Reviewerly/exHarmony huggingface dataset:
Deep learning captcha recognition for mobile based on TensorFlow

As the most common captcha, text captcha can prevent others from maliciously using computer programs to log in or attack, and is an important safeguard in Internet authentication. In recent years, with the development of the Internet, the field of artificial intelligence has also developed at a high speed, and convolutional neural networks are widely used in various fields. In this context, for the common problem of character-based captcha recognition, this paper investigates captcha recognition based on a deep learning neural network framework used by the TensorFlow framework with modifications based on the VGG16 convolutional neural network. The 4-digit captcha randomly composed of 64 characters is then converted into an image, and after operations such as image processing and encoding of the captcha, a large number of training sets are generated and the recognition of the captcha is done by the convolutional neural network. Finally, the design GUI interface is deployed to mobile devices with a final accuracy rate of 85% on the test set.
Sulla ricezione e le traduzioni italiane di Italia di Maria Konopnicka

The paper aims at presenting an outline of the Italian translations of the poetry collection Italia by the poetess Maria Konopnicka published between 1916 and 1929.
Giacomo Leopardi in lituano: L’ Infinito nelle traduzioni di Nyka Niliūnas e Lanis Breilis

This article proposes a comparative analysis of two translations from Italian into Lithuanian of the idyll The Infinite by Giacomo Leopardi.The first translation, published in 1968, was made by the poet and translator Nyka-Niliūnas.The second one, published in 2017, was made by then translator Lainis Breilis.The introduction demonstrates the interest of Lithuanian intellectuals living in the diaspora toward contemporary Italian poetry starting from the sixties in the context of the Soviet occupation.The study proceeds with a close reading of the two target-texts, aimed at understanding how the two relate with the source-text and also between each other.Among the several aspects analyzed, there is the deficiency in both translations of the deictic system relied upon by Leopardi's idyll.More specifically, the attention will be focused on the semantic implications of this deficiency.
“Corpo di cento mille / molini a vento!”. Il percorso testuale del Don Chisciotte nei libretti d’opera italiani

A very particular form of reception of the Don Quijote de la Mancha is musical reception.In transcoding from narrative prose to the language of the Opera, the librettist shows his personal knowledge of the work of Miguel de Cervantes, read in the original text or in translation.Since the eighteenth century, in some librettos written in Italian, adherence to the hypotext, albeit interpreted in a new light, leads in some passages to an authentic rewriting of the play.The aim of this article is to show the processes of transcoding de Cervantes' novels based on significant Italian librettists.
Emosi Tokoh dalam Novel Ayah dan Sirkus Pohon Karya Andrea Hirata

This study examines the classification of the emotions of characters in the novel Father and the Tree Circus by Andrea Hirata. The approach used is the approach of literary psychology. The method used in this study is descriptive qualitative. The data taken in this study is in the form of text excerpts that show the emotions of the characters. The source of the data in the study is the novel Father and the Tree Circus by Andrea Hirata. The data collection technique used is the library technique. The data validity technique uses theoretical triangulation techniques. The data analysis techniques used by researchers are, 1) data collection, 2) data reduction, 3) data presentation, and 4) conclusions. Based on the results of the research conducted, there are eight classifications of emotions. The figures who dominate emotions are Hobirin, Sabari, and Marlena. Angry emotions are experienced by Marlena who is depicted through action by yelling at Sabari when handing over Marlena's fallen handkerchief. The delight experienced by Hobirin when he was accepted into the job of being a traveling circus. Sadly portrayed through the character of Hobirin when the work of the traveling circus is temporarily suspended. Guilt is clearly portrayed by the author through the character Hobirin who feels guilty to his father for doing bad things until he is taken to the police station. Fear is portrayed through the character Sabari who previously felt scared after a divorce case with his wife. Hate is felt by Hobirin who is depicted when the character hates the pomegranate tree. Love is portrayed through Sabari's actions as he grasps a pencil given by Marlena. Shame is experienced by Hobirin which is depicted when he feels ashamed of himself when he loses his job. An important implication of this research is that literature can contribute to the discussion of psychological science.Keywords: Emotions, Psychology, Novel, Ayah dan Sirkus Pohon, Andrea Hirata.
Research and Application of Health Code Recognition Based on Paddle OCR under the Background of Epidemic Prevention and Control

Normalization of epidemic prevention and control,in order to solve the problems of low efficiency and high error probability of manual review of health code and trip card pictures by epidemic prevention personnel, an automatic health code recognition method based on paddeocr is proposed.This method uses the open source paddleocr technology for image recognition, and uses the target text location algorithm to output the required text.Practice shows that this method has good recognition effect and high accuracy.Applying this method to the campus epidemic prevention and control system can greatly improve the audit efficiency.
ECT-ABE Algorithm-Based Secure Preserving Framework for Medical Big Data

Cyber risks and data breaches have expanded considerably across a wide range of business areas. Due to digitalization and the Internet of Things (IoT), the medical community generates a massive amount of heterogeneous clinical records on a daily basis. This unprocessed heterogynous medical data contains a wealth of clinical information that is vital for the medical field's advancement. However, patient privacy concerns arise when storing and analysing this medical big data on a private cloud. In this present study, the Optimized Ciphertext Attribute-Based Encryption (ECT-ABE) algorithm is introduced to enhance the safety of healthcare cloud storage. Additionally, the suggested system design incorporates a separate proxy server to isolate communication between clients and the cloud server, hence limiting direct attacks on cloud servers and lowering computational pressure on cloud servers. In comparison to earlier methods, the proposed cryptosystem is faster to execute and produces a lighter ciphertext. Additionally, both re-encryption and pre-decryption require only a single arithmetic operation.
Analysis of Native Multi-model Database Using ArangoDB

ArangoDB—the native multi-model database—is one of the most promising, next-generation database solutions for handling Big Data. ArangoDB is a scalable, fully managed graph database, document store, and search engine all in one database launched in 2011. ArangoDB is the perfect amalgam of three major NoSQL models—graph, document, and key-value pair. ArangoDB is native in the sense that it supports all the three mentioned NoSQL models under one roof and one query language. ArangoDB uses Arango Query Language (AQL) as its data query language.
Signal domain physical modification for PLS

In wireless communication, the data is conveyed through transmission over a physical signal, occupying a bounded specific space-time-frequency block in hyperspace. The building block of this signal is a distinct waveform that conveys the data symbols. A successful reception or detection of a signal requires the receiver to know the exact location of the transmitted signal in space-time-frequency hyperspace. Moreover, a successful identification requires that the receiver knows the features of the transmitted signal in order to decode the data. The goal of physical layer security (PLS) is to ensure the successful detection and identification of the information at the legitimate receiver while preventing illegitimate receivers from doing that. Initially, this chapter presents an overview of the inherent security characteristics provided by different waveforms against detection and identification. Then, it investigates how securing the control channel signals can affect the secrecy capacity of the communication system. However, inherent security and securing the control signal cannot fulfill the security requirements of all wireless applications. Therefore, in order to provide the required level of security, PLS comes into play, where it includes designing different processes that affect the signal based on the parameters extracted from the observation plane to modify the transmissions to ensure secure communication as highlighted in Chapter 3 (Modification plan).
Comparison between Human Translation and Machine Translation in Translating the Publicity Text of Haihunhou Museum

In 2016, there were amount of news of new technological breakthroughs in the field of machine translation at home and abroad. Whether machine translation can replace human translation has become one of the hot topics in academic circles. This paper argues that in specific restricted language translation, machines translation has provided people with convenient and efficient services, but on the whole, the translation quality of machine translation is not as good as human translation and can not replace human translation in a short time. Translation quality assessment is an important sub task in the field of machine translation. At present, translation quality assessment has a good performance in Chinese-English, English-German machine translation, and the technology is relatively mature. However, applying the model to the machine translation of Haihunhou publicity text still has many problems. Compared with the parallel corpus of manual translation, the differences of machine translation in terms translation, vocabulary discrimination, syntactic analysis and symbol translation in Haihunhou publicity text provide a corpus analysis basis for improving the machine translation system.
When the Supply Voltage is Dynamic

Voltage regulators usually serve the purpose of generating a constant regulated output voltage. By means of control loops, a stable and precise supply is generated from an unregulated input voltage. What purpose, then, does dynamic voltage scaling (DVS) serve?
The influence of the pedagogical technology for motivation development on the value self-determination of students with a high level of creative thinking

Contemporary education in Russia is faced with the task of introducing a humane approach in the training of a future specialist capable of adapting to changing living conditions. The aim of the paper in this perspective was to substantiate theoretically and study empirically the relationship between the value-motivational sphere of the personality and creative thinking of a young person. A theoretical analysis of works devoted to a creative approach in education has shown that internal motivation for educational activities is combined with motivation for a creative approach in solving educational problems. As a result of the experimental study, a significant relationship was revealed between the significance of the value of spirituality and an increase in the integral indicator of the creative thinking level, which indicates the orientation of a creative person to the search for inner harmony and their own destiny among young people.
The social and cultural atmosphere of libraries and their impact on education

The transformation of culture ensures the correction and integration of various forms of activity of social subjects, thereby exerting a direct impact on the functioning of modern society. The article reveals the issues of the formation of socio-cultural space by cultural institutions. In particular, the library is considered as one of the important institutions of society, the activities of which are closely related to education, economic, socio-political, cultural factors. The main purpose of the library is shown, which can help to familiarize the younger generation with the true values of culture, for which it is necessary to model the cultural and informational environment of the formation of the moral experience of individuals. As a result, the work presents a project that provides a prompt response to urgent problems of both information and library services, and a factor in the formation of a socio-cultural space.
Education in training courses in communication-oriented fields

This article considers the role of independent work in distance learning. Independent work of students is an integral part in modern teaching and learning process. At the moment, independent work in percentage terms for full-time education is up to 50%. The indicator increases significantly to 90% for extra-mural form of study. When determining the content and methods of independent work of students in the educational process, one should take into account the expected level of a certain set of graduates’ competencies, which should be achieved during the period of study, and which should be provided by their skills to build their own trajectory of development in a professional and personal format. In this paper, special attention has been given to the potential of visualization as a traditional and, at the same time, exclusively modern means of methodological support.
Design of Recommendation Algorithm Based on Knowledge Graph

Abstract The commodity data in the shopping system contains a wealth of feature information, and different commodities are related by these features. Traditional collaborative filtering algorithms represent commodity data in a structured way, but they have issues such as low commodity similarity calculation accuracy, poor recommendation effect, and unfriendly recommendation results. The commodity recommendation algorithm based on the knowledge graph put forward in this paper initially automatically extracts the entities and entity relationships in the commodity data as the vertices and edges of the graph and then stores the commodity data in the Neo4j graph database. Finally, the similarity between commodities is calculated based on the graph’s path similarity, and the list of TOP-K commodities with the highest similarity is chosen for recommendation. The experimental findings indicate that the recommendation algorithm based on the knowledge graph can not only more accurately represent the similarity between commodities, but also that the graph structure can display the recommended path in a more user-friendly manner, and has better recommendation explainability as well as higher recommendation trust.
COV19-Dijkstra: A COVID-19 Propagation Model Based on Dijkstra’s Algorithm

The presence of the coronavirus, known as COVID-19, has prompted several researchers to study the mode of spread and the different defense mechanisms of the virus.As a reminder, obtaining a vaccine, for which much research is being conducted around the world, is a long and expensive process and it is unlikely that the pandemic can be treated in time.In this article, we present a new way to assess and limit the spread of the virus while trying to answer the following important questions: How to use the shortest path algorithm in a graph to analyze and better understand the spread of the virus?How to use the predictive power of the graph using the shortest path algorithm to find the relationships of a person who might be most at risk?The designed algorithm simulates how the virus spreads and infects people through the graph.Since the size of the collected COVID-19 data can reach a large volume over time and speaking of the graph concept, the NOSQL database including Neo4j which is a graph oriented NOSQL database is used for data collection, storage and processing.To enable the design and optimization of virus defense systems, this study proposes a feasible approach to quantify and predict the danger of a virus infection within a community.
Deep Learning Neural Networks with Auto-adjustable Attention Mechanism for Server Fault Diagnosis Under Log Data

Log data generated by the server describe the system operation information in detail. As a consequence, using algorithms to analyze the log data and realize self-help fault diagnosis has become a research hotspot. There are two kinds of popular automatic fault diagnosis. The first is the traditional machine learning algorithms such as random forest. They can quickly and accurately give the fault diagnosis results after learning the manually extracted feature information. The other is the algorithm using deep neural network, which can complete fault diagnosis end-to-end and obtain better results than the first when the amount of data is large enough. However, these two algorithms have obvious shortcomings: the log file is a time sequence, but machine learning algorithm is unable to capture sequence information. The deep learning algorithm based on recurrent neural network can capture the sequence information, but it lacks the understanding of the system, so the usable conditions are relatively harsh (a large amount of labeled data is required). Therefore, we innovatively combine the advantages of the two algorithms. Firstly, the feature is extracted by machine learning algorithm. Then, the attention mechanism layer is added to the neural network, and the output of machine learning is used as the basis for modifying the attention mechanism parameters. Our algorithm has been verified on the public data set, and achieves an improvement of nearly 3% in F1.
Prediction and Analysis of Infectious Disease Visit Data Based on DPC

Since the outbreak of the COVID-19 in early 2020, the prevention and control of infectious diseases has been raised to a higher level. However, tuberculosis still ranks in the forefront of the incidence rate of various infectious diseases in China. The tuberculosis epidemic has also brought great economic pressure and negative social impact to the society every year. Therefore, we have always been very concerned about how to effectively prevent and control the spread of tuberculosis. However, the diagnostic data of tuberculosis are often high-dimensional, huge, messy and difficult to be used effectively. How to extract knowledge from the data to help medical staff find the incidence trend of tuberculosis to assist decision-making has become a practical topic. In this paper, after clarifying and standardizing the original data, the density peak clustering (DPC) algorithm is used for deep mining. The knowledge is extracted through clustering analysis and visualization. Finally, analysis results can intuitively illustrate the effectiveness and practical research significance of this work.
<i>Flying Waiter’s Tray Demo Levels Up Again</i>

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Flying Waiter’s Tray Demo Levels Up Again. The Physics Teacher 1 March 2023; 61 (3): 239. https://doi.org/10.1119/10.0017370 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Deep Multimodal Habit Tracking System: A User-adaptive Approach for Low-power Embedded Systems

Abstract The pace of population ageing is increasing and is currently becoming one of the challenges our society faces. The introduction of Cyber-Physical Systems (CPS) has fostered the development of e-Health solutions that ease the associated economic and social burden. In this work, a CPS-based solution is presented to partially tackle the problem: a Deep Multimodal Habit Tracking system. The aim is to monitor daily life activities to alert in case of life-threatening situations improving their autonomy and supporting healthy lifestyles while living alone at home. Our approach combines video and heart rate cues to accurately identify indoor actions, running the processing locally in embedded edge nodes. Local processing provides inherent protection of data privacy since no image or vital signs are transmitted to the network, and reduces data bandwidth usage. Our solution achieves an accuracy of more than 80% in average, reaching up to a 95% for specific subjects after adapting the system. Adding heart-rate information improves F1-score by 2.4%. Additionally, the precision and recall for critical actions such as falls reaches up to 93.75%. Critical action detection is crucial due to their dramatic consequences, it helps to reduce false alarms, leading to building trust in the system and reducing economic cost. Also, the model is optimized and integrated in a Nvidia Jetson Nano embedded device, reaching real-time performance below 3.75 Watts. Finally, a dataset specifically designed for indoor action recognition using synchronized video and heart rate pulses has been collected.
Bagging and Boosting

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Smart Attendance Management System Using Encrypted QR Codes and Load Balancing

Over the recent years, lots of efforts have been undertaken to improve the existing monotonous and time consuming task of recording attendance in a classroom which includes systems such as biometric scanners and RFID devices. Taking into account various factors such as cost, ease of use and scalability, an encrypted QR code based attendance system has been proposed in this paper based on the students location to prevent proxy. The system will send every student within the boundaries of the classroom a unique QR Code in order to mark their attendance. Digital attendance reports and regular data backups help in enhancing the efficiency and reliability of the system. Considering the large number of requests coming through in an educational institution, a multi-server architecture with load balancing to distribute the heavy traffic has been proposed. A comparative study of four load balancing algorithms like round robin, ip hash, least connection and weighted round robin was analysed based on their performance on the system.