Reviewerly/exHarmony huggingface dataset:
Adoption Model for Cloud-Based E-Learning in Higher Education

Regarding the use of e-learning in higher education institutions, there is a developing trend. A significant upfront infrastructure with several establishments is needed for an e-learning framework. To keep up with the fast change that is essential to globalization, higher education institutions (HEIs) must overcome various obstacles. The classic e-learning approach is therefore insufficient now because of all the difficulties and challenges. The appealing cloud-based e-learning (CBEL) platform offers a scalable and flexible e-learning method that can be accessed from any location, at any time, and using any device. The proposed framework has been built based on various elements: technological evaluation, readiness evaluation, and information culture factors. These were extracted from two well-known technology adoption theories, the Fit-Viability Model (FVM) and the Diffusion of Innovation (DOI) model. Information culture (IC) elements were chosen as a consideration since they have a substantial impact on the adoption of any technology. To investigate its substantial impact on the adoption of CBEL in HEIs in Oman as well as its viability, 14 proposed hypotheses were established. Data was collected from a sample of academics and IT professionals from 32 HEIs in Oman using a structured survey with standardised questions. The Statistical Package for Social Science (SPSS version 25) and Partial Least Squares (SmartPLS version3) were used to evaluate the proposed CBEL model and to examine the relationship between them. Based on the findings, which demonstrated that factors like fit, viability, and information culture strongly influenced HEIs’ decisions to adopt CBEL in Oman, the final model was created. The resulting model demonstrated that 68.2% of the critical elements for CBEL adoption were addressed, and that by using this model, the quality of academic services could be improved by 56.1%.
Relu Dropout Deep Belief Network for Ontology Semantic Relation Discovery

This paper presents the learning strategy and the environment for Ontology Learning (OL) relations discovery task for the scientific publications domain by adjusting Deep Belief Network (DBN). The adjusted DBN is called Relu Dropout DBN (Re-DDBN). This paper elaborates on the adjusted Re-DDBN configuration, its structure, hyper-parameters, and functions. In addition, the adjusted Re-DDBN was compared with traditional DBN and other comparative models (e.g., Support vector machine (SVM) and Naïve Bayes (NB)) for ontology semantic relations discovery. The outcomes revealed that the adjusted Re-DDBN displayed the best performance when compared to the other models. The SemEval-2018 task 7 dataset was applied in this study.
Implementation of MLOps

The rise of machine learning (ML) has reshaped industries, demanding efficient ML model deployment. This has led to the emergence of Machine Learning Operations (MLOps), which streamlines the entire ML workflow. MLOps aligns data, model, and code components to ensure accurate and scalable models. However, testing and di-verse execution environments are underrepresented in existing literature. This paper bridges this gap by presenting crucial tests for reliability and proposing an integrated MLOps framework that unifies testing with different environments. The paper structure encompasses foundational definitions, a standardized development process, and con-cludes with insights into enhancing MLOps reliability.
Critique or Exclusion? Motives, Forms, and Consequences of Online Political Toxicity

Social media have become an important arena for political representation. However, politicians are increasingly subject to insults, threats, and offensive comments when doing politics online. Should we therefore consider citizen-elite toxicity a challenge for the online democratic discourse? This paper investigates two main hypotheses; the ”critique” vs. the ”exclusion” hypothesis. Whereas the first suggests that online toxicity is used a means to critique the powerful, the latter suggests that it serves to further excluding politically marginalized groups. The paper draws on a full sample of candidates’ campaign activities on Twitter in the run-up to the 2021 German national election to investigate these claims empirically. Using mixed effect models, I estimate variation in the degree, the type, and the consequences of toxicity in over 13,000 Twitter conversations between citizens and political candidates. I find, first, that all candidates are equally likely to receive toxic replies to their tweets. However, men, incumbents, and members of government parties are targeted more often directly and receive more personal insults. Experiencing high amounts of online toxicity reduces tweeting in the following days by about 35%. The paper thus suggests that toxicity works as a corrective of the powerful rather than as an exclusionary mechanism.
Adaptive particle filter to reduce the pose estimation problem to achieve time-efficient navigation of UGV

The proposed research focuses on designing an adaptive filter based on the particle filter in which the number of particles has been optimised by considering the complexity factor of the environment and a mathematical relation for finding out the environment's complexity designed to achieve reliable pose estimation. The performance of traditional particle filters suffers from the time/space complexity, resulting in the time lag problem during the autonomous navigation of vehicles. The proposed adaptive filter is tried and tested in various simulated experiments and it is experimentally obtained that it reduces the computational load by 5.22% and the error in the predicted and actual measurement model is also reduced by 19.66% w.r.t to low complexity environment, 16.05% w.r.t medium complexity environment and 15.35% w.r.t high complexity environment. The proposed technique is applicable in the various subfield of unmanned ground vehicles such as path planning, trajectory tracking problems, autonomous navigation, etc.
Pattern-Based Risk Identification for Model-Based Risk Management

In a previous publication, we have introduced Risk Issue Questionnaires (RIQs) that serve to support risk identification for critical systems. The starting point of our risk identification method are architectural patterns contained in a system architecture, e.g., process control loops or interactive systems. A RIQ enumerates the typical risks associated with such a pattern. By assessing for each issue contained in a RIQ whether it is relevant or not, risks for the system under analysis are identified in a systematic way. In this paper, we complement the RIQ method by a method to set up and validate CORAS threat models for documenting the identified risks. In this way, we provide a basis to perform the further steps of a model-based risk management process. We equip our RIQs with modeling hints that specify what kind of modeling element should be used to represent a given issue in a threat model. Furthermore, we define formal validation conditions (VCs) that allow the risk modeler to check the generated threat models for coherence and completeness, and present a modeling tool that is able to check the defined VCs.
Explainability

While the previous chapter covered Human-in-the-Loop and its importance in building responsible AI algorithms, it’s paramount to ensure the transparency and explainability of ML models following HITL processes. This chapter reviews “explainability,” also known as XAI and its implementation.
Robustness

In addition to ensuring that ML models and applications are respectful and cognizant of people’s privacy, and that they don’t infringe on human rights by violating privacy laws, it’s also important that AI technologies be protected from cyberattacks. This involves building robust ML models, which is another fundamental part of responsible AI.
AUTOMATED ACCESS CONTROL OF HIGHER EDUCATIONAL INSTITUTIONS EMPLOYEES TO INTELLECTUAL RESOURCES

Modern methods of teaching students of higher educational institutions involve the use of remote methods of communication, including the intellectual resources transfer. In this case, special information learning environments are most often used. Unfortunately, as a rule, information environments have only one authentication method for both employees of higher educational institutions and students. The article is devoted to the development of methods for restricting access of employees of higher educational institutions to intellectual resources in order to increase the level of information security.
SUPPORTING TEACHING AND LEARNING ACTIVITIES IN MODERN UNIVERSITY: DEVELOPING ADMINISTRATION MODEL

The paper presents the results of the research aimed at developing a functional model of an enhanced organization structure to effectively manage teaching and learning processes at higher education establishments. The new design features the multifunctional student center (MFC) in Moscow City University, with management based on two basic principles: on the one hand, process approach to management and, on the other hand, delegating decision-making functions to lower-level employees. The main focus in the MFC administration policy is made on engaging the staff in the processes of digital transformation which indicates a sea change in traditional patterns of educational administration. The new administration model is described in terms of the queuing theory, reinforced by statistical analysis of empirical data: volume and value figures characterizing administrative processes aimed at supporting learning activities. The criteria of effective management are defined, the working hours spent on support activities are calculated. The results can be used in employee target-setting strategies: for individuals, units and departments. The new model of administrating educational activities, developed in MCU, might be implemented in other educational institutions.
Mixture Models

In this chapter we introduce the clustering problem and use it to motivate mixture models. We start by describing clustering in a frequentist paradigm and introduce the relevant likelihoods and latent variables. We then discuss properties of the likelihoods including invariance with respect to label swapping. Finally, we expand this discussion to describe clustering and mixture models more generally within a Bayesian paradigm. This allows us to introduce Dirichlet priors used in inferring the weight we ascribe to each cluster component from which the data are drawn. Finally, we describe the infinite mixture model and Dirichlet process priors within the Bayesian nonparametric paradigm, appropriate for the analysis of uncharacterized data that may contain an unspecified number of clusters.
Undercover Investigations and New Technologies

This chapter considers some of the new technologies that make undercover investigations easier, and considers the benefits and dangers of these advances.
State-Space Models

In this chapter we extend our discussion of the previous chapter to model dynamical systems with continuous state-spaces. We present statistical formulations to model and analyze noisy trajectories that evolve in a continuous state space whose output is corrupted by noise. In particular, we place special emphasis on linear Gaussian state-space models and, within this context, present Kalman filtering theory. The theory presented herein lends itself to the exploration of tracking algorithms explored in the chapter and in an end-of-chapter project.
Simulation-aided face strain extraction for ML animation systems

We present a volumetric, simulation-based pipeline for the automatic creation of strain-based descriptors from facial performance capture provided as surface meshes. Strain descriptors encode facial poses via length elongation/contraction ratios of curves embedded in the flesh volume. Strains are anatomically motivated, correlate strongly to muscle action, and offer excellent coverage of the pose space. Our proposed framework extracts such descriptors from surface-only performance capture data, by extrapolating this deformation into the flesh volume in a physics-based fashion that respects collisions and filters non-physical capture defects. The result of our system feeds into Machine Learning facial animation tools, as employed in Avatar: The Way of Water.
Development of a Web-Based Dashboard System for Monitoring Study Programme Accreditation Instruments

Accreditation is a form of assessment or evaluation of the quality of education, both at the level of study programme and higher education institutions, conducted by independent organizations or bodies external to the universities. BAN-PT is one such external agency responsible for accrediting higher education institutions in Indonesia. However, many universities encounter challenges during the accreditation process, such as the absence of a monitoring system and data integration in the preparation of some documents, which are essential for accreditation. To address this issue, researchers have recognized the need to design and build a system that can aid in the preparation and monitoring of accreditation forms. The system was developed using the Extreme Programming model, which adopts the agile concept as the core of application development. The Extreme Programming model consists of four key activities, namely system planning, system design, code writing, and system testing. Based on the black box testing results, the developed system was found to operate as intended. Based on the benchmark results for each aspect of the UEQ, Attractiveness has a value of 1.69 with the good category, Perspicuity has a value of 1.57 with the above average category, Efficiency has a value of 2.11 with the excellent category, Dependability has a value of 1.68 with the good category, Stimulation has a value of 1.29 with the above average category, and Novelty has a value of 0.86 with the above average category.
P‐4.4: A Real‐Time Video Rain Removal Algorithm Using Layer Priors

Vision system is widely used in fields such as target detection and security monitoring, but since its heavy dependence on the qualities of input images is in conflict with the fact that rain and other bad weather will cause the quality of input image to decline, the video deraining algorithm is of great importance. Based on the rain streaks removal algorithm using layer priors, this paper proposes a real-time video rain removal algorithm using layer priors, which regards the rain image as the linear superimposition of background layer and raindrops layer, and decomposes them via MAP. During the procession, temporal direction gradient and vertical direction gradient are adopted as explicit priors, while CNN is adopted as implicit prior. After analyzing the experiment results, it could be observed that the proposed algorithm can not only output rain-free images of higher quality, but also greatly reduce the operation time, which meet the real-time requirement to some extent.
Revisit Systematic Generalization via Meaningful Learning

Humans can systematically generalize to novel compositions of existing concepts.Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results.We revisit this controversial topic from the perspective of meaningful learning, an exceptional capability of humans to learn novel concepts by connecting them with known ones.We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts.Our observations suggest that models can successfully one-shot generalize to novel concepts and compositions through semantic linking, either inductively or deductively.We demonstrate that prior knowledge plays a key role as well.In addition to synthetic tests, we further conduct proof-of-concept experiments in machine translation and semantic parsing, showing the benefits of meaningful learning in applications.We hope our positive findings will encourage excavating modern neural networks' potential in systematic generalization through more advanced learning schemes.
Multiple PDAF Algorithm for Estimation States Multiple of the Ships

In order to implement the autonomous navigation function, it is essential to track an object within a certain radius of the ship"s route. This paper proposes the Multiple Probabilistic Data Association Filter (MPDAF), which can track multiple ships by extending Probabilistic Data Association Filter (PDAF), an existing single object tracking algorithm, using radar data obtained from real marine environments. The proposed MPDAF algorithm was developed to address the problem of tracking multiple objects in a complex environment where there can be significant uncertainty in the number and identification of objects to be tracked. Using real-world radar data provided by the German aerospace center (DLR), it has been verified that the proposed algorithm can track a large number of objects with a small position error.
Exploration and Analysis of Digital Transformation Path of Power Grid Enterprises Based on Internet of Things

In order to understand the digital transformation path of power grid enterprises, a research on the exploration and analysis of digital transformation path of power grid enterprises based on the Internet of Things is put forward. Firstly, this paper analyzes the problems encountered in the digital t
Knowledge Graphs and Ontologies

Fully revised and updated, this third edition includes three new chapters on neural networks and deep learning including generative AI, causality, and the social, ethical and regulatory impacts of artificial intelligence. All parts have been updated with the methods that have been proven to work. The book's novel agent design space provides a coherent framework for learning, reasoning and decision making. Numerous realistic applications and examples facilitate student understanding. Every concept or algorithm is presented in pseudocode and open source AIPython code, enabling students to experiment with and build on the implementations. Five larger case studies are developed throughout the book and connect the design approaches to the applications. Each chapter now has a social impact section, enabling students to understand the impact of the various techniques as they learn them. An invaluable teaching package for undergraduate and graduate AI courses, this comprehensive textbook is accompanied by lecture slides, solutions, and code.
Simple smartphone-based artworks diagnostics by Moiré method and a diffractive optical element

The moir´e method is a well-known tool in NDT; it is based on the principle of superposition of two sets of lines or gratings, which creates a moir´e pattern that can reveal surface deformations caused by underlying defects or damage. In this paper, we propose a simple artwork diagnostic using the moir´e method and a smartphone. The technique is based on coupling the acquisition of fringe patterns by the smartphone camera to an effective fringe generator. The fringe generator consists of a diffractive optical element (DOE) illuminated by a laser diode; this optical device proved to be very effective thanks to its ability to produce in a simple way grid patterns of different spatial frequencies. The smartphone camera is used to capture the grid patterns and to store them in the cloud. We demonstrate the proposed approach by giving some preliminary experimental results.
Perancangan Aplikasi Sistem Informasi Pengelolaan Barang Inventaris Berbasis Android

AbstractIn processing office inventory mutation data at the Mesuji Sekampung River Basin Center, the office inventory data management system is still manual in the form of bookkeeping, besides that there is also no computerized system in processing office inventory data so that an application is needed that can facilitate and assist a job. in processing office inventory mutation data. To overcome this problem, one solution to overcome this is to build a system that can facilitate and assist a job in processing office inventory mutation data at the Mesuji Sekampung River Basin Center to handle this problem. Therefore, the author proposes an Android-based Office Inventory Data Processing Information System at the Great Hall of the Mesuji River Basin Sekampung. The results of this study are the information system for processing Inventory Data at the Central Mesuji Sekampung River Basin which has been developed to facilitate sub-sections in the process of processing inventory data as well as collecting and searching inventory data easily and quickly and sub-sections can find out what assets are owned.
Cyberterrorism and Its Impact on Contemporary Conflicts and Society

The chapter discusses the modern possibilities of cyber-terrorism, gives certain examples of its application in conflicts, shows the dangers that may follow cybercrime as an international level crime. Therefore, the need to improve the world policy of fighting it and to strengthen international cooperation is explored. Terrorism, including cyberterrorism, is a global phenomenon that transcends national boundaries and whose scale is immeasurably large. As a result of technological progress, modern, global cyberspace creates a unique environment for cyberterrorism.
Twitter-based gender recognition using transformers

&lt;abstract&gt; &lt;p&gt;Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's gender from their images and tweets. The image-based classification model is trained in two different methods: using the profile image of the user and using various image contents posted by the user on Twitter. For the first method a Twitter gender recognition dataset, publicly available on Kaggle and for the second method the PAN-18 dataset is used. Several transformer models, i.e. vision transformers (ViT), LeViT and Swin Transformer are fine-tuned for both of the image datasets and then compared. Next, different transformer models, namely, bidirectional encoders representations from transformers (BERT), RoBERTa and ELECTRA are fine-tuned to recognize the user's gender by their tweets. This is highly beneficial, because not all users provide an image that indicates their gender. The gender of such users could be detected from their tweets. The significance of the image and text classification models were evaluated using the Mann-Whitney U test. Finally, the combination model improved the accuracy of image and text classification models by 11.73 and 5.26% for the Kaggle dataset and by 8.55 and 9.8% for the PAN-18 dataset, respectively. This shows that the image and text classification models are capable of complementing each other by providing additional information to one another. Our overall multimodal method has an accuracy of 88.11% for the Kaggle and 89.24% for the PAN-18 dataset and outperforms state-of-the-art models. Our work benefits research that critically require user demographic information such as gender to further analyze and study social media content for health-related issues.&lt;/p&gt; &lt;/abstract&gt;