Reviewerly/exHarmony huggingface dataset:
An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection

The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend to be from a trusted company to provide “financial or personal information” are phishing e-mails. These e-mails contain some links; users might download malicious software on their computers when they click on them. Most techniques and models are developed to automatically detect these “SMS and e-mails” but none of them achieved 100% accuracy. In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. After conducting experiments on the real dataset, the researchers concluded that the proposed system performed better and more accurately than previously existing models. Specifically, the support vector machine (SVM) classifier outperformed all others. These results suggest that SVM is the optimal choice for classification purposes.
Dynamic Graph Learning for Data Clustering

Clustering is one of the fundamental techniques in machine learning. It has been widely applied to various research fields, such as gene expression [1], face analysis [2], image annotation [3], and recommendation [4, 5]. In the past decades, many clustering approaches have been developed, such as k-means [6], spectral clustering [7–11] spectral embedded clustering [12] and normalized cut [13].
Dynamic Graph Learning for Feature Selection

In the era of big data, data presents multi-view, high-dimensional and complex characteristics. For one thing, with multi-view features, the data could be characterized more precisely and comprehensively from different perspectives. For another, high-dimensional multi-view features inevitably generate expensive computation costs and cause massive storage costs. Moreover, since raw data generally contains adverse noise, outlying entries, irrelevant and redundant features, the intrinsic dimension of the data may be much lower than the dimension of the raw data. The low-dimensional and robust data representation can effectively improve model efficiency as well as accuracy and thus is vital for downstream tasks in various fields.
Introduction

We find ourselves immersed in an era defined by the exponential growth of data, encompassing images, videos, and documents. As the volume of data escalates, the extraction of numerous features becomes necessary, leading to the challenge known as the curse of dimensionality. Within this high-dimensional data lie redundant information and concealed correlations, surpassing the capabilities of traditional manual processing. In the domains of pattern recognition and data mining, dimension reduction and data clustering emerge as pivotal learning techniques. Dimension reduction seeks to project data from high-dimensional spaces into lower-dimensional spaces, yielding a more concise and compact representation. By reducing the complexity of data processing and facilitating the discovery of data structure information, dimension reduction enables enhanced visualization and accelerates data analysis.
Explainable Document Classification via Pattern Structures

Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation (AMR) graphs. The pipeline generates two kinds of explanations: intermediate and final ones, that justify its classifications. Intermediate explanations are represented as significant subgraphs found in the document graphs of test documents. Final explanations are the sentences of the test documents, that correspond to the significant subgraphs.
A Method for Modeling the Control Impact Strategy Based on the Mental Frame of References of the Decision-Maker

This article proposes a method for modeling decision makers’ control impact strategy based on cognitive causal models (CMs) to demonstrate its advantages and potential uses in adversarial investigations. The CM is a decision-maker’s cognitive system of attitudes and beliefs regarding institutional factors and their relationships specific to a particular situation. The proposed method simulates cognitive intelligence in decision-making and is able to predict the ranked strategic actions that a decision-maker might take. We consider nonstochastic CMs represented by directional weighted signed graphs with deterministic causality on the arcs. Solving the optimization problem on a control model reveals the hidden structures connecting the impact and response of the system through the CM factor rank. The CM factor rank reflects the contribution of each factor to the impact vector, which determines the strategic action of the management decision-maker. The proposed method for modeling the decision-maker’s action does not impose special requirements on the form or the structure of the CM and demonstrates the predictive ability of the CM. The algorithm automatically generates strategic alternatives by identifying and ranking the decision makers’ control actions encoded in the CM, which makes it possible to robotize this part of the human decision-making process.
Explainable AI: Graph Based Sampling Approach for High Dimensional AI System

The widespread use of AI in various industries has been facilitated by advancements in machine learning and neural networks. To shed light on the workings of opaque data-driven algorithms, several mathematical methods have emerged, such as the Shapley value, tree models, and Taylor expansion. Among these, the Shapley value stands out as a popular perturbation method, garnering significant attention. While calculating Shapley values is known to be an NP-hard problem, some researchers have introduced approximate techniques to alleviate this challenge. However, striking a balance between accuracy and time cost remains difficult, particularly as the number of players involved increases. In this paper, we propose a novel approach that efficiently computes Shapley values using fewer high-quality coalition samples, relying on the relationship map.
Operating with Fuzzy Cases in Distributed Intelligent Systems

The paper deals with issues related to the research and development of means of operating and finding solutions based on fuzzy cases in a distributed intelligent system implemented on the principle of a multi-agent system.
Research on Neural Network Defense Problem Based on Random Noise Injection

Because neural network is a data-driven black box model, people cannot directly understand its decision basis, and once the neural network is constructed with adversarial samples, it can lead to wrong conclusions with high confidence. Therefore, many researchers focus on the robustness of the neural networks. This paper mainly studies neural network defense based on random noise injection. In theory, injecting exponential family noise into any layer of neural network can ensure the robustness of neural network. But the experiment shows that the disturbance resistance effect varies greatly with different noise distribution. We investigate the robustness of neural networks for injection of exponential and Gaussian noise, and give the upper bound of Renyi divergence under these two types of noise. In terms of experiments, we uses CIFAR-10 dataset to conduct experiments on a variety of neural network structures. It is found that random noise injection can effectively reduce the attack effect of adversarial sample and make the neural network more robust. However, when the noise is too high, the classification accuracy of the neural network itself will decline. This paper proposes to add Gaussian noise with small variance to the image subject and Gaussian noise with large variance to the background, so as to achieve better defense effect.
Approach to the Selection of Significant Parameters for Diagnostics of Complex Technical Systems Using Soft Computing

When diagnosing complex technical systems (CTS), one of the main tasks is selection of significant diagnostic parameters that characterize the technical condition (TC) of CTS. A lot of factors have a great impact on the CTS operation, so neglecting to take them into account when diagnosing can lead to making wrong decisions concerning their operation. To solve this problem we propose an approach to the selection of significant parameters for diagnosing TC of CTS with the use of “soft computing” under conditions of incomplete and fuzzy information. The novelty of the approach is in the combination of two technologies: cognitive modeling methodology (relations between the parameters in the form of fuzzy, verbal description) and the theory of fuzzy sets (relations between the parameters in the form of verbal description). The statement of the problem is considered and an algorithm for the selection of significant parameters with a detailed description of each step is proposed. An example of application of the developed approach to the selection of significant diagnostic parameters, affecting the TC of asynchronous electric motor (AM) of BAO5К−450, 560 series is considered. The approach proposed will make it possible to formalize the knowledge of the personnel on duty, which may be incomplete or subjective, as well as to increase the speed of decision-making about the TC equipment under conditions of incomplete and fuzzy information.
Cognitive Architecture of a System to Replicate Human Strategic Decision-Making

This article addresses the problem of creating an artificial intelligence (AI) system to replicate human management decision-making in the development of control impact strategies using cognitive causal models (CMs) of complex systems (sociohumanitarian, political). The basis of replication is the research and development of a cognitive architecture as a fixed base of closely related mechanisms that underlie intelligent behavior. The cognitive architecture of the proposed system is based on the hypothesis that human management decision-making is based on a general (abstract) idea of the system as a whole (mental image) in the paradigm of causal relations, which is created as a result of a special type of mental perception. This special kind of perception is the ability of a person to create mental images using generalization and abstract thinking. We present these mental images as professional knowledge that a person acquires through experience and learning throughout his life, and which is stored in the human brain. The acquisition of professional knowledge for storage in long-term memory will be carried out by accessing the vast database of world knowledge on the Internet (initial knowledge), processing the discovered data into CM, and storing this new knowledge. The decision procedure retrieves the CM stored in the declarative semantic memory, compares it with the current situation, and updates this CM representation using the CM learning mechanism. Further, the solution of the optimization problem creates ranked possible scenarios of control actions.
Ontology-Based Methodology for Knowledge Maps Design

Visualization of corporate knowledge in organizations creates the potential for a significant improvement in the quality of decision making and management efficiency. However, at the moment there is little correlation between the needs of enterprises and new technologies in the field of knowledge engineering. Models and methods of knowledge visualization are not yet mature enough to solve practical problems of information and knowledge management. This is especially true for knowledge-intensive enterprises and organizations, universities, and research institutes. The paper presents a new methodology for the knowledge and competences mapping of the faculty members (or any employees). The methodology is supported by the ontology-based approach and significantly expands the traditional knowledge engineering palette of methods. Ontologies as conceptual models of the subject areas are one of the most promising approaches to the development of knowledge bases and knowledge graphs. The importance of ontology engineering is rapidly gaining the new adopters. The proposed methodology consists of 4 main meta-steps to create a knowledge map: The proposed ONE-VID approach was implemented at Graduate School of Management in Saint-Petersburg State University for the sample of 63 faculty members. Several types of K-maps were designed and developed – treemaps, radars, knowledge profiles, etc. The paper describes the new methodology and illustrates the first results. The subsequent longitude study plan is considered that will give the possibility to compare the specific knowledge profiles and gaps as time passes.
Tracking to Success? A Critical Reflection on Workplace Quantified-Self Technologies from a Humanistic Perspective

Self-tracking has become omnipresent in our daily lives. By providing insights into how work practices relate to performance or (physical and mental) health, quantified-self technologies (QST) have also made their way into the world of work. Contemporary QST process personal data from multiple sources to help people to gain a better knowledge about themselves at work. According to humanistic psychology, humans have an initial drive to grow and self-actualize, that is, to become a better version of themselves. A key step towards self-actualization is accepting oneself without meeting certain conditions and thus questioning so called “conditions of worth” (e.g., I am a valued person if I am good at my job). In this article, we discuss possible effects of QST at the workplace. We raise the question, how QST could be either used as a tool to locate personal growth outside the capitalist labor process or, on the contrary, to create conditions of worth and thus promote the “entrepreneurial self” – a self which is driven to constantly improve and therefore vulnerable for self-exploitation. In QST development, design choices determine whether they are more designed toward personal growth outside the capitalist labor process or toward the idea of an entrepreneurial self. Thus, we conclude this article by formulating implications for the design of QST, which we cluster along the actors and purpose of the technology (Who? and Why?), the specifics of the work context (Where?), the temporal aspects of the usage (When?), and the design elements used (How?).
Co-active of Fuzzy Temporal Ontological Models and Fuzzy Temporal Cognitive Models for the Analysis and Forecasting of Complicated Systems

The expediency of joint application of Fuzzy Temporal Ontological Models and Fuzzy Relational Temporal Cognitive Models for complex analysis and multidimensional state prediction of complicated systems is substantiated. Fuzzy Temporal Ontological Models are proposed for representation, complex analysis, and display of the dynamics of changes in the state of the system. The specific feature is that the attributes corresponding to the parameters of its vector space are characterized by time series of corresponding crisp or fuzzy values, obtained measurements or estimates, and from their multivariate forecasting. The fuzzy granulation of Fuzzy Temporal Ontological Model is performed at the level of fuzzy values of time series of these attributes, as well as at the level of fuzzy values of binary relations of mutual influence between model attributes. Fuzzy Relational Temporal Cognitive Models are proposed for multidimensional prediction of system vector space parameters, which allow typifying the tuning of temporal operators and transformations: «personification» of system dynamics models; tuning of fuzzy relations between concepts based on learning algorithms using generated training samples for component time series; the calculation of dependencies between fuzzy specified parameters in vector-matrix form.
Explanatory Capabilities of an Ontology-Based Solver with Linguistic and Visual Support

An approach to the explanatory capabilities of an ontology-based solver with linguistic and visual support is developed. The results of the study of the computer implementation of the approach in an education-oriented system are described. Explored the explanatory capabilities at each stage of the system: linguistics, solution search, visualization. The use of the ontology allows transparency of the system solutions in accordance with the 4 principles of XAI. The search for a solution is based on the ideas of George Polya. Instrumental means of implementation: VBA macros, JavaScript, JSXGraph. Developed approach is focused on the consideration of cognitive aspects in XAI, and in the application plan - on the creation of learning systems of a qualitatively new level.
Protecting Secrets from Computers

Bob is in prison and Alice is dead; they trusted computers with secrets. Review time-tested tricks that can help you avoid the grim fate of the old crypto couple.
Characterization of the Person’s Leading Interests in Terms of RIASEC Scores

The paper discusses the lack of automated systems that provide personalized career advice without the need for traditional career guidance tests or counselling sessions. It suggests analysing social media data, specifically the topics of the user subscriptions, could be used to generate personalized career recommendations. The article proposes the use of the RIASEC test to identify the mean RIASEC profile of users with the same leading subscription topic. The study to test the hypothesis that the RIASEC codes vary for leading group subscriptions in terms of frequencies of appearance in the upper and lower triades and in terms of the mean scores. The research methods are statistical analysis, namely the chi-squared test for homogeneity with the Bayesian Factor and the Kruskall-Wallis test. The statistical analysis confirms the existence of RIASEC score patterns for leading group topics, and for several leading subscription topics the patterns of the environment RIASEC profile were established. The findings of the study can be used to develop decision making intelligent system that utilize social media data to provide personalized career guidance.
Robust structured light 3D imaging with two fringe patterns using recurrent classification neural network

Abstract Robust and accurate 3D reconstruction using a limited number of fringe patterns has posed a challenge in the field of structured light 3D imaging. Unlike traditional approaches that rely on multiple fringe patterns, using only one or two patterns makes phase recovery and unwrapping difficult. To address this issue, a recurrent classification neural network (RCNN) has been developed, transforming the phase recovery and unwrapping tasks into a unified phase classification task. First, a training dataset consisting of 1200 groups of data was collected to generate a total of 38 400 training samples, enabling the RCNN to learn the mapping between the input fringe patterns and the corresponding label maps. Then, based on the well-trained network, a label map is generated based on the input two fringe patterns using the output classification results. Finally, 3D reconstruction data could be obtained by combining the inferred label map with the vision system’s parameters. A series of comprehensive experiments have been conducted to validate the performance of the proposed method.
Quantum kernel evaluation via Hong–Ou–Mandel interference

Abstract One of the fastest growing areas of interest in quantum computing is its use within machine learning methods, in particular through the application of quantum kernels. Despite this large interest, there exist very few proposals for relevant physical platforms to evaluate quantum kernels. In this article, we propose and simulate a protocol capable of evaluating quantum kernels using Hong–Ou–Mandel interference, an experimental technique that is widely accessible to optics researchers. Our proposal utilises the orthogonal temporal modes of a single photon, allowing one to encode multi-dimensional feature vectors. As a result, interfering two photons and using the detected coincidence counts, we can perform a direct measurement and binary classification. This physical platform confers an exponential quantum advantage also described theoretically in other works. We present a complete description of this method and perform a numerical experiment to demonstrate a sample application for binary classification of classical data.
Understanding Potential Causes of Learning Loss: Teachers’ Perceptions Regarding Educational Challenges During the COVID-19 Pandemic in Mexico

Abstract School lockdowns due to the COVID-19 pandemic resulted in a rapid transition from face-to-face to distance learning without considering teachers’ capacities and experience. Deep educational inequalities observed in Mexico before the pandemic made this change complex, disproportionately affecting the poorest students and their families. Estimations about the magnitude of the adverse effects of the transition on student learning are now available for different countries and regions. However, research on the factors explaining these adverse effects is limited, including how teachers reacted to the lockdown policies and adapted their instructional practices during the pandemic to adequately support all students. This chapter describes and analyzes teachers’ perceptions regarding the main barriers that affected their instructional activities during the pandemic to identify policy lessons that may inform the design of educational interventions for a post-pandemic stage.
The Switch to Distance Teaching and Learning in Finland During the COVID-19 Pandemic (2020–2022) Went Technically Well but Was Emotionally Challenging

Abstract In this chapter, we analyze and discuss teaching, learning and well-being in Finnish education during the COVID-19 pandemic between Spring 2020 and Fall 2022. First, we analyze the preconditions, such as teachers’ and students’ digital competences and the digital infrastructure necessary to switch to distance teaching and learning. Second, we present the results of a survey concerning the organization and experience of teaching and learning during the COVID-19 pandemic. Third, we discuss the engagement and well-being of teachers, principals and students during the pandemic based on survey data. During the pandemic, teachers developed digital pedagogy and students enhanced their digital competence, and several digital pedagogy and co-teaching innovations were created. However, we identified decreased engagement among students, teachers, and principals during the pandemic and an increase in stress and burnout among teachers and principals. Principals felt the impact of the stress their teachers faced, and teachers struggled to make up for the differing efforts among families to cope with distance learning. Overall, the switch to distance teaching and learning was organized effectively, but the distance-learning period weakened the equality of teaching and the conditions that encourage learning and well-being.