Reviewerly/exHarmony huggingface dataset:
SWYNT: Swin Y-Net Transformers for Deepfake Detection

Nowadays, less technical individuals can create false videos by only source and target images, using deepfakes generation tools and methodologies. Distributing false information on social media and other concerns related to the deepfakes have thus significantly increased. To deal with the challenges posed by incorrect details, efficient Deepfakes detection algorithms must be developed considering the tremendous advancement in deepfakes generating techniques. Existing techniques are not reliable enough to find deepfakes, especially when the videos are made with various deepfakes generation methods. The Swin Y-Net Transformers (SWYNT) architecture we created in this paper can visually discriminate between natural and artificial faces. The architecture uses a Swin transformer, encoder, and decoder in a U -Net architecture with a classification branch to build a model that can classify and segment deepfakes. The segmentation process creates segmentation masks and helps train the classifier. We have evaluated our suggested method using the extensive, standard, and diverse FaceForensics++ (FF++) and the Celeb-DF dataset. The generalizability evaluation of our process, which is part of the performance evaluation, reveals the model's promising performance in identifying deepfakes videos generated using various methodologies on both large-scale datasets.
PROMISES AND PERILS OF AI USE ON THE TERTIARY EDUCATIONAL LEVEL

This article describes possible practical applications of Artificial Intelligence on the tertiary educational level both from the staff and students’ perspectives on the basis of experience gained by universities worldwide. AI usage in higher education field is analyzed on the basis of correlation between participants of educational process and the areas of innovative software application. Advantages of AI use are outlined on institutional, social and instructional levels. AI proves to be a growing and promising tool for facilitating educational processes, having obvious benefits for the education-givers and seekers, including the institutional possibilities of streamline enrollment and improved retention, organizational guidance and support of students on campus as well as educational opportunities such as better recourses management and successful online training process. Additionally, ethical considerations are raised, regarding use of AI, data management and privacy.
A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry–epithermal mineralization in the Duolong ore District, Tibet

Machine learning (ML) is emerging as a highly effective technique for mineral exploration. However, mineral exploration poses several unique challenges to ML application, such as uncertain geological information in remote regions and imbalanced labeled training data. In this study, we developed a deep-learning framework — a self-attention back-propagation neural network (SA-BPNN) — which is used to automatically explore relationships among diverse features and improve the capability of information extraction. Moreover, we proposed a mineral prospectivity modeling workflow involving “quantitative data + ML + expert experience” for porphyry-epithermal deposits. Using quantitative data obtained from hyperspectral remote sensing, geochemistry, and geophysics, we predicted ore-prospecting targets by applying the SVM, SA-BPNN, and U-Net models. Thereafter, we combined the model-based prediction with geological data to delineate the target areas. The model-based prediction by SVM, SA-BPNN, and U-Net occupy 1.73%, 1.40%, and 2.21% of the study area and contain 100%, 100%, and 80% of the known Cu-Au mineralization in the Duolong ore district in Tibet, respectively. The proposed SA-BPNN method, thus, achieved superior performance for mineral prospectivity modeling compared with alternative methods.
Modified orca predation algorithm: developments and perspectives on global optimization and hybrid energy systems

Abstract This paper provides a novel, unique, and improved optimization algorithm called the modified Orca Predation Algorithm (mOPA). The mOPA is based on the original Orca Predation Algorithm (OPA), which combines two enhancing strategies: Lévy flight and opposition-based learning. The mOPA method is proposed to enhance search efficiency and avoid the limitations of the original OPA. This mOPA method sets up to solve the global optimization issues. Additionally, its effectiveness is compared with various well-known metaheuristic methods, and the CEC’20 test suite challenges are used to illustrate how well the mOPA performs. Case analysis demonstrates that the proposed mOPA method outperforms the benchmark regarding computational speed and yields substantially higher performance than other methods. The mOPA is applied to ensure that all load demand is met with high reliability and the lowest energy cost of an isolated hybrid system. The optimal size of this hybrid system is determined through simulation and analysis in order to service a tiny distant location in Egypt while reducing costs. Photovoltaic panels, biomass gasifier, and fuel cell units compose the majority of this hybrid system’s configuration. To confirm the mOPA technique’s superiority, its outcomes have been compared with the original OPA and other well-known metaheuristic algorithms.
An Overview on the Challenges and Limitations Using Cloud Computing in Healthcare Corporations

Technological advances in high throughput platforms for biological systems enable the cost-efficient production of massive amounts of data, leading life science to the Big Data era. The availability of Big Data provides new opportunities and challenges for data analysis. Cloud Computing is ideal for digging with Big Data in omics sciences because it makes data analysis, sharing, access, and storage effective and able to scale when the amount of data increases. However, Cloud Computing presents several issues regarding the security and privacy of data that are particularly important when analyzing patients’ data, such as in personalized medicine. The objective of the present study is to highlight the challenges, security issues, and impediments that restrict the widespread adoption of Cloud Computing in healthcare corporations.
Teachers’ strategies and challenges in teaching 21st century skills: Little common understanding

Faced with a world of accelerating change and rapidly-evolving technology, education systems must provide students with the skills they need to succeed in the 21st century. However, many countries have failed to incorporate the teaching of these skills within their schools. Our study therefore looks to portray teachers' understanding, strategies and obstacles in teaching these skills across Latin American classrooms. To do so, we analyzed the responses to an online survey from 1391 active teachers across 20 countries in the region. This revealed varying understandings of 21st century skills, with little common understanding. Most teachers failed to mention the skills included in the most popular framework (the 4 Cs); those who did reported using the same strategies, regardless of the skill being taught. These strategies included project-based learning, oracy activities, literacy strategies, and teamwork. We conclude that there is little or no common understanding around these skills, nor the best strategies for developing them. Our study helps understand the potential causes preventing the teaching of these skills in the classroom, a problem that extends beyond Latin America.
Image generation models from scene graphs and layouts: A comparative analysis

An image is the abstraction of a thousand words. The meaning and essence of complex topics, ideas, and concepts can be easily and effectively conveyed visually by a single image rather than a lengthy verbal description. It is not only essential to teach computers how to recognize and classify images but also how to generate them. Controlled image generation depicting complex and multiple objects is a challenging task in computer vision despite the significant advancements in generative modeling. Among the core challenges, scene graph-based and scene layout-based image generation is a significant problem in computer vision and requires generative models to reason about object relationships and compositionality. Due to its ease of use, less time cost, and labor needs, image generation/synthesizing models from scene graphs and layouts are proliferating. In the case of a more significant number of scene graphs and layout to image generation models, a unique experimental evaluation methodology is required to evaluate the controlled image generation. To this extent, we, in this work, present a standard methodology to evaluate the performance of scene graph and scene layout-based image generation models. We perform a comparative analysis of image generation models to evaluate image generation models’ complexity from scene graphs and scene layouts. We analyze the different components of these models on Visual Genome and COCO-Stuff datasets. The experimental results show that the scene layout-based image generation outperforms its graph-based counterpart in most quantitative and qualitative evaluations.
Adaptive Consensus Clustering for Multiple K-Means Via Base Results Refining

Consensus clustering, which learns a consensus clustering result from multiple weak base results, has been widely studied. However, conventional consensus clustering methods only focus on the ensemble process while ignoring the quality improvement of the base results, and thus they just use the fixed base results for consensus learning. In this paper, we provide an alternative idea to improve the final consensus clustering performance by considering the base results refining. In our framework, we adaptively refine the base results in the process of the ensemble. In more detail, on one hand, we ensemble multiple K-means results to learn the consensus one by considering the consensus and diversity; on the other hand, we apply the consensus result to design a graph filter to learn a more cluster-friendly embedding for refining the base K-means results. In our framework, the consensus learning and base results refining are integrated into one unified objective function so that these two tasks can be boosted by each other. Then we design an effective iterative algorithm to optimize the carefully designed objective function. The extensive experiments on benchmark data sets demonstrate that the proposed method can outperform both the single clustering and the state-of-the-art consensus clustering methods. The codes of this paper are released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://Doctor-Nobody.github.io/codes/ACMK.zip</uri> .
Joint Network Topology Inference via Structural Fusion Regularization

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component shared among multiple graphs. However, in practice, a more intricate topological pattern, comprising simultaneously of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homogeneous</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> components, would exhibit in multiple graphs. In this paper, we propose a general graph estimator based on a novel structural fusion regularization that enables us to jointly learn multiple graphs with such complex topological patterns, and enjoys rigorous theoretical guarantees. Specifically, in the proposed regularization term, the structural similarity among graphs is characterized by a Gram matrix, which enables us to flexibly model different types of network structural similarities through different Gram matrix choices. Algorithmically, the regularization term, coupling the parameters together, makes the formulated optimization problem intractable, and thus, we develop an implementable algorithm based on the alternating direction method of multipliers (ADMM) to solve it. Theoretically, non-asymptotic statistical analysis is provided, which precisely characterizes the minimum sample size required for the consistency of the graph estimator. This analysis also provides high-probability bounds on the estimation error as a function of graph structural similarities and other key problem parameters. Finally, the superior performance of the proposed method is demonstrated through simulated and real data examples.
Capacity-Raising Reversible Data Hiding Using Empirical Plus–Minus One in Dual Images

Electronic records of a patient’s health history are often shared among healthcare providers, and patient data must be kept secure to maintain the privacy of patients. One way of doing this is through data hiding, and this paper demonstrates a scheme to achieve this. This paper proposes a capacity-raising reversible data-hiding scheme using an empirical rules table in dual images. The aim of this research is to avoid drawing awareness to the transmission of information by providing a steganographic technique capable of embedding high-capacity data into an image while maintaining the good quality of the image. To hide the secret message(s), a rules table containing 13 entries is presented. This rules table is extendable to a table of up to 262,133 entries (with each entry containing one distinct character) that are related to the 13 entries in terms of the rules. The rules of this table are used during the embedding and extraction procedures. In the proposed method, 512 × 512 images are divided into 1 × 2 blocks where adjacent pixels are represented using x and y for both embedding and extraction, respectively. Recovery of the cover image from the stego image is also achievable during the extraction process. Conducted experiments show that the proposed method has an average pixel-to-signal noise ratio of 52.65 dB, which is higher than that achieved with the methods discussed in this paper. Additionally, the proposed method can embed a wider range of characters (depending on the image size) as compared to the rest of the methods, hence its high embedding capacity of 4.25 bpp. The proposed method can also withstand security attacks such as RS, pixel value difference, entropy, and chi-square attacks. The proposed method is also undetectable under visual attack analyses such as the difference histogram, pixel difference histogram, and visual inspection. Based on the higher embedding capacity, pixel-to-signal noise ratio, the ability of this method to be undetected under visual attack analysis, and the ability of this method to withstand security attacks, it can be concluded that the proposed method is superior to the other methods.
An Evolutionary Game Theoretic Analysis of Cybersecurity Investment Strategies for Smart-Home Users against Cyberattacks

In the digital era, smart-home users face growing threats from cyberattacks that threaten their privacy and security. Hence, it is essential for smart-home users to prioritize cybersecurity education and training to secure their homes. Despite this, the high cost of such training often presents a barrier to widespread adoption and accessibility. This study aims to analyze the costs and benefits associated with various cybersecurity investment strategies for smart-home users in the context of cyberattacks. The study utilizes evolutionary game theory to model a game comprised of three populations: smart-home users, stakeholders, and attackers. We derive and analyze the replicator dynamics of this game to determine the evolutionarily stable strategy (ESS). Furthermore, we investigate the impacts of the costs and benefits of cybersecurity investment and cyberattack costs on the ESS. The findings indicate that incurring costs for cybersecurity training is beneficial for smart-home users to protect their homes and families. However, the training costs must be low and affordable for smart-home users in order to ensure their participation and engagement. Additionally, providing rewards for commitment to cybersecurity is crucial in sustaining interest and investment over the long term. To promote cybersecurity awareness and training for smart-home users, governments can incorporate it as a priority in national cybersecurity plans, provide subsidies for training costs, and incentivize good cybersecurity practices.
Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

This study is concerned with path planning in a structured greenhouse, in contrast to much of the previous research addressing applications in outdoor fields. The prototype mainly comprises an independently driven Mecanum wheel, a lidar measuring module, a single-chip microcomputer control board, and a laptop computer. Environmental information collection and mapping were completed on the basis of lidar and laptop computer connection. The path planning algorithm used in this paper expanded the 8-search-neighborhood of the traditional A* algorithm to a 48-search-neighborhood, increasing the search direction and improving the efficiency of path planning. The Floyd algorithm was integrated to smooth the planned path and reduced the turning points in the path. In this way, the problems of the traditional A* algorithm could be solved (i.e., slow the path planning speed and high numbers of redundant points). Tests showed that the turning points, planning path time, and distance of the improved algorithm were the lowest. Compared with the traditional 8-search-neighborhood A* algorithm, the turning point was reduced by 50%, the planning time was reduced by 13.53%, and the planning distance was reduced by 13.96%. Therefore, the improved method of the A* algorithm proposed in this paper improves the precision of the planning path and reduces the planning time, providing a theoretical basis for the navigation, inspection, and standardization construction of greenhouses in the future.
Smart Automotive Diagnostic and Performance Analysis Using Blockchain Technology

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in different environments. Furthermore, we discuss many aspects of the benefits to different parties, such as the vehicle’s owner and manufacturers. Furthermore, a performance evaluation via simulation was performed on the proposed system using MATLAB Simulink to simulate both the vehicles and Blockchain and give a prototype for the system’s structure. In addition, OMNET++ was used to measure the expected system’s storage and throughput given some fixed parameters, such as sending the periodicity and speed. The simulation results showed that the throughput, end-to-end delay, and power consumption increased as the number of vehicles increased. In general, Original Equipment Manufacturers (OEMs) can implement this system by taking into consideration either increasing the storage to add more vehicles or decreasing the sending frequency to allow more vehicles to join. By and large, the proposed system is fully dynamic, and its configuration can be adjusted to satisfy the OEM’s needs since there are no specific constraints while implementing it.
Terrain Self-Similarity-Based Transformer for Generating Super Resolution DEMs

High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques are currently low-cost and feasible methods used for obtaining high-resolution DEMs. Single-image super-resolution (SISR) techniques have become popular in DEM SR in recent years. However, DEM super-resolution has not yet utilized reference-based image super-resolution (RefSR) techniques. In this paper, we propose a terrain self-similarity-based transformer (SSTrans) to generate super-resolution DEMs. It is a reference-based image super-resolution method that automatically acquires reference images using terrain self-similarity. To verify the proposed model, we conducted experiments on four distinct types of terrain and compared them to the results from the bicubic, SRGAN, and SRCNN approaches. The experimental results show that the SSTrans method performs well in all four terrains and has outstanding advantages in complex and uneven surface terrains.
Against ‘Hate Speech’

ABSTRACT This article argues against the term and concept of ‘hate speech’ and in favour of using the concept and term ‘discriminatory speech’. ‘Hate speech’ is a misnomer; we should name the harmful speech in question by what it in fact does: it discriminates. The article argues for this conceptual replacement claim by identifying a number of functions the concept ‘hate speech’ has been meant to serve and by arguing that extant concepts of hate speech have not served this function well. Roughly, they do not serve the functions well because of five properties of hate speech: hate centricity, perpetrator centricity, intention centricity, emotion centricity, and individual centricity. The article then proposes a definition of discriminatory speech and argues that it fulfils the conceptual functions better.
Self-information of radicals: A new clue for zero-shot Chinese character recognition

Zero-shot Chinese character recognition (ZSCCR) is an important research topic in Chinese character recognition as it attempts to recognize unseen Chinese characters. As basic components and mid-level representations, radicals are significant for ZSCCR. However, previous methods treat the importance of radicals equally, ignoring the different contributions of radicals in distinguishing characters. In this paper, we propose the self-information of radicals (SIR) to measure the importance of radicals in recognizing Chinese characters. The proposed SIR can be easily adopted by two commonly used radical-based ZSCCR frameworks, i.e., sequence matching based and attribute embedding based. For sequence matching based ZSCCR, we propose a novel Chinese character uncertainty elimination (CUE) framework to alleviate the radical sequence mismatch problem. For attribute embedding based ZSCCR, we propose a novel radical information embedding (RIE) method that can highlight the importance of indispensable radicals and weaken the influence of some unnecessary radicals. We conducted comprehensive experiments on the CASIA-HWDB, ICDAR2013, CTW datasets, and AHCDB datasets to evaluate the proposed method. Experiments show that our proposed methods can achieve superior performance to the state-of-the-art methods, which demonstrate the effectiveness and the high extensibility of the proposed SIR.