Reviewerly/exHarmony huggingface dataset:
Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images

Feature extraction in Copy-Move Forgery Detection (CMFD) is crucial to facilitate image forgery analysis.Edge detection is one of the processes to extract specific information from Copy-Move Forgery (CMF) Images.It sensitizes the amount of information in the image and filters out useless ones while preserving the important structural properties in the image.This paper compares five edge detection methods: Robert, Sobel, Prewitt (first Derivative), Laplacian, and Canny edge detectors (second Derivatives).CMFD evaluation datasets images (MICC-F220) are tested with both methods to facilitate comparison.The edge detection operators were implemented with their respective convolution masks.Robert with a 2x2 mask, The Prewitt and Sobel with a 3x3 mask, while Laplacian and canny used adjustable masks.These masks determine the quality of the detected edges.Edges reflect a great-intensity contrast that is either darker or brighter.
Making heterogeneous smart home data interoperable with the SAREF ontology

SAREF is an ontology created to enable interoperability between smart devices, but there is a lack in the literature of practical examples to implement SAREF in real applications. We validate the practical implementation of SAREF through two approaches. We first examine two methods to map the IoT data available in a smart home into linked data using SAREF: (1) by creating a template-based mapping to describe how SAREF can be used and (2) by using a mapping language to demonstrate it can be simple to map, while still using SAREF. The second approach demonstrates the communication capabilities of IoT devices when they share knowledge represented using SAREF and describes how SAREF enables interoperability between different devices. The two approaches demonstrate that all the information from various data sets of smart devices can successfully be transformed into the SAREF ontology and how SAREF can be applied in a concrete interoperability framework.
SafeAMC: Adversarial training for robust modulation classification models

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial pertur-bations, namely imperceptible additive noise crafted to induce misclassification. This raises questions about the security but also about the general trust in model predictions. We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation classification (AMC) models. We show that current state-of-the-art models can effectively benefit from adversarial training, which mitigates the robustness issues for some families of modulations. We use adversarial perturbations to visualize the learned features, and we found that the signal symbols are shifted towards the nearest classes in constellation space, like maximum likelihood methods when adversarial training is enabled. This confirms that robust models are not only more secure, but also more interpretable, building their decisions on signal statistics that are actually relevant to modulation classification.
FLYFDetect: A Smart Home Privacy Protection Framework via Federated Learning

With the advent of the Internet of Things (IoT), smart home devices and applications have become more widespread, and the privacy issues of smart homes have increasingly become more important. At present, the research on privacy protection technologies for smart home is to localize data as much as possible to reduce the outflow of users’ data. However, the data cannot be processed completely locally, and the private data of smart home devices that require intelligent services will still leak to the cloud. Therefore, we propose a smart home privacy protection communication protocol PPTrans based on federated learning and design a smart home privacy protection framework FLYFDetect that supports PPTrans, which implements a federated learning method based on YOLOv5, and verified the effectiveness of FLYFDetect in a real environment combined with a smart home flame warning application.
Cyber Social Engineering Kill Chain

Cyber attacks are often initiated with a social engineering attack to penetrate a network, which we call Cyber Social Engineering (CSE) attacks. Despite many studies, our understanding of CSE attacks is inadequate in explaining why these attacks are prevalent and why humans are still the weakest link in cybersecurity. This paper aims to deepen our understanding of CSE attacks and help design effective defenses against them. Specifically, we propose a framework, dubbed CSE Kill Chain, for systematically modeling and characterizing CSE attacks. To demonstrate the usefulness of the framework, we perform a case study in which we apply it to analyze a real-world CSE attack.
Diagrammatic Modelling Tools for Grounded Theory Research: The Implementation of a Multi-Representational Approach

Grounded Theory (GT) researchers have an ever-expanding palette of digital tools available to further analyse complex phenomena with interrelated data sets. However, few GT researchers have systematically examined how the use of diagramming tools can enhance analysis. To advance the analytical process of GT, this study develops a multi-representational approach that integrates with research design. After diagrams supportive of GT are identified for their potential improvements to the analytical process, the research focuses on the experience of employing three diagramming tools (Flourish, Observable, and Pajek) in developing two complementary diagrams (Network and Arc diagrams). The use of these tools for analysis is explained in detail for conducting extensive constructivist GT study; illustrated via a case study examining a century of innovation in hospital design. Via this multiple-source case study, this paper demonstrates how the sagacious deployment of diagramming tools, when carefully aligned to research objectives, can complement GT analysis by facilitating systematic thinking and holistic interpretation of hidden patterns.
The Investigation of Student Engagement as Mediator in ICT Readiness and Experience on E-Learning Effectiveness in Post-Pandemic Recovery

E-learning is an asynchronous or synchronous communication mode to confirm knowledge through electronic media. The implementation of e-learning in Indonesia has many challenges, including the low independent learning culture, which creates the perception that the absence of direct interaction between lecturers and students and e -learning does not always enhance students' abilities to study independently. The effectiveness of e-learning needs to assess to assist e-learning providers with product customization so that e-learning matches the needs and expectations of users. This study analyzes the effect of information and communication technology (ICT) readiness and experience on the effectiveness of e-learning with student engagement as a mediator. This research used Structural Equation Modeling Partial Least Square (SEM-PLS) to test the hypothesis. The results of this study are the mediation of emotional engagement on readiness, cognitive engagement on information seeking, cognitive engagement on socialization and behavior engagement on readiness and socialization have a significant effect on e-learning effectivity.
NEW TECHNOLOGIES AND THEIR IMPACT ON FOREIGN LANGUAGE TEACHER PROFESSIONAL BURNOUT

Because of the dynamic and rapid development of digital technologies, teachers are facing more than ever challenging issues related to coping with educational innovations. This situation has become ultimately evident under COVID-19 quarantine and the necessity to work remotely. Such requirements induce extra pressure on teachers, which causes excessive stress, often leading to emotional and professional burnout. In our paper, we aim to focus on the types of impact (positive or negative) of digital technologies in relation to foreign language teachers’ age and experience and the level of professional burnout. Specific emphasis is put on the study of coronavirus-related remote learning mode circumstances. To reach the research goal, a mixed method was used, engaging 140 English language teachers from 18 Polish schools, Silesia region, with a questionnaire and a semi-structured interview as the tools used in the quantitative and qualitative stages of the study. The research findings show that even though teachers are ready to integrate technologies in the classroom, they are vulnerable to technical problems which often occur during the lessons, causing the feeling of disappointment, frustration, anxiety, and lowering the efficiency of teachers’ professional performance. The implications of the study suggest that the professional support for teachers in technology use, better working conditions, and equipping language classrooms with good quality digital devices should serve as the best remedy against teacher burnout.
The Effects of Augmented Reality Applications on Secondary Students&amp;#039; Academic Achievement in Science Course

The aim of this study is to investigate how the use of augmented reality (AR) applications in educational environments affects students’ achievement levels. The study was carried out through two dimensions; a quantitative and a qualitative aspect. The study was conducted with 7th-grade students in a public secondary school in Turkey as part of a science course. For this purpose, AR applications and activities were implemented that were suitable for the topic of Astronomy. The quasi-experimental model and one of the quantitative research methods were used for the study. As data collection instruments an achievement test and an interview form were made use of. AR activities were used for teaching in the experimental group, while the traditional method for the control group. The quantitative results of the study showed that there was a significant difference in the achievement level of learners in favor of the experimental group. The interviews with the students revealed that the AR applications used in the course were interesting, that they increased interest, desire, and motivation in class, and that they facilitated learning. However, students indicated that the cost of the AR materials was high, that they had difficulty in obtaining the materials, and that they sometimes had technical problems. Another limitation was expressed as that the excess the number of students makes it more difficult to take the advantage of AR activities. Students suggested that lowering the cost of AR materials and using them in other lessons can be more effective in learning environment.
Ensemble Generative Adversarial Imputation Network with Selective Multi-Generator (ESM-GAIN) for Missing Data Imputation

As a pervasive issue, missing data may influence the data modeling performance and lead to more difficulties of completing the desired tasks. Many approaches have been developed for missing data imputation. Recently, by taking advantage of the emerging generative adversarial network (GAN), an effective missing data imputation approach termed generative adversarial imputation nets (GAIN) was developed. However, its modeling architecture may still lead to significant imputation bias. In addition, with the GAN structure, the training process of GAIN may be instable and the imputation variation may be high. Hence, to address these two limitations, the ensemble GAIN with selective multi-generator (ESM-GAIN) is proposed to improve the imputation accuracy and robustness. The contributions of the proposed ESM-GAIN consist of two aspects: (1) a selective multi-generation framework is proposed to identify high-quality imputations; (2) an ensemble learning framework is incorporated for GAIN imputation to improve the imputation robustness. The effectiveness of the proposed ESM-GAIN is validated by both numerical simulation and two real-world breast cancer datasets.
Wandering with Rancière: Sound and Structure under the Aesthetic Regime

Drawing on Hegel and Foucault, Rimbaud and Wordsworth, Martin Kaltenecker engages conventional historical reading of the progression of Western music. Beyond this important context, Kaltenecker draws attention to a recent, noteworthy development: a certain tendency of recent music to devote itself to its own medium, to the materiality of sound devoid of any structuring principle.
DESIGN GUIDELINES FOR MOBILE APPLICATIONS

With the widespread rapid development of computers and wireless communication, mobile computing is fast growing asthe communication and sales platform of choice. The aim of this paper is to present design guidelines for mobileapplications. The
Graph-Based Image Segmentation with Shape Priors and Band Constraints

In this work, we describe an efficient algorithm, with proof of correctness, for finding an optimal binary segmentation of an image such that the indicated object satisfies a novel high-level prior, called the Band constraint (B), which is the extension of a recent shape prior, called Local Band constraint (LB), to its limiting case with radius tending to infinity. Unlike the LB constraint, the new algorithm can be applied directly to the original image graph saving memory. In our theoretical investigations, we discuss the theoretical relationship of the new B constraint with the Boundary Band (BB) constraint, formerly known as Geodesic Band constraint. Finally, we experimentally conduct a template rotation invariance study of the B constraint within the Oriented Image Foresting Transform framework in region adjacency graphs, when applied to natural images with templates by Gielis geometric equation.
Automotive Embedded Image Classification Systems

Electric vehicles are becoming more autonomous, so they must classify images using embedded systems and advanced classification methodologies to achieve a fast response when navigating. Thus, studying and analyzing classification algorithms and embedded systems is a mandatory endeavor to improve the performance of electric vehicles during their operation. On the other hand, artificial intelligence is one of the leading technology topics in autonomous electric vehicles; however, the computational requirements to analyze a large amount of data in real-time would mean having costly and powerful computers on board. Also, this can mean using a significant physical space in the vehicle and energy resources. An embedded system can handle the necessary data to classify standard traffic signals on the road so the principal processor can be released from these tasks. This paper proposes a traffic signal object detector and classifier that is implemented using a Tiny YOLOv4 and compares Frames Per Second obtained in an embedded system using the trained model, a web camera, and a Hardware Accelerator called Movidius Neural Stick by Intel are integrated into the proposed solution. The results show that the proposal is a good alternative for implementing a specialized image classification system into an embedded digital system for electric vehicles. This proposal could be extended to classify more images that can show up on a conventional road.
Blind Watermarking for Hiding Color Images in Color Images with Super-Resolution Enhancement

This paper presents a novel approach for directly hiding the pixel values of a small color watermark in a carrier color image. Watermark embedding is achieved by modulating the gap of paired coefficient magnitudes in the discrete cosine transform domain according to the intended pixel value, and watermark extraction is the process of regaining and regulating the gap distance back to the intensity value. In a comparison study of robustness against commonly encountered attacks, the proposed scheme outperformed seven watermarking schemes in terms of zero-normalized cross-correlation (ZNCC). To render a better visual rendition of the recovered color watermark, a generative adversarial network (GAN) was introduced to perform image denoising and super-resolution reconstruction. Except for JPEG compression attacks, the proposed scheme generally resulted in ZNCCs higher than 0.65. The employed GAN contributed to a noticeable improvement in perceptual quality, which is also manifested as high-level ZNCCs of no less than 0.78.
Interactive Storytelling for Immersive Media, Augmented Manufacturing, and Digital Healthcare

The article explores interactive storytelling in augmented and virtual reality. It is based on the research project Immersive Media Lab, bridges the gap between technological developments and their application, and aims to combine art, economy, and science. Due to their increased availability, virtual and immersive technologies have not only seen a resurgence in their popularity but also offer intriguing new perspectives regarding their use in different contexts. Consequently, the notions of interactivity and immersion also play significant roles in the study of storytelling in conjunction with virtual and augmented reality. The Immersive Media Lab has employed state-of-the-art AR/VR technology in five use cases: VR Interaction, Artistic Motion Tracking in AR/VR, Audio AR for Industry, AR/VR Interfaces for Industry, and AR Education for Patients. All of them, but one, developed prototypical applications related to the central concept of interactive storytelling. Furthermore, each use case addresses three distinct dimensions of storytelling: the production of a coherent story, the collection and transfer of knowledge, and the specificity of the technology used. Covering the areas of Creative Media, Smart Manufacturing, and Healthcare leads to a particular research design, as each area is connected to different research contexts and comes with appliances of immersive media. The article focuses on various theoretical inputs regarding interactive and immersive storytelling in general and relates them to the academic background of the five use cases. Furthermore, each use case defined a story in its context, the purpose of its narration, and its media-specific context. Finally, the paper reflects on the outcomes of the use cases and outlines their potential for future applications of interactive storytelling in immersive media.
A Systematic Literature Review on Smart Waste Management Using Machine Learning

Waste management is a difficult problem for both developed and developing countries. One of the biggest problems is that trash cans in public areas often overflow long before the next scheduled cleaning. Waste management causes high levels of gases, insects, and houseflies, which can cause serious health problems. This study presents a systematic literature review on smart waste management using machine learning. With machine learning, the system may enhance waste disposal using the quickest route. The ML-IoT-based design incorporates equipment to determine the weight of the waste, which adjusts to the network environment and contains information regarding waste management. Comparing the studies are meant to give the reader a complete picture of the smart waste management domain.
Network Security Situation Assessment Method Based on Absorbing Markov Chain

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation.
Ukrainian Political Internet Discourse: Signs of Language Aggression

The problem of language aggression, hostility, and contempt is one of the most acute in the modern world. The rapid spread of this phenomenon is associated with the development of the Internet and social networks, which led to revolutionary changes in people's communication and the emergence of new types of socio-political interaction. The accessibility of internet resources, the absence of communication barriers that are present in real life, and the speed of information transmission have greatly expanded social contacts. The growth of language aggression, hostility, and contempt in society and the resulting need to invent and implement models of nonviolent language interaction have created new areas for research for Ukrainian linguists. The purpose of the study is to define such concepts as language aggression, hostility, and contempt; to identify the dominant areas of language aggression, hostility, and contempt in the Ukrainian political internet discourse. Language aggression, hostility, and contempt are aimed at humiliating other people or a group of people. The most frequent stereotypes of aggression are associated with discrediting based on political views, ethnicity, language views, less often – gender, age, religion, social status, etc.
ANALISIS SPASIAL DAERAH RAWAN BANJIR DI DAS WAE HERU, KOTA AMBON

The intensity of the high rainfall resulted in the overflow of the Wae Heru watershed, which resulted in a catastrophic flood that hit residential areas around the watershed. This study aimed to analyze spatially the level of flood hazard and its exposure to settlements in the Wae Heru watershed, Ambon City. This research used overlay technique, scoring method and weighting using ArcMap 10.8 software as a Geographic Information System (GIS) based approach. The variables used included rainfall, elevation, slope, soil type, land cover and distance from the river. The results showed that the flood hazard level was dominated by a low hazard level of 361.28 ha and the flood exposure level in settlements was dominated by a high hazard area of 54.68 ha. The results of the study are expected to be useful in flood mitigation efforts to minimize losses, both fatalities and physical damage.
Inverse Conversion of Transition Matrices Method for Polygeneration Microgrid Dynamic Electricity Cost Prediction

This article is devoted to the application of the inverse transformation (deployment) method of the matrix of empirical transition probabilities to detailed solutions for predicting the energy states of power generating components of a combined power supply system in local facilities with renewable energy sources. It is known that inverse problems are so-called incorrect problems. The one-level and two-level algorithms of the method of inverse transformation of the matrix of empirical probabilities of transitions to detailed solutions are considered. Through modeling, it is shown the forecasting of the level of electricity generation given by the load profile by several sources (traditional and renewable) at the specified forecasting interval to form the influence of the control system or the user himself on the electricity consumption mode of the local object. In addition, a system of criteria is considered, which is based on a conditional dynamic tariff and the ratio of installed capacities of renewable sources of electricity, to ensure the balance of the microgrid system with polygeneration.