Reviewerly/exHarmony huggingface dataset:
Safety Analysis and Design of Artificial Intelligence System Based on Sensor and AI Technology

In recent years, with the continuous updating and development of my country’s science and technology, AI (artificial intelligence) technology, as an emerging technology, is constantly affecting our lives, and the importance of AI technology to our social production has been greatly reflected. Since the beginning of the 21st century, the development of AI has reached a staged peak. However, due to the imperfect application of AI systems, there are unstable factors and risk factors in its practical application. In this paper, the safety analysis and design of AI systems are carried out based on sensors and AI technology. With the emergence of safety-related problems in AI systems, the safety assurance of AI systems in industrial manufacturing has also attracted more and more researchers’ attention. Analyze the application status of AI systems in industrial control, and carry out safety analysis and design combined with sensors and AI technology. Therefore, it is necessary to understand the safety and ethical issues in the application of AI systems, pay close attention to its development, better manage risks, and make AI technology better used in industrial manufacturing. The final result of the study shows that when the number of operations under the sensor and AI technology is 88 times, the accuracy rate is 95%, while under the industry access mode, the number of operations is 77 times, and the accuracy rate is 76%. It can be seen from the experimental data, the AI system based on sensor and AI technology has a high operation accuracy, which can greatly improve the safety performance.
Python Data Analysis Techniques in Administrative Information Integration Management System

With the development of computer and the progress of network technology, computer application has become a powerful catalyst in all walks of life. In the information age, the efficiency of information processing is directly related to the quality of administrative service. Especially in administrative information management, most of its work is related to information dissemination and file transfer. This paper adopts Python data analysis technology to improve the efficiency of administrative information integration management. The use of Python technology improves the quality and efficiency of administrative information integration management and greatly improves the data service performance of the system. This paper studies the application of Python data analysis technology in administrative information integration management system, and illustrates the relevant working mechanism of Python data analysis technology in administrative information integration management system. Data analysis shows that Python data analysis technology has high performance in administrative information integration management.
The Construct in the Context of the Evolution of the Modern Picture of the World

The current picture of the world has many names: digital, post-non-classical, information, telecommunications, etc. However, all of them cannot fully grasp the definition of the world, which leads to the idea that the task of substantiating the picture of the world cannot be connected only with interpretation through technological progress, or quantitative changes in the material world. The starting point of the authors reasoning in the presented article is the thesis that the digital picture of the world is a field of constructs, which means that this field cannot be purely empirical. In other words, it opens up opportunities for discussing reality in terms of its constructiveness, not virtuality. This article is of an overview nature and is intended to indicate new areas of development of philosophy.
Toxic comment classifier on social media platform

The objective of the paper is to mitigate internet negativity by identifying and blocking toxic comments related to a particular topic or product. The detrimental effects of social media abuse and harassment can cause people to refrain from expressing themselves. Although some platforms disable user comments altogether, this method is not efficient. The presence of toxicity in comments can assist platforms in taking appropriate measures. The paper aims to classify comments according to their toxicity levels for future blockage. The dataset comprises comments classified into six types, toxic, severe toxic, threat, obscene, identity hate, and insult. Multiple classification techniques will be employed to determine the most accurate one for the data. The authors will employ four types of classification and select the most precise one for each dataset. This methodology enables the authors to choose various datasets for the problem and select the most accurate classifier for each dataset.
Implementing smart technology in engineering education

Engineering is an important field for the evolution of the world. Engineers build and create things that improve people’s lives. In addition, engineers use innovative technologies in their academic and professional work. Consequently, it is important to train future engineers to use intelligent technology. Engineering is a complex field that uses many innovative technologies. Such technologies as CAD/CAM, 3D printing, and virtual reality are becoming increasingly popular in the engineering community. Engineers are using these technologies to create new prototypes and inform the public about new ideas. By presenting these intelligent ideas to the world, engineers are helping to create better products and designs. In addition, engineering schools are using smart technology to educate their students. Instructors use CAD/CAS software in the classroom to demonstrate practical applications. This helps students understand what they are learning and apply it to real-world scenarios. In addition, engineering schools use gamification and case studies to engage students in the classroom. These strategies help teach students relevant concepts and apply them to real-world situations. It is easy to see how smart technology improves engineering education and implementation in the field. Current and future engineers use these tools in their daily work. These changes improve learning and teaching tactics for instructors. Ultimately, this leads to better designs and applications of technology in the real world. It is also interesting to note how the adoption of smart technology affects engineering degrees. Many engineering colleges are introducing technology tools into their curricula as a graduation requirement. This allows their programmes to properly prepare their students for the workplace. Students are required to take technology implementation exams before entering college courses. This ensures that all graduates are well-prepared to work as engineers. Engineering is an exciting field, with many advanced technologies, both educational and practical. Future engineers learn by introducing and implementing new smart technologies in their classrooms. In addition, renowned engineers use innovative strategies to improve existing technologies or create new blueprints for inventions. Engineering is an integral part of the world; thanks to clever technological solutions, everyone can become an engineer!
The Influence of Media on Modern Reality: Theoretical Aspects of Media Philosophy and Media Reality

Modern stage of society development is characterized by media penetration into almost in all spheres of life and human activity. Media develops rapidly and changes reality. There is a concept called media reality. Article is devoted to philosophical understanding of media influence on modern reality, their ability to transform attitudes in society. Theoretical aspects of modern media theories and media philosophies are considered.
Innovations in Russian Education

The chapter is devoted to the analysis of modern education system's ability to withstand the challenges in a situation of risk and instability. The author analyzes the problem situations that the education system may face, examines the specifics of entrepreneurial education and evaluates its capabilities in terms of finding the relative stability of a specialist in the labor market, as well as reveals the main current trends in the education system.
Image text deblurring by convolutional neural networks

In this research paper, a modern autoencoder architecture based on convolution neural networks is suggested for document deblurring. The main goal of this project is to achieve image deblurring without having any prior knowledge about the blur kernel. The approach creates a sharp image from a blurry one, demonstrating the utility of convolutional neural networks in document deblurring. Only a blurred image is used as input to find a sharp image. As a result, the blur kernel knowledge isn’t requested. In the evaluation step, the predicted image is assessed by using peak signal to noise ratio (PSNR) and structural similarity index (SSIM). The results indicate a major increase in visual quality over the studies revelated using a dataset of text images.
GradMDM: Adversarial Attack on Dynamic Networks

Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robustness of dynamic neural networks against energy-oriented attacks targeted at reducing their efficiency. Specifically, we attack dynamic models with our novel algorithm GradMDM. GradMDM is a technique that adjusts the direction and the magnitude of the gradients to effectively find a small perturbation for each input, that will activate more computational units of dynamic models during inference. We evaluate GradMDM on multiple datasets and dynamic models, where it outperforms previous energy-oriented attack techniques, significantly increasing computation complexity while reducing the perceptibility of the perturbations https://github.com/lingengfoo/GradMDM.
Case Analysis: FairChain

This chapter presents a within-case analysis of FairChain. The content includes background of the company and the industry; the issues that FairChain hopes to address by applying blockchain; the actual implementation stages and the plan for future; what kind of benefits blockchain can bring to FairChain; and what the challenges are that FairChain overcame.
Plant Growth Phase Classification Using Deep Neural Network (Case Study of ASF in Poso District, Central Sulawesi Province)

An innovation developed through a combination of satellite data with official data to provide a solution to the limitations of the Area Sample Framework (ASF) survey where surveyors have to go directly to places that are sometimes difficult to reach and require a relatively long time, the Central Statistics Agency (BPS) suggested using Landsat-8 satellite imagery with Deep Neural Network Method (DNN) to classify rice plant growth phases. Data from Landsat-8 which has the characteristics to see land cover, especially plants. Apart from the band, the variables in this study were added to the vegetation index calculated from satellite data and combined with official data. One of the classification methods used is Deep Neural Network. This study aims to compare the methods between Artificial Neural Network (ANN) and DNN in classifying rice growth phases and predicting rice growth phases using DNN. With split data stratified 5-fold cross validation and data normalization using a robust scaler, the classification results show the average performance in terms of accuracy, precision, sensitivity, f1-score, Cohen Kappa index and Average Precision (AP) values. Based on several performance evaluations of the two methods from both ANN and DNN there is no significant difference.
Unsupervised Logo Detection Using Adversarial Learning From Synthetic to Real Images

Most existing deep learning-based methods for logo detection require a large amount of object-level annotated training data. However, it takes a lot of time and effort to create a good object-level annotated logo detection dataset. Many researchers have tried to utilize synthesized images with automatically generated annotations for training to deal with data annotation problem. However, in real applications, model trained using synthesized images suffer from performance loss due to domain shift between synthesized and real-world images. To this end, in this paper, we propose an adversarial learning-based unsupervised domain adaptation method for logo detection. We only use labeled synthesized logo images for model training and adapt target domain knowledge using unlabeled real-world logo images. We propose entropy minimization of mid-level output feature maps in order to effectively align the domain gap between synthetic and real images for the logo detection task. Additionally, we have generated coherent synthesized logo images with automatically constructed bounding box annotations for different datasets to perform unsupervised training for the experiments. Our experiments show that the proposed method improves performance on different logo datasets compared to direct transfer from source to target domain (synthetic-to-real images) without any labeling cost and increasing network parameters.
Two genre classification of Japanese literary works written by Ryuunosuke Akutagawa and Kenji Miyazawa based on word vectors

Word vectors are applied various tasks in natural language processing. However, the potentiality of the word vectors of Japanese has not been discussed as much as those of English. Therefore, the purpose of this paper is to classify the genre of modern Japanese literary works using characteristic features evaluated by the word vectors. The accuracy of the classification between novels and poetic works was 95%, and the one between novels and essays was 90%. The word vectors are applicable in the genre classification problem in modern Japanese literary works.
Algorithm for emergency deletion of files by voice command

The Currently, there is a growing interest in various methods and means of protecting information, which is due to the growing number of crimes related to the violation of confidentiality of information, including through physical access to a computer. There are many ways to protect information from unauthorized access, such as physical security measures, password protection, and so on. However, a situation may arise when all defense lines have already been overcome and there is no other way to quickly block or destroy confidential information in any other way than with the help of a voice command. Thus, the topic of the article is relevant. The object of research is an information system containing confidential information. The subject of the research is the methods of emergency deletion of confidential information. The purpose of this article is to develop an algorithm for urgent deletion of files by voice command. In the course of the work, the algorithm for the emergency deletion of files by voice command was developed, which allows blocking the means of inputting information (keyboard and mouse) and deleting files located in a predetermined folder. The algorithm provides protection against accidental pronunciation of a voice command: before deleting files, a timer is first started, the time of which is pre-set by the user. During the duration of the timer, it is possible to cancel the deletion of files by entering a password, however, there is no way to access the information until the appropriate password is entered.
Phishing Attack Types and Mitigation: A Survey

The proliferation of the internet and computing devices has drawn much attention during the Covid-19 pandemic stay home and work, and this has led the organization to adapt to staying home. Also, to let the organization work due to the infrastructure for working on proxy during the pandemic. The alarming rate of cyber-attacks, which through this study infer that phishing is one of the most effective and efficient ways for cyber-attack success. In this light, this study aims to study phishing attacks and mitigation methods in play, notwithstanding analysing performance metrics of the current mitigation performance metrics. Results indicate that business enterprises and educational institutions are the most hit using email (social engineering) and web app phishing attacks. The most effective mitigation methods are training/awareness campaigns on social engineering and using artificial intelligence/machine learning (AI/ML). To gain zero or 100% phishing mitigation, AI/ML need to be applied in large scale to measure its efficiency in phishing mitigation.
The Data and the Tools

To perform a quantitative, entropy-based analysis like the one that has been sketched out in the previous chapter, a large, representative lexicon of inflected wordforms in phonetic transcription is needed. Similar resources are increasingly being developed for modern languages: cf. among else Zanchetta and Baroni (2005) and Calderone et al. (2017) for Italian, Bonami et al. (2014) and Hathout et al. (2014) for French, the lexicon described in Bonami and Luís (2014) for Portuguese.
Discovering Rule Lists with Preferred Variables

Interpretable machine learning focuses on learning models that are inherently understandable by humans. Even such interpretable models, however, must be trustworthy for domain experts to adopt them. This requires not only accurate predictions, but also reliable explanations that do not contradict a domain expert's knowledge. When considering rule-based models, for example, rules may include certain variables either due to artefacts in the data, or due to the search heuristics used. When such rules are provided as explanations, this may lead to distrust. We investigate whether human guidance could benefit interpretable machine learning when it comes to learning models that provide both accurate predictions and reliable explanations. The form of knowledge that we consider is that of preferred variables, i.e., variables that the domain expert deems important enough to be given higher priority than the other variables. We study this question for the task of multiclass classification, use probabilistic rule lists as interpretable models, and use the minimum description length (MDL) principle for model selection. We propose S-Classy, an algorithm based on beam search that learns rule lists and takes preferred variables into account. We compare S-Classy to its baseline method, i.e., without using preferred variables, and empirically demonstrate that adding preferred variables does not harm predictive performance, while it does result in the preferred variables being used in rules higher up in the learned rule lists.
Military Hand Signal Classification Using Deep Learning

Military hand signals are a method of visual communication for field use and are now the most common forms of communication during operations. It is important that communications in missions are clear, distinct, and understandable. Hand Sign Language (SL) has become a standard part of communication in the military, especially when voice communication is not desirable or silence has to be maintained for security. With the advent of drones and high-precision cameras, communication using hand signals can be very effective, as it is quick and the support device does not need to be in proximity. A trained human operator may be required to translate the meaning of these hand signals. Instead, a machine learning algorithm can be developed to recognize and translate these messages, saving time for translation and the requirement of specialized training for the interpreters. This project aims to create a method that can translate army hand signals into their respective labels, which can be used by drones for reconnaissance and safety. Our model, which is based on convolution neural networks, achieves a cross-validation accuracy of 98.32% and a test accuracy of 97.94%.
The Review of the Monograph by V.A. Bazhanov “Cultural Brain – Neuroscience – Mathematics. Listening to I. Kant”

Over the past 2025 years, significant changes have taken place in the Russian philosophy and methodology of science: philosophers of science are actively exploring new ways to solve the problems facing modern methodology, attracting more and more new subject areas of science. At the same time, the research experience accumulated in the previous period continues to have a serious impact on the current state of the philosophy of science in our country. The author of the monograph skillfully combines the classical gnostic approaches of Kant and modern contexts, thereby revealing the problems of sociocentrism. The appeal in the text of the work to the aesthetic areas of human knowledge looks absolutely logical, given the context of the study of the cultural brain chosen by the author.
The COVID-19 pandemic and graduation in visual arts: collaborative experiences in remote teaching

Since the beginning of the COVID-19 pandemic in 2020, universities have suspended in-person activities, and, after a series of measures education has become remote. In this context, the Visual Arts Majors at FURG - Bachelor and Licentiate - in a joint action between the Coordination, Structuring Faculty Nucleus (NDE), professors, and students - prepared an Emergency Pedagogical Project (EPP), whose pedagogical proposals are structured in teaching-research-extension projects and interdisciplinary modules. We present this process, the challenges, and the solutions built, discussing the role of Public University and Visual Arts undergraduate courses in this moment of sanitary and political crisis.
Fully Cascade Consistency Learning for One-Stage Object Detection

Object detection is usually solved by deploying one single prediction head including classification and localization branches to obtain the final results. Recently proposed works utilize several prediction heads in a cascade learning manner to improve the detection performance. Despite achieving promising performance, existing cascade learning manner methods still meet with two inconsistency issues. Firstly, most of them refine the bounding boxes in different prediction heads only by depending on the localization accuracy (i.e., IoU), while ignoring the inconsistency between classification confidence and localization accuracy. Moreover, simply increasing the IoU threshold by experience to select positive samples makes the inconsistency issue even worse. Secondly, little consideration has been paid on the feature inconsistency between detection-specific features from different prediction heads and detection-generalized ones from backbone model. The extracted feature from backbone model contains the general representation for the whole images. While prediction heads need to be carefully designed to have specific ability which contains more discriminative expressions for the two sub-tasks classification and regression. The different contexture representations of the output features from these two parts lead to the feature inconsistency between backbone model and prediction head in cascade learning architecture. To solve these two inconsistency issues, this paper proposes a novel cascade consistency learning method for one-stage detector. Specifically, a feature adaptation module is firstly developed to calibrate features from different prediction heads and backbone model for solving the feature inconsistency. Then, we design an automatic positive sample threshold selection strategy for further solve the inconsistency between the classification and localization predictions. Moreover, the quality of bounding boxes in cascade learning manner are evaluated by taking both the classification confidence and localization accuracy into consideration. Experiments on MS COCO show that our proposed cascade consistency learning manner (dubbed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}^{2}\text{L}$ </tex-math></inline-formula> ) can achieve clear improvement over counterparts based on several different one-stage detectors, while performing favorably against state-of-the-arts.