Abukar Mukhtar Omar, Ali Abdi Jama, Abdirahman Ibrahim Abdi
Abstract This study investigates how infrastructure challenges, stakeholder engagement, and technical capacity shape the effectiveness of Education Management Information Systems (EMIS) in post-conflict Somalia. Drawing on survey data from 210 school administrators, teachers, ministry officials, and IT specialists, the research applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a framework grounded in Rogers’ Diffusion of Innovations Theory and the DeLone & McLean IS Success Model. The findings show that both infrastructure challenges and stakeholder engagement exert direct and significant effects on EMIS effectiveness. Stakeholder engagement also strongly enhances technical capacity, underscoring the importance of inclusive participation in sustaining system reforms. By contrast, technical capacity on its own does not significantly improve effectiveness and does not mediate the effects of either infrastructure or engagement. Unexpectedly, infrastructure challenges demonstrated a positive association with both EMIS effectiveness and technical capacity—an outcome that may reflect adaptive learning, targeted support, or compensatory strategies in resource-constrained contexts. This indicates that a multi-faceted approach, addressing both stakeholder participation and infrastructure deficits, is required to improve the functioning of EMIS in Somalia. In doing so, EMIS can become a cornerstone for advancing data-driven decision making, educational accountability, and the achievement of Sustainable Development Goal 4 (quality education) and Goal 16 (strong institutions).
Mahmud Hasan, Mathias Nthiani Muia, Md Mahmudul Islam
The Information Maximizing Generative Adversarial Network (InfoGAN) can be formulated as a minimax problem involving a generator and a discriminator, augmented by a mutual information regularization term. Despite strong empirical performance, rigorous generalization guarantees for InfoGAN-type objectives remain limited, particularly when additional structural components are introduced. In this paper, we study an InfoGAN-inspired adversarial framework obtained by removing the latent code component and introducing an explicit regularization term on the generator, yielding an analytically tractable generator-regularized adversarial objective. We establish generalization error bounds by analyzing the gap between empirical and population objective functions using Rademacher complexity arguments for the discriminator, the generator, and their composition. The resulting bounds reveal explicit n−1/2 and m−1/2 decay rates with respect to the discriminator and generator sample sizes and clarify the role of the generator regularization parameter. The theory is further specialized to two-layer neural networks with Lipschitz continuous and non-decreasing activation functions, where explicit entropy-based complexity bounds are derived. Experiments on the CIFAR-10 dataset validate the predicted scaling behavior and demonstrate that the generalization gap decreases systematically as sample size increases, highlighting the stabilizing effect of generator regularization. Overall, this work provides one of the first rigorous generalization analyses for an InfoGAN-inspired adversarial objective with explicit generator regularization.
This paper addresses the distributed optimization problem in linear multiagent systems (MASs) under external disturbances. Firstly, an observation system is designed by utilizing the output values of agents, which can eliminate external disturbances of system. Secondly, an event-triggered control algorithm is proposed through the gradient information of local cost functions, and its convergence is rigorously established using the Lyapunov stability and looped functional theory. This novel event-triggered protocol incorporates dwell time within the threshold function, effectively eliminating Zeno behavior. By leveraging the looped functional technique, more relaxed conditions are derived for solving the distributed optimization problem. Finally, the validity and feasibility of the proposed protocol are substantiated through numerical simulation.
Due to applicability of mathematical inequalities in control systems, actuarial science, information theory and its utilization in other sciences, several researchers are inclined to prove their refinements and generalizations. In the study of mathematical inequalities, convex functions (CFs) and the derived classes of CFs are the most studied objects. On the other hand, fractional calculus has emerged as an indispensable tool in Engineering, Physics, and Mathematics, with widespread applications. The recently introduced Cr-order based class of interval-valued functions (IVFs) has shown applications in information theory. In this paper, we aim to investigate mathematical inequalities for geometrically arithmetically Cr-convex functions (GA-Cr-CFs) via interval-valued Hadamard fractional integral operators (IVHFIOs). More precisely, we study the Hermite–Hadamard–Mercer type inequalities and the weighted Hermite–Hadamard–Mercer type inequalities involving GA-Cr-CFs and IVHFIOs. Under particular assumptions, the proved results produce inequalities from the recent related literature. Furthermore, we establish connections between geometrically arithmetically convex functions (GA-CFs) and self information function of each event with a given probability in a complete information system. At the end, we demonstrate the role of fractional order of IVHFIOs in producing the refinements of the inequalities for the usual integrals. Thus, the current study provides both the extension of theoretical literature as well as application aspects.
Dhanya Pramod, Kanchan Pranay Patil, Vijayakumar Bharathi S
This study examined the impact of deepfakes on consumer protection behaviour and psychosocial responses, focusing on threats and coping appraisals in deepfake marketing. The study applied a two-theory framework combining the Theory of Planned Behavior and the Protection Motivation Theory. Data from 317 adult consumers were collected using a structured questionnaire. Scales were adapted from prior research, and analysis was conducted using Partial Least Squares Structural Equation Modeling (Smart-PLS 4.0 software). The results revealed that threats, attitudes, and subjective norms significantly influenced protective behaviour, while perceived behavioural control did not. Perceived severity and susceptibility significantly affected attitude and motivation to comply impacted consumers’ subjective norms. Perceived Response Efficacy, Self-Efficacy, and Perceived Response Cost were not supported as drivers of perceived behavioural control. This research on consumers’ threat and coping appraisals of deepfake technology offers key insights. It advances consumer behaviour theories in information systems, aids stakeholders like companies and marketers, and supports policymakers in developing regulations and safeguards against deepfake threats.
S. Sangeetha, T. Aruldoss Albert Victoire, M. Premkumar
et al.
This research focuses on Wireless Sensor Networks (WSNs) and proposes a three-phase approach to achieve energy-efficient routing. The approach consists of node deployment using Voronoi diagrams, clustering, and Cluster Head (CH) selection using energy-efficient game theory, and a routing strategy based on Improved Pelican Optimization (ImPe) segment routing. Random deployment of sensor nodes in WSNs can lead to coverage issues, and hence, in order to address this, Voronoi-based node deployment is employed to ensure uniform and balanced coverage of the monitoring area. An energy-efficient game theory-based approach is used for CH selection by considering the energy levels to select CHs for enhancing network longevity. The proposed routing mechanism utilizes segment routing, which provides deterministic routing paths from CHs to the sink (Base Station). Segment routing eliminates the need for route discovery and maintenance, making it energy-efficient. The ImPe algorithm that works on the characteristics of pelican search agents is employed to choose the optimal segment path for information sharing. The assessment based on delay, network lifetime, packet delivery ratio, residual energy, throughput, communication overhead, and energy utilization acquired the values of 2.57, 98.59, 98.29, 0.98, 238.51, 7.71, and 0.02 respectively.
Control engineering systems. Automatic machinery (General), Automation
Muhammad Adil Khan, Hidayat Ullah, Tareq Saeed
et al.
The field of mathematical inequalities has exerted a profound influence across a multitude of scientific disciplines, making it a captivating and expansive domain ripe for research investigation. This article offers estimations for the Slater difference through the application of the concept of convexity. We present a diverse type of applications that stem from the main findings related to power means, Zipf–Mandelbrot entropy, and within the field of information theory. Our main tools for deriving estimates for the Slater difference involve the triangular inequality, the definition of the convex function, and the well-established Jensen inequality.
Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model.
Andrey Ivashko , Andrey Zuev , Dmytro Karaman
et al.
The purpose of the study is to develop methods for synthesizing a Gaussian filter that ensures simplified hardware and software implementation, particularly filters with powers-of-two coefficients. Such filters can provide effective denoising of images, including landscape maps, both natural and synthetically generated. The study also involves analyzing of methods for FPGA implementation, comparing their hardware complexity, performance, and noise reduction with traditional Gaussian filters. Results. An algorithm for rounding filter coefficients to powers of two, providing optimal approximation of the constructed filter to the original, is presented, along with examples of developed filters. Topics covered include FPGA implementation, based on the Xilinx Artix-7 FPGA. Filter structures, testing methods, simulation results, and verification of the scheme are discussed. Examples of the technological placement of the implemented scheme on the FPGA chip are provided. Comparative evaluations of FPGA resources and performance for proposed and traditional Gaussian filters are carried out. Digital modeling of the filters and noise reduction estimates for noisy images of the terrain surface are presented. The developed algorithm provides approximation of Gaussian filter coefficients as powers of two for a given window size and maximum number of bits with a relative error of no more than 0.18. Implementing the proposed filters on FPGA results in a hardware costs reduction with comparable performance. Computer simulation show that Gaussian filters both traditional and proposed effectively suppress additive white noise in images. Proposed filters improve the signal-to-noise ratio within 5-10 dB and practically match the filtering quality of traditional Gaussian filters.
This article examines the issue of education from the point of the system theory of the modern German sociologist Niklas Luhmann. The main goal was to present arguments in favor of the possibility of education as a system, to describe its main functions and to highlight the problem of the medium. Firstly, the problem of translation of the German term Erziehung and its English counterpart Education was described; the existence of ambiguity, due to which it is possible in the context of the system theory to talk about both education and upbringing. Against this background, it was decided to use both terms as synonyms, bearing in mind their common meaning and the possibility of reverse translation. Then, by describing the main terms, Luhmann’s general understanding of the system theory and the system as a whole was given. Was mentioned such concepts as: distinguishing between the system and Umwelt, the phenomenon of self-reference and form. This gives rise to the second term – autopoiesis. The term was taken by Luhmann from the Chilean scientist Humberto Maturanа, the main point is in the special ability of systems to reproduce themselves from their own parts and to reproduce the parts themselves. A specific feature of autopoiesis is that it does not affect the final form. The phenomenon that provides autopoiesis is communication. It is possible because it is based on understanding and misunderstanding, which is found when distinguishing between message and information. From this constant distinction, sense is born. The possibility of understanding sense by a human, which is a psycho-physical system, is provided by structural coupling, openness of the system to external information. Based on this, we can describe the educational system. It is aimed at the formation and editing of the Person – a social symbol of communication. By providing each pupil with the same necessary knowledge, the education system thus increases the success of future communication. The medium that enables the system is the Pupil. However, significant social changes led to its reinterpretation and the emergence of a new term Lebenslauf, which causes problems in translation and interpretation.
Among numerous vegetation studies, there are few studies on the elevation gradient distribution control mechanism and horizontal law of small-scale vegetation. In the same climate zone, topography is one of the most important factors affecting vegetation pattern. Here, we used the geo-informatic Tupu theory to construct topographic gradient-vegetation distribution information Tupu (TG-VDI Tupu) to display the topographical differentiation characteristics of vegetation. Moreover, an improved evaluation of topographical differentiation characteristics of vegetation was proposed based on topographic gradients, and the topographic composite index (TCI) was constructed to analyse the topographic variation in vegetation distribution. Meanwhile, the dominant factors and limiting factors affecting vegetation distribution under different topographic gradients were determined through statistical analysis. Combined with field surveys, Gaofen-1 (GF-1) satellite images were used to extract vegetation types, and the solar radiation value (SRV), topographic wetness index (TWI) and topographic variables were extracted from DEM data. The results indicate that TG-VDI Tupu can visually display topographic differentiation characteristics of vegetation on an elevation gradient. Elevation controls the horizontal distribution of vegetation on a small scale by changing ecological factors. At the same elevation, slope affects vegetation distribution by changing the SRV and TWI, while aspect changes the TWI. Coniferous forest is separated along a slope gradient and is more abundant on steep slopes. The percentage of broadleaf forest is negatively correlated with SRV and positively correlated with TWI, and the proportion is higher on the leeward slope facing north. The distribution of shrubs is more abundant on more xeric aspects and on steeper and more xeric slope configurations. In alpine areas above 2800 m, the abundance of vegetation types declines. This decline may be related to the weak solar radiation and widespread glacial landforms, exposed rocks and strong weathering. The methodology in our study can be applied to other regions and is expected to provide useful information for ecological conservation policy making.
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely. Among which, context-based OMRL (COMRL) as a popular paradigm, aims to learn a universal policy conditioned on effective task representations. In this work, by examining several key milestones in the field of COMRL, we propose to integrate these seemingly independent methodologies into a unified framework. Most importantly, we show that the pre-existing COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $M$ and its latent representation $Z$ by implementing various approximate bounds. Such theoretical insight offers ample design freedom for novel algorithms. As demonstrations, we propose a supervised and a self-supervised implementation of $I(Z; M)$, and empirically show that the corresponding optimization algorithms exhibit remarkable generalization across a broad spectrum of RL benchmarks, context shift scenarios, data qualities and deep learning architectures. This work lays the information theoretic foundation for COMRL methods, leading to a better understanding of task representation learning in the context of reinforcement learning. Given its generality, we envision our framework as a promising offline pre-training paradigm of foundation models for decision making.
Sadly, our esteemed colleague and friend Ning Cai passed away on 25th May, 2023. In his memory, Ingo Althöfer, Holger Boche, Christian Deppe, Jens Stoye, Ulrich Tamm, Andreas Winter, and Raymond Yeung have organized the "Workshop on Information Theory and Related Fields" at the Bielefeld ZiF (Center for Interdisciplinary Research). This special event will be held from 24th November to 26th November, 2023. The workshop aims to celebrate Ning Cai's remarkable contributions to the field of information theory and to provide a platform for discussing current research in related areas. Ning Cai's work has had a significant impact on many domains, and this gathering will bring together colleagues, collaborators, and young researchers who have been influenced by his pioneering efforts.
Construction projects are inherently complex and entail extensive information processing. Thus, they require effective information management, which, in turn, requires the preservation of critical construction data (CD). Although BIM and blockchain methodology use the “change type of query and storage for data management” to improve the service quality of data, data redundancy still causes inefficient retrieval. Moreover, project managers face various source limitations, which prevent the contents of the database from being managed efficiently. This study uses network analysis theory to design an information network (IN). Critical CD were extracted, and an IN structure was built using data from construction practices (network nodes) and data relation (network links). Three metrics were used for performance evaluation of the data references and data delivery. The refurbishment of heritage buildings in Kinmen, Taiwan, was used as a case study to extract critical CD such as the “inspection record checklist” and “architect design plan drawing”. Lastly, CD can be applied as the elementary item of a backstage database for BIM and blockchain applications of DM. The combined system of critical DM can play an important role in obtaining comprehensive information for a construction project. Customized metrics of IN analysis can be developed as an integrated composite to decide the priority of CD.
Employee innovative behavior is significant in maintaining an organization's sustainable development. This study explored the impact of team psychological safety and workplace anxiety on the association between self-serving leadership and employee innovation behavior by synthesizing social information processing theory, conservation of resources theory, and ego depletion theory. We conducted a hierarchical linear model analysis using three-wave paired data collected from 86 leaders and 392 employees. The research results showed that self-serving leadership is negatively correlated with employee innovation behavior. Meanwhile, team psychological safety and workplace anxiety mediated this relationship. In addition, team psychological safety mitigates the impact of workplace anxiety on employee innovation behavior and the indirect impact of self-serving leadership on employee innovation behavior via workplace anxiety. These findings have a number of theoretical and practical implications in the domains of self-serving leadership and employee innovation behavior.
The issue of preparing teachers has been a forcing concern for a lot of educators in Egypt especially at the age of rapid technological changes. So, certain rules should be issued to guarantee choosing the best teachers; the thing which cannot be achieved without professionalizing education by issuing licenses for working as teachers. The license of professionalizing the job of teachers is considered an acknowledgement of the teacher's ability to teach. In addition, this license encourages teachers to develop themselves and upgrade their imaginative thinking for a long time. There must be an official department to issue this licence according to regulations and criteria corresponding with the necessary regulations for doing the teaching effectively and efficiently.
Education (General), Theory and practice of education
This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space and (ii) jointly embeds users and content items (that they interact with) into that space in a manner that facilitates a number of downstream tasks, such as stance detection, stance prediction, and ideology mapping. Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e.g., posts that represent user views) into an appropriate disentangled latent space. To better disentangle latent variables in that space, we develop a total correlation regularization module, a Proportional-Integral (PI) control module, and adopt rectified Gaussian distribution to ensure the orthogonality. The latent representation of users and content can then be used to quantify their ideological leaning and detect/predict their stances on issues. We evaluate the performance of the proposed InfoVGAE on three real-world datasets, of which two are collected from Twitter and one from U.S. Congress voting records. The evaluation results show that our model outperforms state-of-the-art unsupervised models by reducing 10.5% user clustering errors and achieving 12.1% higher F1 scores for stance separation of content items. In addition, InfoVGAE produces a comparable result with supervised models. We also discuss its performance on stance prediction and user ranking within ideological groups.