Reviewerly/exHarmony huggingface dataset:
Diffusion Transport Alignment

The integration of multimodal data presents a challenge in cases where the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domains of the entire dataset, which may be unrealistic. Furthermore, existing manifold alignment methods are not suited for cases where the data contains domain-specific regions, i.e., there is not a counterpart for a certain portion of the data in the other domain. We propose Diffusion Transport Alignment (DTA), a semi-supervised manifold alignment method that exploits prior knowledge of between only a few points to align the domains. After building a diffusion process, DTA finds a transportation plan between data measured from two heterogeneous domains with different feature spaces, which by assumption, share a similar geometrical structure coming from the same underlying data generating process. DTA can also compute a partial alignment in a data-driven fashion, resulting in accurate alignments when some data are measured in only one domain. We empirically demonstrate that DTA outperforms other methods in aligning multiview data in this semi-supervised setting. We also show that the alignment obtained by DTA can improve the performance of machine learning tasks, such as domain adaptation, inter-domain feature mapping, and exploratory data analysis, while outperforming competing methods.
Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings

Knowledge graphs (KGs) are key tools in many AI-related tasks such as reasoning or question answering. This has, in turn, propelled research in link prediction in KGs, the task of predicting missing relationships from the available knowledge. Solutions based on KG embeddings have shown promising results in this matter. On the downside, these approaches are usually unable to explain their predictions. While some works have proposed to compute post-hoc rule explanations for embedding-based link predictors, these efforts have mostly resorted to rules with unbounded atoms, e.g., $$\textit{bornIn}(x,y) \Rightarrow \textit{residence}(x,y)$$ , learned on a global scope, i.e., the entire KG. None of these works has considered the impact of rules with bounded atoms such as $$\textit{nationality}(x,\textit{England}) \Rightarrow \textit{speaks}(x, \textit{English})$$ , or the impact of learning from regions of the KG, i.e., local scopes. We therefore study the effects of these factors on the quality of rule-based explanations for embedding-based link predictors. Our results suggest that more specific rules and local scopes can improve the accuracy of the explanations. Moreover, these rules can provide further insights about the inner-workings of KG embeddings for link prediction.
Web Service Credibility Evaluation Methods in Different Application Domains

Websites are growing in number and size at an explosive rate. This has created an enormous field of service competitors, which can cause a real challenge for information seekers and inexperienced users. As a result, these users require public information to help their decision about service selection. This aid comes in the form of clear and accessible credibility measures. Numerous research articles have studied the concept of Web service credibility evaluation to support user choices. This paper studies the existing methods and frameworks for evaluating web service credibility. We contrast these methods concerning their applications, underlying approach, evaluation factors, and primary research outcomes. Our study also provides a classification of these methods based on the underlying approach, application domain, purpose of the measure, and measurement automation. This study will set the stage for future research on automating web credibility measurements and dissemination methodology.
Semantic-Preserving Adversarial Text Attacks

Deep learning models are known immensely brittle to adversarial text examples. Existing text adversarial attack strategies can be roughly divided into character-level, word-level, and sentence-level attacks. Despite the success brought by recent text attack methods, how to induce misclassification with minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency is still a challenge. In this paper, we devise a Bigram and Unigram-based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constrain the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making minimal word modifications compared with competitive methods.
Comparative Studies with Random Datasets Using Enhanced Faster R-CNN, Mask R-CNN, and Single Shot Detector

In recent times, object detectors are classified into two main categories. One in which the architecture separates into two main branches one to determine the object localization and another one to make object classification. Another type which architecture, which predicts the bounding boxes and class scores in a single process. The main goal of this paper is implementation and analyze the overall performance of Faster R-CNN, Mask R- CNN, and Single Shot Detector algorithms by its enhanced version in object detection on eight random and customized datasets. The output parameter values of each algorithm are tabulated and measurements are taken.
Tuning of Interval Type-2 Fuzzy Precompensated PID Controller: GWO-ABC Algorithm Based Constrained Optimization Approach

With advances in technology, the control system becomes more complex and enhanced controllers are required for them. Fuzzy based controllers are always preferred for intelligent control of such systems. Fuzzy logic system is developed and advance versions are proposed. The interval type-2 fuzzy logic controller (IT2-FLC) has gained wide recognition for controlling systems with nonlinearities and uncertainties. This chapter presents systematic strategy to get maximum benefit from the shapes of the antecedent MF parameters. For experimental studies the interval type-2 fuzzy precompensated PID (IT2FP-PID) controller is designed for robotic arm and optimized for trajectory tracking problem. Various constraints are considered during optimization procedure. 60 parameters are tuned in a high dimensional problem using recent enhanced algorithm. The robustness of the controller in the presence of external disturbances, measurement noise and parameter variations is investigated. The results are compared with other equivalent counterparts. Minimization of performance metric integral time absolute error (ITAE) is selected as a objective function and hybrid grey wolf optimizer and artificial bee colony algorithm (GWO-ABC) is used.
An Abstraction-Based Approach for Privacy-Aware Federated Process Mining

Process awareness is an essential success factor in any type of business.Process mining uses event data to discover and analyze actual business processes.Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this discipline.Most of the contributions made to privacy-preserving process mining consider an intra-organizational setting, where a single organization wants to safely publish its event data so that process mining experts can analyze the data and provide insights.However, in real-life settings, organizations need to collaborate for performing their processes, e.g., a supply chain process may involve many organizations.Therefore, event data and processes are often distributed over several partner organizations, yet organizations hesitate to share their data due to privacy and confidentiality concerns.In this paper, we introduce an abstraction-based approach to support privacy-aware process mining in interorganizational settings.We implement our approach and demonstrate its effectiveness using real-life event logs.
Classification and analysis of the MNIST dataset using PCA and SVM algorithms

Introduction/purpose: The utilization of machine learning methods has become indispensable in analyzing large-scale, complex data in contemporary data-driven environments, with a diverse range of applications from optimizing business operations to advancing scientific research. Despite the potential for insight and innovation presented by these voluminous datasets, they pose significant challenges in areas such as data quality and structure, necessitating the implementation of effective management strategies. Machine learning techniques have emerged as essential tools in identifying and mitigating these challenges and developing viable solutions to address them. The MNIST dataset represents a prominent example of a widely-used dataset in this field, renowned for its expansive collection of handwritten numerical digits, and frequently employed in tasks such as classification and analysis, as demonstrated in the present study. Methods: This study employed the MNIST dataset to investigate various statistical techniques, including the Principal Components Analysis (PCA) algorithm implemented using the Python programming language. Additionally, Support Vector Machine (SVM) models were applied to both linear and non-linear classification problems to assess the accuracy of the model. Results: The results of the present study indicate that while the PCA technique is effective for dimensionality reduction, it may not be as effective for visualization purposes. Moreover, the findings demonstrate that both linear and non-linear SVM models were capable of effectively classifying the dataset. Conclusion: The findings of the study demonstrate that SVM can serve as an efficacious technique for addressing classification problems.
Historical and retrospective aspects of the development of the regional system of additional (pre-university) education as an environment for the formation of a specialist who meets the requirements of the time

The concept of socio economic development of the Russian Federation pays special attention to the issue of increasing the competitiveness of the domestic economy, which implies improving the training of personnel capable of meeting its needs. Ensuring the high quality of Russian education in accordance with the changing demands of the population are promising tasks for the development of Russian society and economy. Practically every legal document regulating the sphere of education in the Russian Federatio n emphasizes the importance of training and retraining of specialists for various branches of social and economic development, giving priority to the needs of the population for high quality educational services that meet their needs, paying special attent ion to the role of training and retraining of scientific and pedagogical personnel, since they will train specialists various specialties and areas of professional activity in the conditions of social and economic transformations.
A CTC Alignment-Based Non-Autoregressive Transformer for End-to-End Automatic Speech Recognition

Recently, end-to-end models have been widely used in automatic speech recognition (ASR) systems. Two of the most representative approaches are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. Autoregressive transformers, variants of AED, adopt an autoregressive mechanism for token generation and thus are relatively slow during inference. In this paper, we present a comprehensive study of a CTC Alignment-based Single-Step Non-Autoregressive Transformer (CASS-NAT) for end-to-end ASR. In CASS-NAT, word embeddings in the autoregressive transformer (AT) are substituted with token-level acoustic embeddings (TAE) that are extracted from encoder outputs with the acoustical boundary information offered by the CTC alignment. TAE can be obtained in parallel, resulting in a parallel generation of output tokens. During training, Viterbi-alignment is used for TAE generation, and multiple training strategies are further explored to improve the word error rate (WER) performance. During inference, an error-based alignment sampling method is investigated in depth to reduce the alignment mismatch in the training and testing processes. Experimental results show that the CASS-NAT has a WER that is close to AT on various ASR tasks, while providing a ~24x inference speedup. With and without self-supervised learning, we achieve new state-of-the-art results for non-autoregressive models on several datasets. We also analyze the behavior of the CASS-NAT decoder to explain why it can perform similarly to AT. We find that TAEs have similar functionality to word embeddings for grammatical structures, which might indicate the possibility of learning some semantic information from TAEs without a language model.
Targeted Adversarial Attack Against Deep Cross-Modal Hashing Retrieval

Deep cross-modal hashing has achieved excellent retrieval performance with the powerful representation capability of deep neural networks. Regrettably, current methods are inevitably vulnerable to adversarial attacks, especially well-designed subtle perturbations that can easily fool deep cross-modal hashing models into returning irrelevant or the attacker’s specified results. Although adversarial attacks have attracted increasing attention, there are few studies on specialized attacks against deep cross-modal hashing. To solve these issues, we propose a targeted adversarial attack method against deep cross-modal hashing retrieval in this paper. To the best of our knowledge, this is the first work in this research field. Concretely, we first build a progressive fusion module to extract fine-grained target semantics through a progressive attention mechanism. Meanwhile, we design a semantic adaptation network to generate the target prototype code and reconstruct the category label, thus realizing the semantic interaction between the target semantics and the implicit semantics of the attacked model. To bridge modality gaps and preserve local example details, a semantic translator seamlessly translates the target semantics and then embeds them into benign examples in collaboration with a U-Net framework. Moreover, we construct a discriminator for adversarial training, which enhances the visual realism and category discrimination of adversarial examples, thus improving their targeted attack performance. Extensive experiments on widely tested cross-modal retrieval datasets demonstrate the superiority of our proposed method. Also, transferable attacks show that our generated adversarial examples have well generalization capability on targeted attacks. The source codes and datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tswang0116/TA-DCH</uri> .
Deep Learning-Based Classification of Customer Communications of a German Utility Company

The Germany utility company, Zweckverband Ostholstein (ZVO), receives each year a large number of customer communications that are read and classified by its employees. To automate the processing of customer communications, University of Lübeck and ZVO collaborate to model the classification problem of customer communications over a challenging real-world dataset that is multi-label, imbalanced, extremely varied in length and full of noise. To find an optimal model, we first identify suitable classification algorithms and text encoding techniques and then develop 12 deep learning models that combine the different capabilities of neural networks with classical feature-extraction methods and modern word embedding techniques. These models are extensively tested and evaluated, and the results of evaluation are in-depth analyzed and discussed. Finally, we not only find an optimal model for the challenging dataset but also obtain interesting findings and insights that we believe will benefit other deep learning-based text classification projects.
A GNN-Based Architecture for Group Detection from Spatio-Temporal Trajectory Data

Detecting and analyzing group behavior from spatio-temporal trajectories is an interesting topic in various domains, such as autonomous driving, urban computing, and social sciences. This paper revisits the group detection problem from spatio-temporal trajectories and proposes "WavenetNRI", a graph neural network (GNN) based method. The proposed WavenetNRI extends the previously proposed neural relational inference (NRI) method (an unsupervised learning approach for inferring interactions from observational data) in two directions: (1) symmetric edge features and edge updating processes are applied to generate symmetric edge representations corresponding to the symmetric binary group relationships; (2) a gated dilated residual causal convolutional (GD-RCC) block is adopted to capture both short and long dependency of the edge feature sequences. We evaluated the performance of the proposed model on three simulation datasets and three real-world pedestrian datasets, using the Group Mitre metric to measure the quality of the predicted groups. We compared WavenetNRI with four baseline methods, including two clustering-based and two classification-based methods. In these experiments, NRI and WavenetNRI outperformed all other baselines on the group-interaction simulation datasets, while NRI performed slightly better than WavenetNRI. On the pedestrian datasets, the WavenetNRI outperformed other classification-based baselines. However, it did not compete against the clustering-based methods. Our ablation study showed that while both proposed changes cannot be effective at the same time, either of them can improve the performance of the original NRI on one dataset type.
The Review of the Monograph by V.A. Kutyrev. Man is the Age of Technologies, the Philosophy of Falsehood

In his last major work, V.A. Kutyrev continued to reflect the growing process of humanitys self-denial in favor of the emerging post-outside-human reality. The seriousness of the situation the approach of the irrevocable point of singularity requires the formation of an effective philosophy of resistance. Anthropoconservatism should defend the human at the practical, political level.
Development of Graph-Based Knowledge on Ransomware Attacks Using Twitter Data

Ransomware is constantly being developed on underground marketplaces, and spreads through Internet, causing damage to individuals’ and businesses’ data. The purpose of this study is to investigate the current issue related to knowledge graphs on ransomware attacks using Twitter data. To Construct a knowledge graph from informal text, three steps need to be followed. Namely, data collection and cleaning, entity extraction, and relation extraction. Although Natural Language Processing techniques are widely used for text representation and modeling, there exist some limitations related to the lack of a dedicated Named Entity recognizer for extracting Ransomware-related entities from unstructured data such as text. Therefore, this article relies on using the ontology approach to construct a ransomware knowledge graph from unstructured data. An improvement to the ontology is done to make it fit the ransomware attack representation based on data captured from the tweets. The Knowledge Graph was developed by extracting relations between entities. In the end, the accuracy of the Knowledge Graph was evaluated using the formal method.
Analysis of English Machine Translation Methods Based on Intelligent Fuzzy Decision Tree Algorithm

The accuracy of English machine translation results depends on the algorithm used for translation, but the accuracy of the traditional algorithm is insufficient, so to find a new and better algorithm, intelligent fuzzy decision tree algorithm is a good choice. In this paper, the intelligent fuzzy decision tree algorithm is used as the basis of the research, and the English machine translation method based on the algorithm is proposed. Firstly, the present situation of English machine translation and the basic concept of intelligent fuzzy decision tree algorithm are introduced, and the advantages of the algorithm are demonstrated. Secondly, the algorithm model is established and the application test is carried out. Through the research, the intelligent fuzzy decision tree algorithm, English machine translation results are more accurate and semantic smooth, so the application of the algorithm is effective.
Toward Federated Learning With Byzantine and Inactive Users: A Game Theory Approach

Federated learning (FL) can guarantee privacy by allowing local users only upload their training models to central server (CS).However, the existence of Byzantine or inactive users may cause model corruption or inactively participation in FL.In this paper, a game theory based detection and incentive method is designed for Byzantine and inactive users.Specifically, a differential aggregate gradient descent (DAGD) algorithm is adopted to improve the stability and fasten the convergence.Then the loss function is modified by considering Byzantine and inactive users.For Byzantine users, a random Euclidean distance (RED) voting method is designed to identify Byzantine users, and after identification, Byzantine users are motivated by the game theory.For inactive users, when inactive users are detected by contribution, Nash equilibrium with a mixed strategy is used to solve the problem of inactive users' participation in FL.Extensive experimental results show that Byzantine users and inactive users can be detected and motivated by the algorithm.Meanwhile, compared with other methods, the accuracy of the optimized model is improved with reduced training time.