Reviewerly/exHarmony huggingface dataset:
A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain.

<ns3:p>Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new vulnerabilities regarding security and privacy. Moreover, trust issues in such an untrustworthy environment need an effective trust management solution and incentive mechanisms to improve overall security. This will increase the computation offloading success rate and the vehicles' willingness to share their resources. Particularly given the high transportability and heterogeneity of vehicular networks, the conventional security and trust management methods are inadequate. Blockchain, the rapidly emerging trend technology, is a unique solution that can help overcome security and privacy issues and meet trust management and incentive mechanism goals. Blockchain’s immutable distributed ledger, traceability, consensus validation system and smart contract features can improve vehicular network security. Although most research is focused on enhancing the performance of computation offloading algorithms, blockchain security solutions in computation offloading scenarios are not fully discussed. Thus, security and trust issues related to computation offloading in VEC environments need more consideration since supporting the new complex vehicular applications is essential. Therefore, this paper provides a review of recent surveys and studies, an overview of VEC, computation offloading and blockchain, in addition to discussing security, privacy and trust in vehicular networks and computation offloading while considering blockchain as a distributed security solution. We propose a new paradigm called blockchain edge of vehicle (BEoV) at the end, which enables several blockchain-based security services for vehicular computation offloading in particular.</ns3:p>
Exploring the Role of Educational Digitalization in Advancing Inclusivity within Universities

This research examines how the integration of digital tools in higher education can play a role in advancing social inclusion. The study employs quantitative methodologies. To establish connections among participants, descriptive analysis and cluster grouping techniques were utilized. The survey collected data from 516 undergraduate students across diverse universities in Peru, encompassing information about age, educational level, and gender. The outcomes reveal distinct clusters: Cluster 1 (chi-sqr = 29.78; p .001, max = 5.1), Cluster 2 (chi-sqr = 99.6; p .001), and Cluster 3 (chi-sqr = 13.1; p = .001). The results suggest that integrating digital tools into education can significantly contribute to enhancing social inclusion within higher education environments. Nevertheless, the degree to which digitalization fosters inclusivity is impacted by factors such as the quality of digital infrastructure, the digital proficiency of students and educators, and the chosen educational approach
Research on differential privacy protection method based on user tendency

It is a new attack model to mine user’s activity rule from user’s massive data. In order to solve the privacy leakage problem caused by user tendency in current privacy preserving methods, an extended differential privacy preserving method based on user’s tendency is proposed in the paper. By constructing a Markov chain, and using the Markov decision process, it equivalently expresses user’s tendency as measurable state transition probability, which can transform qualitative descriptions of user’s tendency into a quantitative representation to achieve an accurate measurement of the user tendency. An extended (P,ε)-differential privacy protection method is proposed in the work, by introducing a privacy model parameter R, it combines the quantified user’s propensity probability with a differential privacy budget parameter, and it can dynamically add different noise amounts according to the user’s tendency, so as to achieve the purpose of protecting the user’s propensity privacy information and improve data availability. Finally, the feasibility and effectiveness of the proposed method was verified by experiments.
Disentangling the latent space of GANs for semantic face editing

Disentanglement research is a critical and important issue in the field of image editing. In order to perform disentangled editing on images generated by generative models, this paper presents an unsupervised, model-agnostic, two-stage trained editing framework. This work addresses the problem of discovering interpretable, disentangled directions of edited image attributes in the latent space of generative models. This effort’s primary objective was to address the limitations discovered in previous research, mainly (a) the discovered editing directions are interpretable but significantly entangled, i.e., changes to one attribute affect the others and (b) Prior research has utilized direction discovery and direction disentanglement separately, and they can’t work synergistically. More specifically, this paper proposes a two-stage training method that discovers the editing direction with semantics, perturbs the dimension of the direction vector, adjusts it with a penalty mechanism, and makes the editing direction more disentangled. This allows easy distinguishable image editing, such as age and facial expressions in facial images. Experimentally compared to other methods, the proposed method outperforms them both qualitatively and quantitatively in terms of interpretability, disentanglement, and distinguishability of the generated images. The implementation of our method is available at https://github.com/ydniuyongjie/twoStageForFaceEdit .
Boosting Brands with Augmented Reality: Why and When it Works

Abstract Augmented reality (AR) is gaining recognition among executives as a powerful marketing tool. Unlike virtual reality, AR integrates virtual content into the real world, providing consumers with interactive experiences. AR applications like IKEA Place and Dulux Visualizer allow users to visualize furniture or paint colors in their homes, while AR games like Pokémon Go generate new revenue streams. The increasing availability of AR capabilities in smartphones and the creation of efficient developer tools have contributed to ist breakthrough. AR offers various levels of local presence, ranging from simple overlays to highly immersive mixed-reality experiences. The potential of AR extends beyond virtual reality, as it can create a new form of integrated three-dimensional Internet. AR marketing can enhance brand evaluations and emotional consumer-brand relationships, particularly through inspiration and closeness. Companies should consider AR as a tool for branding, as it has shown positive effects on brand attitudes, product evaluations, purchase intentions and word-of-mouth. Managers are encouraged to experiment with AR to understand ist potential and stay competitive in the evolving digital landscape.
Maximum-Margin Nearest Prototype Classifiers with the Sum-over-Others Loss Function and a Performance Evaluation

This paper investigates margin-maximization models for nearest prototype classifiers. These models are formulated through a minimization problem, which is a weighted sum of the inverted margin and a loss function. It is reduced a difference-of-convex optimization problem, and solved using the convex-concave procedure. In our latest study, to overcome limitations of the previous model, we have revised the model in both of the optimization problem and the training algorithm. In this paper, we propose another revised margin-maximization model by replacing the max-over-others loss function used in the latest study with the sum-over-others loss function. We provide a derivation of the training algorithm of the proposed model. Moreover, we evaluate classification performance of the revised margin-maximization models through a numerical experiment using benchmark data sets of UCI Machine Learning Repository. We compare the performance of our models not only with the previous model but also with baseline methods that are the generalized learning quantization, the class-wise k-means, and the support vector machine.
Enhanced ant colony algorithm with obstacle avoidance strategy for multi-objective path planning of mobile robots

AbstractPath planning with multiple evaluation metrics makes the motion of a robot realistic, but the contradiction between the metrics and the lack of global search and obstacle avoidance capabilities increases the difficulty of obtaining the optimization solution. To solve these problems, an enhanced ant colony algorithm (EACA) with an obstacle avoidance strategy is proposed in this article. First, the path planning model is constructed, and strict movement rules are designed. Secondly, the EACA with global search, balancing the contradiction between metrics, is designed. The dynamic regulation of pheromone concentration and the mechanism of fluctuating pheromone distribution are explored, heuristic information is optimized and the path planning effect is enhanced. Finally, a new mechanism of away-from-obstacles is proposed as the obstacle avoidance strategy, which ensures a reasonable safe distance. Comparative simulations on several different maps validate the performance of EACA with the obstacle avoidance strategy for planning robot movement paths.KEYWORDS: Enhanced ant colony algorithmobstacle avoidance strategypath planningmulti-objectiveoptimal AcknowledgementsThe authors are deeply grateful to the anonymous reviewers for their valuable comments and suggestions, which greatly enhanced the quality of this article.Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementAll data generated or analysed during this study are included in this published article.
Application of virtual reality in simulated training for arthroscopic surgeries: A systematic literature review

INTRODUCTION: In recent years, technology has provided significant contributions to the health area with innovative projects such as artificial intelligence, virtual reality, augmented reality, etc.&#x0D; OBJECTIVE: This review aims to review studies that propose solutions with virtual reality (VR) technologies for the training of surgeons in the arthroscopic surgery procedure.&#x0D; Method: We identified 352 articles from the Scopus database, from 2017 to the present, of which 31 studies were retrieved by a screening procedure based on the principles of the PRISMA declaration for the study of this systematic literature review (RSL).&#x0D; Results: The implementation of virtual reality in surgical simulations has significantly reduced the percentage of risk in the execution of arthroscopic surgeries.&#x0D; Conclusions: The virtual reality implemented in the simulations of arthroscopic surgeries stands out and represents a promising tool that will allow improvements with respect to the formation of skills and the anticipation of possible events. However, it is necessary to address certain limitations, such as the accuracy and realism of simulations, as well as the implementation of haptic or perceptual systems to improve tactile or visual feedback during simulation.
LIMBAH ANORGANIK SEBAGAI IDE PENCIPTAAN SENI KRIYA SMA WAHYU MAKASSAR

This research utilizes inorganic waste, namely used plastic that is no longer used, it is hoped that in the future the art of craft from anorgnaik waste can reduce the amount of waste that is an obstacle at this time. The purpose of this study was to determine the planning, implementation and results of making decorative lamps from inorganic waste in Class X Students of SMA Wahyu Makassar. Collection techniques through observation, interviews and documentation. The results of the study provide results where Class X students of SMA Wahyu Makassar are able to know the materials and tools to be used and are able to work on decorative lamps in accordance with the design design that has been designed. The results of the decorative lamps produced are very good and have high artistic value so that they can be used as displays at home or anywhere. The results of interviews with both teachers and students provide answers that the art craft learning process starts from good planning and students can work on artwork well and enthusiastically.
AI-Driven Hospital Readmission Predictor for Diabetic Patients

In smart healthcare delivery, predictive methods have been proven to be revolutionary in present time, especially amidst several critical health issues and pandemics. Healthcare is the most dynamic field wherein the demand of prediction is very high as it can offer lifesaving solutions and precautionary measures to patients who are particularly suffering from chronic ailments. This chapter deals with predicting hospital readmission rate of diabetic patients. Hospital readmission is the procedure in which a former patient is readmitted after discharge because of a new diagnosis or due to negative changes in the old diagnosis, based on historical medical record. The analysis would cover and benefit patients whose symptoms seem to be indicating toward a possibility of diabetes in the future. Our work would also benefit the medical world by pointing out the medical factors which may have vital impact on diabetes. The results would also support the initiative by the Centers for Medicare and Medicaid Services and assist in hospital readmission reduction.
Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback

User relevance feedback (URF) is emerging as an important component of the multimedia analytics toolbox. State-of-the-art URF systems employ high-dimensional vectors of semantic features and train linear-SVM classifiers in each round of interaction. In a round, they present the user with the most confident media items, which lie furthest from the SVM plane. Due to the scale of current media collections, URF systems must be supported by a high-dimensional index. Usually, these indexes are designed for nearest-neighbour point queries, and it is not known how well they support the URF process. In this paper, we study the performance of four state-of-the-art high-dimensional indexes in the URF context. We analyse the quality of query results, compared to a sequential analysis of the collection, over a range of classifiers, showing that result quality depends (i) heavily on the quality of the SVM classifier and (ii) the index structure itself. We also consider a search-oriented workload, where the goal is to find the first relevant item for a task. The results show that the indexes perform similarly overall, despite differences in their paths to the solution. Interestingly, worse recall can lead to better application-specific performance.
Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models

Recently, digital humans for interpersonal interaction in virtual environments have gained significant attention. In this paper, we introduce a novel multi-dancer synthesis task called partner dancer generation, which involves synthesizing virtual human dancers capable of performing dance with users. The task aims to control the pose diversity between the lead dancer and the partner dancer. The core of this task is to ensure the controllable diversity of the generated partner dancer while maintaining temporal coordination with the lead dancer. This scenario varies from earlier research in generating dance motions driven by music, as our emphasis is on automatically designing partner dancer postures according to pre-defined diversity, the pose of lead dancer, as well as the accompanying tunes. To achieve this objective, we propose a three-stage framework called Dance-with-You (DanY). Initially, we employ a 3D Pose Collection stage to collect a wide range of basic dance poses as references for motion generation. Then, we introduce a hyper-parameter that coordinates the similarity between dancers by masking poses to prevent the generation of sequences that are over-diverse or consistent. To avoid the rigidity of movements, we design a Dance Pre-generated stage to pre-generate these masked poses instead of filling them with zeros. After that, a Dance Motion Transfer stage is adopted with leader sequences and music, in which a multi-conditional sampling formula is rewritten to transfer the pre-generated poses into a sequence with a partner style. In practice, to address the lack of multi-person datasets, we introduce AIST-M, a new dataset for partner dancer generation, which is publicly availiable at https://github.com/JJessicaYao/AIST-M-Dataset. Comprehensive evaluations on our AIST-M dataset demonstrate that the proposed DanY can synthesize satisfactory partner dancer results with controllable diversity.
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels

Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden. Recently, contrastive learning has exhibited a great potential in learning robust representations to boost downstream tasks with limited labels. In medical imaging scenarios, ready-made meta labels (i.e., specific attribute information of medical images) inherently reveal semantic relationships among images, which have been used to define positive pairs in previous work. However, the multi-perspective semantics revealed by various meta labels are usually incompatible and can incur intractable "semantic contradiction" when combining different meta labels. In this paper, we tackle the issue of "semantic contradiction" in a gradient-guided manner using our proposed Gradient Mitigator method, which systematically unifies multi-perspective meta labels to enable a pre-trained model to attain a better high-level semantic recognition ability. Moreover, we emphasize that the fine-grained discrimination ability is vital for segmentation-oriented pre-training, and develop a novel method called Gradient Filter to dynamically screen pixel pairs with the most discriminating power based on the magnitude of gradients. Comprehensive experiments on four medical image segmentation datasets verify that our new method GCL: (1) learns informative image representations and considerably boosts segmentation performance with limited labels, and (2) shows promising generalizability on out-of-distribution datasets.
Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.
Modern methods of technical education in the conditions of distance education

The article substantiates the trends and prospects for the development of distance education. It was noted that the development of methods and the development of remote forms of the educational process belong to innovative methods of training future specialists. A promising path for the development of distance learning has been revealed in accordance with the development of world trends related to the development of new information technologies and systems for establishing communications between various strata of population groups and, including, the educational sector.&#x0D; The expediency of the development of remote forms of education in Ukraine is substantiated and it is noted that such methods will motivate the students themselves to the need to increase their informational and intellectual level, capable of mastering new forms of acquiring professional abilities. It has been proven that the creation of remote forms of education will contribute to the improvement of the professional level of teaching staff and the level of mastery of computer technology, as it requires high professional abilities from teachers in the process of providing educational services using modern information technologies.&#x0D; Generalized features of distance learning methods are presented and brief characteristics of each of them are given. Separate information technologies, tools and the most popular online platforms used for organizing training in a distance format are considered. The main advantages that teachers and students receive when studying in a distance format are highlighted, and, in particular, the high mobility of students, which in turn does not require their strict presence in classrooms and laboratories of educational institutions. It is indicated that the introduction of distance learning allows to significantly reduce the expenditure items of the educational institution on utility payments. At the same time, the distance education system does not require the educational institution to have and maintain a wide list of basic funds, including classrooms for the full-time educational process in the presence of students. Such features of obtaining educational services are especially relevant in modern conditions, when Ukraine is waging a war of liberation with the Russian Federation.