Reviewerly/exHarmony huggingface dataset:
An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast cancer prediction has been studied by a number of researchers, and is considered as a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models which predicts the outcome in early stages by analyzing history of patient’s data. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR)are used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting(XGBoost). For performance comparison, we employed k-fold cross validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms.
Learning curves for the multi-class teacher–student perceptron

Abstract One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher–student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) estimation and empirical risk minimisation (ERM) were extensively analysed in this setting. At the same time, a considerable part of modern machine learning practice concerns multi-class classification. Yet, an analogous analysis for the multi-class teacher–student perceptron was missing. In this manuscript we fill this gap by deriving and evaluating asymptotic expressions for the BO and ERM generalisation errors in the high-dimensional regime. For Gaussian teacher, we investigate the performance of ERM with both cross-entropy and square losses, and explore the role of ridge regularisation in approaching Bayes-optimality. In particular, we observe that regularised cross-entropy minimisation yields close-to-optimal accuracy. Instead, for Rademacher teacher we show that a first-order phase transition arises in the BO performance.
An intelligent blockchain strategy for decentralised healthcare framework

Nowadays, securely sharing medical data is one of the significant concerns in blockchain technology. The existing blockchain approaches have faced high time consumption, low confidentiality, and high memory usage for transferring the file in a secure way because of attack harmfulness and large unstructured records. It has ended in security threat, so the integrity of the user data has been lost. Hence, a novel hybrid Deep Belief-based Diffie Hellman (DBDH) security framework was presented to protect medical data from malicious events. Incorporating a deep belief neural system continuously monitors the system and identifies the attacks. Initially, the IoMT dataset was collected from the standard site and imported into the system. Moreover, hash 1 was calculated for the original data and stored in the cloud server for verification. Then, the original data was encrypted with a private key for data hiding. The incorporation of homomorphic property helps to calculate hash 2 for encrypted data. Finally, in the verification module, both hash values are verified. In addition, cryptanalysis was performed by launching an attack to validate the performance of the designed model. Moreover, the estimated outcomes of the presented model were compared with existing approaches to determine the improvement score.
Research on Academic Early Warning Model Based on Improved SVM Algorithm

In this paper, machine learning technology is applied to the study of student academic early warning, and a student academic early warning model is constructed to help college education managers fully understand students, accurately predict students and personalized service students. Aiming at the problem that the SVM algorithm uses the default value of the penalty factor γ and the kernel parameter gamma when constructing the academic early warning model, the prediction model cannot achieve higher accuracy, a SVM algorithm based on improved FOA is proposed. Based on the first three years ' score data and library data of a university student, the SVM algorithm based on improved FOA is used to predict whether students can graduate smoothly in the future, and to give academic warning to students who may not graduate smoothly in the future. Experiments show that the SVM early warning model based on improved FOA is superior to the three types of traditional SVM model, decision tree and random forest in terms of accuracy.
A Lightweight Risk Advertising Image Detection Method Based on Mobile-ViT

To test risky advertising images efficiently on the Internet, this paper brings a recognition method based on Mobile-ViT. By integrating the advantages of CNN, training task can be finished on small data sets. Premised on multi-head attention mechanism, can further enhance the testing effect along with the increase of data sets. As the model is provided with fewer parameters, there is also less cost in training and reasoning, and real-time testing can be completed by deploying the model on equipment with limited computing power. In addition, Mobile-ViT will be integrated with other networks apart, to achieve the purpose of boosting the accuracy of the model.
Multi-Object Detection Algorithm in Wind Turbine Nacelles Based on Improved YOLOX-Nano

With more and more wind turbines coming into operation, inspecting wind farms has become a challenging task. Currently, the inspection robot has been applied to inspect some essential parts of the wind turbine nacelle. The detection of multiple objects in the wind turbine nacelle is a prerequisite for the condition monitoring of some essential parts of the nacelle by the inspection robot. In this paper, we improve the original YOLOX-Nano model base on the short monitoring time of the inspected object by the inspection robot and the slow inference speed of the original YOLOX-Nano. The accuracy and inference speed of the improved YOLOX-Nano model are enhanced, and especially, the inference speed of the model is improved by 72.8%, and it performs better than other lightweight network models on embedded devices. The improved YOLOX-Nano greatly satisfies the need for a high-precision, low-latency algorithm for multi-object detection in wind turbine nacelle.
Describing minimum bow force using Impulse Pattern Formulation (IPF) – an empirical validation

With bowed string instruments, the minimum bow force necessary to produce stable Helmholtz motion has often been discussed over the last decades. If the bowing force is too small or the bowing velocity too large, no stable tone is produced, and bifurcations or noise occurs. The Impulse Pattern Formulation (IPF) is a top-down method proposed previously (Bader, 2013), which explains such transitions between regular periodicity, bifurcation scenarios, and noise. The recursive equation is based on the idea that impulses, produced at a generator within a musical instrument, travel through the instrument, are reflected, exponentially dampened, and finally trigger or interact with succeeding impulses at the generator. Real bowing is measured on an experimental pendulum for self-organized bowing. Bowing force and velocity are recorded during the transitions from bifurcation to stable tone production. Then the IPF is used under a Simulated Annealing paradigm to reproduce this behavior, using the measured bowing pressure and velocity as input parameters. Compared to previous models, the IPF predicts minimum bow force with much higher precision, which is caused by bowing being a nonlinear dynamical system, including, e.g., hysteresis. In contrast, previous works mainly used analytical equations, therefore not considering the self-organizing nature of the process.
Semantic Communication Approach for Multi-Task Image Transmission

This paper presents a deep learning-based image features extraction and compression for multi-tasks, which can be applied to various intelligent tasks. We explore the multilevel features of the source by designing different information extraction networks, which contain text semantics, image segmentation, and pixel information. We propose a coarse-to-fine architecture to excavate the plentiful semantic information received from the encoder. The coarse module recovers the multi-granularity image according to the receiving symbols, and the fine module fuse different quality image to improve the reconstruction performance. In particular, we use multi-attention networks to extract and recover the image features at pixel levels. To overcome the artifact blocks phenomenon during the image reconstruction process that lacks necessary information, we design a dual features block that can mitigate the problem. Meanwhile, the system can accomplish different tasks by changing the last layers of the model.
Dataset Distillation: A Comprehensive Review

Recent success of deep learning is largely attributed to the sheer amount of data used for training deep neural networks.Despite the unprecedented success, the massive data, unfortunately, significantly increases the burden on storage and transmission and further gives rise to a cumbersome model training process. Besides, relying on the raw data for training \emph{per se} yields concerns about privacy and copyright. To alleviate these shortcomings, dataset distillation~(DD), also known as dataset condensation (DC), was introduced and has recently attracted much research attention in the community. Given an original dataset, DD aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. In this paper, we give a comprehensive review and summary of recent advances in DD and its application. We first introduce the task formally and propose an overall algorithmic framework followed by all existing DD methods. Next, we provide a systematic taxonomy of current methodologies in this area, and discuss their theoretical interconnections. We also present current challenges in DD through extensive experiments and envision possible directions for future works.
The problems of scientific realism today

The article describes scientific realism and the debate around this position. It shows that initially (in the scholastic tradition) the debate between realists and antirealists was purely ontological, since it was accepted that when we know, we know the real − knowledge cannot be anything other than knowledge of the real. The question about the reality of the object of our knowledge, about whether the world beyond our representations is equal to the world we represent to ourselves, distinguishes modern philosophy from classical philosophy and arises from the claim that we know our representations and not the real. A twofold problem is formed: first, to demonstrate the existence of the world beyond our representations, and second, to demonstrate that that the knowledge we have constitutes precisely the knowledge of the world in which we live and is, in fact, actual knowledge, not chimer. Thus the problem of realism takes on an almost exclusively epistemological meaning. Nevertheless, contemporary realistic positions often confuse ontological and epistemological theses, which leads to internal contradictions. The same is true of the proponents of anti-realist views. The question of the causes of the anti-realistic tendency in the philosophy of science is raised and it is shown that the initial attitude of the modern science was realistic. It was undermined, on the one hand, by anti-realistic interpretations of the cognitive process (starting from Kant), on the other hand, by difficulties of theoretical order arisen in physics, and the main thing was that science began to deal with the unobservable, undermining the cognitive basis of radical empiricism. However, the new cognitive situation does not necessarily lead to anti-realism, another way of development relies on an understanding of the complexity and problematic relationship between theory and experience. A number of reasons in favor of scientific realism are concluded.
THE PHILOSOPHY OF ARTIFICIAL INTELLIGENCE IN THE EDUCATIONAL PROCESS

The present is considered through the prism of artificial intelligence introduction, which became the reason for the formation of human dependence on science-intensive technologies. In this context, education is not an exception, it uses the opportunities of technological progress to change the processes of cognition. Such changes become one of the primary causes of changes in human existence, communication, the way of obtaining educational services, etc., all this takes on new forms in the conditions of the artificial intelligence development and the conditions that it creates. In accordance with this, the goal was formed – to consider the features of artificial intelligence implementation in the educational process. To achieve the goal, the following tasks were defined: 1) to find out the essence of artificial intelligence; 2) to demonstrate the possibilities of using artificial intelligence in the conditions of a pandemic and war; 3) to establish the impact of artificial intelligence on the formation of Homo sapiens in the age of society informatization. The implementation of these tasks was facilitated by the following methods: historical, behavioral, dialectical, and phenomenological. The combination of these methods helped to clarify the essence of artificial intelligence at the theoretical and practical levels, to trace the people behaviour who use the results of artificial intelligence in the educational spheare. The confirmation of the proposed hypothesis was a sociological survey conducted as a part of the experiment for six years (2016–2022). The study identifies factors that take place in real life and influenced the wider possibilities of involving artificial intelligence in the educational process. The conducted research gives reasons to claim that artificial intelligence contributes to the formation of a new type of Homo sapiens with a new type, level of thinking and cognition. We note that the era of Internet opportunities, given by artificial intelligence, has not only positive, but also negative factors. The biggest negative phenomenon is the lack of responsibility of artificial intelligence for its actions.
The Virtual Interactive Learning Model using Imagineering Process via Metaverse

The virtual interactive learning model using imagineering process as a tool to promote happy learning for digital age learners. The concept is based on the combination of virtual learning environment and metaverse in order to create learning experience via virtual community. The objectives of this research are (1) to study and synthesise the conceptual framework of the virtual interactive learning model using imagineering process via metaverse, (2) to develop the virtual interactive learning model using imagineering process via metaverse, and (3) to study the results after using the virtual interactive learning model using imagineering process via metaverse. The participants in this research include seven experts from various institutions, all of whom are specialised in the design and development of instruction models and instruction systems. The research tools consist of (1) the virtual interactive learning model using imagineering process via metaverse, and (2) the evaluation form on the suitability of the virtual interactive learning model using imagineering process via metaverse. The results, which are in consistence with the expectation of the researchers, show that (1) this research can be used as a guideline to develop the virtual interactive learning system using imagineering process via metaverse, which can promote happy learning, and it consists of six steps of imagineering process integrated with learning through virtual environments via metaverse; thereby, users can interact in the virtual world and exchange knowledge with one other through virtual reality technology, (2) the overall suitability of the development of the virtual interactive learning model using imagineering process via metaverse (overall elements) is at the very high level, and (3) the overall suitability of the development of the virtual interactive learning model using imagineering process via metaverse is at the very high level.
Witness Generation for JSON Schema

JSON Schema is a schema language for JSON documents, based on a complex combination of structural operators, Boolean operators (negation included), and recursive variables. The static analysis of JSON Schema documents comprises practically relevant problems, including schema satisfiability, inclusion, and equivalence. These problems can be reduced to witness generation: given a schema, generate an element of the schema --- if it exists --- and report failure otherwise. Schema satisfiability, inclusion, and equivalence have been shown to be decidable. However, no witness generation algorithm has yet been formally described. We contribute a first, direct algorithm for JSON Schema witness generation, and study its effectiveness and efficiency in experiments over several schema collections, including thousands of real-world schemas.
Cloud Multimedia Data Security by Optimization-Assisted Cryptographic Technique

Currently, the size of multimedia data is rising gradually from gigabytes to petabytes, due to the progression of a larger quantity of realistic data. The majority of big data is conveyed via the internet and they were accumulated on cloud servers. Since cloud computing offers internet-oriented services, there were a lot of attackers and malevolent users. They always attempt to deploy the private data of users without any right access. At certain times, they substitute the real data by any counterfeit data. As a result, data protection has turned out to be a noteworthy concern in recent times. This paper aims to establish an optimization-based privacy preservation model for preserving multimedia data by selecting the optimal secret key. Here, the encryption and decryption process is carried out by Improved Blowfish cryptographic technique, where the sensitive data in cloud server is preserved using the optimal key. Optimal key generation is the significant procedure to ensure the objectives of integrity and confidentiality. Likewise, data restoration is the inverse process of sanitization (decryption). In both the cases, key generation remains a major aspect, which is optimally chosen by a novel hybrid algorithm termed as “Clan based Crow Search with Adaptive Awareness probability (CCS-AAP). Finally, an analysis is carried out to validate the improvement of the proposed method.
Neural Mesh-Based Graphics

We revisit NPBG, the popular approach to novel view synthesis that introduced the ubiquitous point feature neural rendering paradigm. We are interested in particular in data-efficient learning with fast view synthesis. We achieve this through a view-dependent mesh-based denser point descriptor rasterization, in addition to a foreground/background scene rendering split, and an improved loss. By training solely on a single scene, we outperform NPBG, which has been trained on ScanNet and then scene finetuned. We also perform competitively with respect to the state-of-the-art method SVS, which has been trained on the full dataset (DTU and Tanks and Temples) and then scene finetuned, in spite of their deeper neural renderer.
Computing Rule-Based Explanations by Leveraging Counterfactuals

Sophisticated machine models are increasingly used for high-stakes decisions in everyday life. There is an urgent need to develop effective explanation techniques for such automated decisions. Rule-Based Explanations have been proposed for high-stake decisions like loan applications, because they increase the users' trust in the decision. However, rule-based explanations are very inefficient to compute, and existing systems sacrifice their quality in order to achieve reasonable performance. We propose a novel approach to compute rule-based explanations, by using a different type of explanation, Counterfactual Explanations, for which several efficient systems have already been developed. We prove a Duality Theorem, showing that rule-based and counterfactual-based explanations are dual to each other, then use this observation to develop an efficient algorithm for computing rule-based explanations, which uses the counterfactual-based explanation as an oracle. We conduct extensive experiments showing that our system computes rule-based explanations of higher quality, and with the same or better performance, than two previous systems, MinSetCover and Anchor.
AI-Enabled Blockchain Consensus Node Selection in Cluster-Based Vehicular Networks

In the scenario of highly mobile nodes, applying blockchain not only needs to ensure high performance and security but also needs to consider the low stability of consensus nodes. We introduce a novel framework to reduce the negative impact of low-stable consensus nodes. In addition, we have developed an intelligent algorithm for selecting the optimal number of Mobile Consensus Nodes (MCNs) considering various parameters including Stability of Node (SoN). The results demonstrate that the proposed scheme improves the average reputation and stability of consensus nodes by 6.8% and 17.5%, respectively while reducing the average message counts by 33.9%.
A Study on Security and Privacy Practices in Danish Companies

Increased levels of digitalization in society expose companies to new security threats, requiring them to establish adequate security and privacy measures.Additionally, the presence of exogenous forces like new regulations, e.g., GDPR and the global COVID-19 pandemic, pose new challenges for companies that should preserve an adequate level of security while having to adapt to change.In this paper, we investigate such challenges through a two-phase study in companies located in Denmark-a country characterized by a high level of digitalization-focusing on software development and tech-related companies.Our results show a number of issues, most notably i) a misalignment between software developers and management when it comes to the implementation of security and privacy measures, ii) difficulties in adapting company practices in light of implementing GDPR compliance, and iii) different views on the need to adapt security measures to cope with the COVID-19 pandemic.
ChatGPT on Characteristic Mode Analysis

&lt;p&gt;ChatGPT is a natural language processing model based text-to-text AI interactive conversation platform developed using generative pre-trained architecture (GPT) by OpenAI. The present study utilizes the ChatGPT to understand the characteristic mode analysis-a popular numerical technique for antenna and array design in recent times. It was noted that ChatGPT can provide a basic understanding of characteristic mode theory in an elegantly simplified text language. However, being a data-driven model, its accuracy strongly depends on the data used to train the model, low accuracy is noted on predicting the names of the reference papers or related journal authors correctly in most cases. Another major limitation of using chatGPT is the lack of plagiarism check as it chooses sentences based on the training data resources. &lt;/p&gt;