missing, while this is not always satisfied in real-world applications. Incomplete multi-view clustering (IMC) aims at discovering the latent cluster structure and partitioning the incomplete multi-view data into different groups, which is more practical yet challenging. Moreover, the main bottleneck of the current IMC research is how we could economically cluster large-scale incomplete multi-view data with limited resources. In this paper, we propose a novel Scalable Incomplete Multi-view Clustering with Adaptive Data Completion (SIMC_ADC) method, which seeks to deal with large-scale IMC with promising instance-level restoration. Specifically, to cater to large-scale IMC, our SIMC_ADC unifies representative anchor learning and similarity recovery into a one-stop learning scheme within linear computational and memory costs. Moreover, we formulate an adaptive instance completion scheme to discover and generate trustworthy underlying connections across instances, which iteratively infers and pads the missing instances along with the complete similarity learning. Importantly, theoretical analysis on data completion error further guarantees the reliability of such data completion paradigm. Extensive experiments validate the efficiency and effectiveness of our method when handling large-scale IMC problems in comparison with state-of-the-art algorithms. The source code for our SIMC_ADC is available at https://github.com/DarrenZZhang/SIMC_ADC.
FS-MGKC: Feature selection based on structural manifold learning with multi-granularity knowledge coordination

Feature selection uses the hierarchical dependency information provided by multi-granularity knowledge to identify the most relevant features for a given task. Most of these methods manually assign parameters to adjust the restrictive relationships between these dependencies including strong and weak links. However, manual parameter tuning is challenging to accurately determine the influence degree of each parameter to harm performance, because the parameter tuning process involves the interdependence between the multiple hierarchical structures. Along these lines, we propose feature selection based on structural manifold learning with multi-granularity knowledge coordination to balance local structure relationships automatically. First, an affinity matrix based on multi-granularity knowledge is defined as a manifold coordination analysis, which learns all dependencies without destroying the original class space. This analysis effectively trade-offs these dependencies to adjust inter-class alienation and intra-classes inseparable. Afterward, feature correlation and sparsity are leveraged to select features with the most distinguishable and informative features as differentiation analysis. These two analyses jointly selected the feature subsets that are ask-related and informative in the original knowledge structure. Additionally, the proposed method has good universality, which can handle hierarchical classification tasks of the different structure types. Different hierarchical classification results demonstrate the effectiveness of the developed method.
A Dual-View Style Mixing Network for unsupervised cross-domain fault diagnosis with imbalanced data

The remarkable progress of cross-domain fault diagnosis is based on the balanced distribution of different health conditions in a supervised manner. However, in engineering scenarios, the monitored fault data is scarce and imbalanced; variable working conditions and high labor costs make it luxurious to obtain labels; there is a huge gap between the current domain adaptation methods based on class balance data and real industrial applications. Therefore, a Dual-View Style Mixing Network (DVSMN) for dealing with unsupervised cross-domain fault diagnosis with imbalanced data is proposed. Two parallel graph convolution frameworks are first constructed to extract the fault features. Then, the style mixing module together with the domain style loss is proposed for obtaining generalized and domain-invariant representations without augmenting any synthetic samples. An intermediate domain can also be initialized to increase the original cross-domain overlap to facilitate the domain adaptation. Finally, a dual-view module that consists of a binary classifier and a multi-class classifier is constructed to realize sample-level dynamic re-weighting and accurate fault classification of imbalanced data. As such, the DVSMN can learn the generalized and domain-invariant features from the imbalanced data without any generative modules for sample re-balancing as well as target labels. Cross-domain experiments with different imbalance ratios are carried out via two datasets to validate the performance of the proposed method. Comparative studies with state-of-the-art methods and ablation experiments have demonstrated the effectiveness and superiority of the proposed method. The code of DVSMN is available at https://github.com/CQU-ZixuChen/DVSMN.
Spatio-temporal interpretable neural network for solar irradiation prediction using transformer

Deep learning models have been increasingly applied in the field of solar radiation prediction. However, the characteristics of a deep learning black box model restrict its application in practical scenarios such as model predictive control. Because energy system controllers may be unable to make final decisions based solely on the predictions of a black-box model. This study considers both the temporal and spatial dependencies of solar radiation predictions through unfolding sequences and applying a transformer model As the results indicate, the transformer model used can improve the mean absolute percent error by approximately 20.9% and the mean squared error by 14.3% compared to the baseline recurrent neural network model. At the same time, detailed case studies show that the transformer model heavily considers humidity and temperature when predicting the more significant outcomes Finally, the detailed results of a one-step analysis prove that the change in weight of the transformer model is related to the change in outdoor weather conditions.
Fluxformer: Flow-Guided Duplex Attention Transformer via Spatio-Temporal Clustering for Action Recognition

Vision transformers have demonstrated impressive performance in various robotics and automation applications, such as classification automation and action recognition. However, the drawback of transformers is their quadratic increase in computing resources with larger inputs and dependence on considerable data for training. Most action recognition models using the transformer structure rely on a few frames from the original video to reduce computation, so temporal information is compromised by low frame rates. Spatial information is also compromised by reducing the number of embeddings as the transformer layer iterates. The letter proposes a robust model for action recognition that overcomes the limitations of most action recognition models with the transformer structure using the duplex attention function, flow-guided information, RGB information, and spatial support tokens. The proposed duplex attention mechanism leverages optical flow and RGB to address the lack of temporal information. The method employs spatial interest clustering to convert input data into tokens, improving the preservation of spatial information. Finally, meaningful action event frames are extracted by analyzing the flow and clustering to distinguish scenes. The experimental results reveal that the proposed model outperforms state-of-the-art methods in action recognition accuracy.
Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

Abstract We explore the integration of domain knowledge graphs into Deep Learning for improved interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a protein-protein interaction (PPI) network is masked over a deep neural network for classification, with patient-specific multi-modal genomic features enriched into the PPI graph’s nodes. Subnetworks that are relevant to the classification (referred to as “disease subnetworks”) are detected using explainable AI. Federated learning is enabled by dividing the knowledge graph into relevant subnetworks, constructing an ensemble classifier, and allowing domain experts to analyze and manipulate detected subnetworks using a developed user interface. Furthermore, the human-in-the-loop principle can be applied with the incorporation of experts, interacting through a sophisticated User Interface (UI) driven by Explainable Artificial Intelligence (xAI) methods, changing the datasets to create counterfactual explanations. The adapted datasets could influence the local model’s characteristics and thereby create a federated version that distils their diverse knowledge in a centralized scenario. This work demonstrates the feasibility of the presented strategies, which were originally envisaged in 2021 and most of it has now been materialized into actionable items. In this paper, we report on some lessons learned during this project.
The Tower of Babel in Explainable Artificial Intelligence (XAI)

Abstract As machine learning (ML) has emerged as the predominant technological paradigm for artificial intelligence (AI), complex black box models such as GPT-4 have gained widespread adoption. Concurrently, explainable AI (XAI) has risen in significance as a counterbalancing force. But the rapid expansion of this research domain has led to a proliferation of terminology and an array of diverse definitions, making it increasingly challenging to maintain coherence. This confusion of languages also stems from the plethora of different perspectives on XAI, e.g. ethics, law, standardization and computer science. This situation threatens to create a “tower of Babel” effect, whereby a multitude of languages impedes the establishment of a common (scientific) ground. In response, this paper first maps different vocabularies, used in ethics, law and standardization. It shows that despite a quest for standardized, uniform XAI definitions, there is still a confusion of languages. Drawing lessons from these viewpoints, it subsequently proposes a methodology for identifying a unified lexicon from a scientific standpoint. This could aid the scientific community in presenting a more unified front to better influence ongoing definition efforts in law and standardization, often without enough scientific representation, which will shape the nature of AI and XAI in the future.