Reviewerly/exHarmony huggingface dataset:
Autoencoder

Earlier we introduced the neural network learning algorithm given the sample and its corresponding label. This type of algorithm actually learns the conditional probability P(y| x) given the sample x. With the booming social network today, it is relatively easy to obtain massive sample data x, such as photos, voices, and texts, but the difficulty is to obtain the label information corresponding to these data. For example, in addition to collecting source language text, the target language text data to be translated is also required for machine translation. Data labeling is mainly based on human prior knowledge. For example, Amazon's Mechanical Turk system is responsible for data labeling, recruiting part-time staff from all over the world to complete customer data labeling tasks. The scale of data required for deep learning is generally very large. This method of relying heavily on manual data annotation is expensive and inevitably introduces the subjective prior bias of the annotator.
Ivan Bunin and E.N. Chirikov: Dialogue and Competition (Social and Metaphysical in the Artistic View of the World)

The paper attempts to compare the Ivan Bunin and E.N. Chirikov’s artistic views of the world which revealed a number of similar stages in their creative evolution that do not coincide chronologically, but clearly indicate that a long dialogue existed between the writers. Despite the fact the origins of the artistic search for writers initially lay in different planes (one of them paid more attention to the understanding of philosophical problems, the other focused mainly on the image of social contradictions and ideological disputes in Russian society), the vectors of creative interests of Bunin and Chirikov were quite close. Both of them focused on the issues related to understanding the fate of the rural world and the peasantry, the contradictions of the Russian national character, the causes of social evil and injustice and the revolutionary catastrophe of 1917, the meaning of writing and the mystery of love. The revealed points of intersection in the legacy of both authors allow us to significantly expand the idea of personal and creative contacts of the first Russian Nobel laureate with contemporary realist writers, primarily colleagues from the literary association “Sreda” and the book partnership “Znanie”.
Ontology for Product Lifecycle Management in the Oil &amp; Gas Turbomachinery Industry

Abstract In the industrial contexts of the digital era, data, information and knowledge should seamlessly flow throughout the product lifecycle, and be available at any time and to any agent acting in the value creation stream. PLM is one of the enabler of this scenario, striving to scale the “cobbler” model — where people, information, resources and processes are perfectly integrated — to modern industrial realities operating with multi-disciplinary teams and world-wide dispersed internal and external resources. Yet, despite many industrial realities has invested in the institutionalization of a PLM system, still approx. 60% of time in the value creation process is wasted in searching and waiting for data, data translation, working with wrong data or reinvention of the existing knowledge. After having analyzed the above scenario in the context of a real big industrial reality operating in the turbo-machinery production for Oil &amp; Gas and Engery markets, the present paper aims to propose a solution introducing a working approach based on the modeling of the knowledge domain relevant to a product and its data model by an OWL-DL ontology, and to present the relevant preliminary results. The final target will be the establishment of an ontology-based domain model as the foundation for a digital, human-machine interoperable, product knowledge and data lifecycle management system to bridge the diverse agents operating in the Product Lifecycle Management.
Protecting geometric quantum discord via partially collapsing measurements of two qubits in multiple bosonic reservoirs

We study the dynamics of geometric quantum discord (GQD) between two qubits, each qubit interacting at the same time with K independent multiple bosonic reservoirs at zero temperature. In both weak and strong qubit–reservoirs coupling regimes, we find that the increase of the number K of reservoirs can induce the damped oscillation of GQD, and enhance the memory effects of the overall environment. And the Hilbert–Schmidt norm GQD (two-norm GQD) is always smaller than the trace norm geometric quantum discord (one-norm GQD). Therefore, the one-norm GQD is a better way to measure the quantum correlation. Finally, we propose an effective strategy to improve GQD by using partially collapsing measurements, and we find that the protection effect is better with the increase of the weak measurement strength.
Decision-Making and Impact of Blockchain on Accounts Receivable Financing

This paper mainly discuss the decision-making and influence of blockchain technology on accounts receivable financing in supply chain finance. In recent years, supply chain finance has become an important way to alleviate the financing difficulties of small and medium-sized enterprises, but problems such as high financing costs and frequent fraud still has not solved. As the underlying technology of digital currency, blockchain has the characteristics of immutability, traceability and smart contract, which can help supply chain finance solve the above problems. We takes supply chain finance Accounts Receivable Financing (ARF) as the research, sorts out the ARF transaction process with or without blockchain technology, and then we investigate two collaborative accounts receivable financing decision-making schemes: accounts receivable (AR) financing and blockchain-embed accounts receivable (BAR) financing. The mean-variance method is used to discuss the optimal performance of the two financing decisions. The results show that the blockchain-embed accounts receivable financing leads to lower financing risks, and when the cost of credit investigation and additional benefits are high enough, the use of blockchain can bring higher expected benefits to financing participants.
OUP accepted manuscript

In this gorgeously written and theoretically robust book, Alda Balthrop-Lewis has given Thoreau scholarship and the broader field of environmental ethics one of the most important works about America’s so-called “patron saint of environmentalism” to appear in many years. Thoreau’s Religion joins a renaissance of scholarship that has followed in the wake of Thoreau’s bicentennial in 2017. Since the late Stanley Cavell’s various philosophical meditations on Thoreau, I can think of few others who have done so much to restore the vital importance of Christian thought and Biblical scripture behind Thoreau’s most well-known work, Walden; or, Life in the Woods, originally published in 1854. Some of Balthrop-Lewis’s insightful excavations of the biblical intertextual echoes that punctuate Walden make Thoreau’s Religion alone worthy of our attention; that it has so much more to offer is a testament to Balthrop-Lewis’s knack for having written something that is much more than just another historically situated study of Thoreau that embeds him in various streams of influence, Christian or otherwise. This is a book that cogently demonstrates why and how Thoreau (still) matters for the Anthropocene—that he remains a useful interlocutor in our present, someone who can speak to the twinned crises of climate calamity and our ongoing dysfunctional politics.
Internal resources for improving the higher education quality

The article analyzes the resources of the modern higher education system in the direction of improving the quality of student training for professional activities, including the railway transport. Gaps in educational programs between professional disciplines (technological) module and general subjects modules of the basic professional educational programs of Federal State Educational Standard of higher education. The identity of the structure of professional and any other (cognitive, household, socio-cultural, etc.) human activity is determined. The connection between rational activity and technological thinking is established. The author substantiates the proposals for the development of educational disciplines of the professional module and the module of humanitarian disciplines, based on the formation of rational technological thinking on the design and implementation of activities, which can be based on the orientation of students’ individual work to finding solutions to problems for life-sustaining activities.
THE USE OF WEB QUEST TECHNOLOGY IN LUNGUISTIC EDUCATION IN HIGHER SCHOOLS

The article presents a web quest as an interactive learning technology. The main characteristics of the quest are considered; a scenario of accomplishing an interactive task by students-architects in the study of Moroccan Intangible Cultural Heritage at the sessions of English and results are given.
Multiview Face Automatic Recognition Method Based on Geometric Characteristics

At present, most face recognition methods are aimed at frontal faces, but there are still great difficulties in multiview face detection, and there are few effective methods. In this paper, considering the multiview situation in face recognition, a multiview face automatic recognition method based on geometric characteristics is proposed. First, multiview face image preprocessing is performed, and then the skin color model is established. After processing, the elliptical area of the face is determined to realize face detection. Then, the geometric characteristics of the multiview face are extracted, and finally, the minimum distance classifier is established. According to the extracted multiview people face geometry features, complete multiview face automatic recognition is performed. The results show that compared with knowledge-based methods, template matching-based methods, and statistical learning methods, the research method has higher recognition accuracy and shorter running time, indicating that the research method is simple and easy to implement and is more suitable for real-time needs. Researched face recognition technology.
Prediction method in student relationship network based on Big Data Mining

In some universities, there will often be a Matthew effects problem of student groups, can scientifically explain this phenomenon, and make the best management prediction program, this is a hot problem of the school. By building a student group interaction's social network, the paper combines complex network characteristics with big data mining technology to establish prediction method to achieve predictions in the development trend of key nodes in the network. The prediction method mainly consists of two steps. The first is to use time series analysis to find groups with poor performance; it is used to explain the Matthew effects phenomenon in the social relationships network with uncertain. The second according to the first step's research, build predictive algorithms in complex networks, and give some suggestions in moral education and style education. The paper in theory puts forward a new prediction algorithm on the complex network, which provides targeted strategies for the management of college student education in application.
Conclusion and Future Perspective

Although we have seen the development of the continuous-variable quantum computation using the optical system throughout this book, there are still many hurdles we have to overcome. In this chapter, we will review what we have discussed in this book and give future perspectives of what must be done to reach the ultimate goal of realizing the quantum computer.
Conclusions

Throughout this book, deduction has been examined and discussed from many angles and perspectives. However, one question has remained conspicuously unaddressed until now: Is deduction a correct, reliable method for reasoning? In other words, is deduction justified (Dummett, 1978)?
Key-Points Based Enhanced CMFD System Using DBSCAN Clustering Algorithm

The proposed CMFD system in the last chapter was robust against different geometric transformation attacks but was unable to detect multiple forgeries present in the image [1]. To overcome this limitation, in this chapter, an improved system for multiple copy-move forgeries detection using SIFT key-points and DBSCAN clustering algorithm is proposed.
Illusory correlation

When observing and learning about the environment, the ability to identify correlations between causes and effects constitutes a basic module of adaptive cognition. Although humans and animals can assess environmental correlations quite accurately, numerous experiments identified conditions that lead to distorted subjective correlation estimates. The illusion of perceiving a correlation that does not actually exist is termed an illusory correlation. There are different types of illusory correlations that reflect different underlying cognitive processes. We outline four different theoretical explanations that have been advanced to account for illusory correlations: expectancies, unequal weighting of present versus absent information, unequal sample size (e.g., more opportunity to learn about a majority than about a minority), and the alignment of skewed base-rate distributions (pseudocontingencies). These different types of illusory correlations can occur independently or in conjunction. As illusory correlations, much like other illusions, may be reflective of adaptive intelligence, one should be cautious in drawing premature conclusions about the inherent irrationality of illusory correlations.
A Robust Visible Authenticated Video Watermarking Based on One-to-One Compound Mapping

We implemented in this letter a comprehensive and highly authenticate visible technique for the use of video watermarking as an application for copyright protection for many governments. We have put forward the use of two methods of watermarking with one-to-one compound image maps, which allows us to retrieve any video without loss, for lossless video watermarking. We have also proposed using a single-to-one compound image. This paper uses both the full colored translucent and the Opaque mono chrome images. There is a repetitive development of two fold more compound mapping to get more conventional visible watermarks. Steps to protect against hackers were taken.
Accelerated on-Chip Algorithm Based on Semantic Region-Based Partial Difference Detection for LiDAR-Vision Depth Data Transmission Reduction in Lightweight Controller Systems of Autonomous Vehicle

LiDAR sensors are one type of sensor used in autonomous driving vehicles that obtain distance data through the flight time of light. A LiDAR sensor can measure data at high speeds, and the precision of the data is higher than with other sensors. A large amount of data per sensing time is transmitted from sensors. Autonomous driving vehicles use man electronic devices, so the data channels they use and the domain control unit resources that control the system are limited. In this environment, if LiDAR sensor data can be reduced without compromising the original data, it can have a quite positive impact on autonomous vehicle systems. In this paper, we propose a differential partial update for data reduction of LiDAR sensors and a semantic detection to eliminate the resulting noise and increase the reliability of the data. The sensor processor extracts only the changed parts of the continuous distance data, excluding the same parts, and transmit them to the host. The high-difference noise is eliminated by filtering through a window-sliding operation. Semantic detection marks only parts that change and detects movement in the field of view. Simple differential partial updates reduce the amount of data by 59.31% based on a simple case. A semantic detection partial update can reduce the amount of data by 83.41%. This process can also reduce computing time by 61.36% with graphics processing unit acceleration.
world.me

In 2019 the total accumulated digital data reached 4.4 trillion gigabytes; Facebook alone produced 4 million gigabytes, which includes 350 million photos and 100 million hours of video watch time, 65 billion messages via WhatsApp, and 95 million photos or videos on Instagram.11. A Day in Data (2019). Raconteur.net. Raconteur, 2019, https://www.raconteur.net/infographics/a-day-in-data/. With the vast volumes of data accessible to users, the necessity for filtering agents, which separate relevant data from noise, becomes clear for both the user and the businesses aiming to target specific audiences. The lack of regulation and misalignment of values between businesses and users, however, has led to poor design and implementation of filtering agents, which have been shown to be able to manipulate, extremize, and restrict users, even in situations and contexts beyond access to information and media. This paper aims to map the current state of interaction and dynamics between stakeholder groups and the consequences that arise from the current state and its perpetuation.
Sign Language to Voice Translator Using Tensorflow and TTS Algorithm

In this system, it helps to convert the sign language to voice with hand gestures understanding and capture the motion of hands. Many sign languages are natural languages, but they are differing in construction from oral languages used in proximity to them, and are employed mainly by deaf people in order to communicate. It is based on a Raspberry Pi with a camera module and is programmed in Python with the Open-Source Computer Vision (Open CV) library as a backend. A built-in image processing algorithm on the Raspberry Pi is named gesture that tracks an object (a finger) with features pulled out. The main purpose of a gesture recognition system is to establish a connection between a human and a computer control system. Camera is used in this system and it captures the various gestures of hands. Various algorithms are taking place in processing of image. First preprocessing of the image takes place. Finally, the sign is identified by using Tensor flow algorithm and the outcome result as a voice using TTS algorithm. Open CV Python is implemented in this system. Various libraries are used in this system.
Malicious Website Detection Through Deep Learning Algorithms

Traditional methods that detect malicious websites, such as blacklists, do not update frequently, and they cannot detect new attackers. A system capable of detecting malicious activity using Deep Learning (DL) has been proposed to address this need. Starting from a dataset that contains both malevolent and benign websites, classification is done by extracting, parsing, analysing, and preprocessing the data. Additionally, the study proposes a Feed-Forward Neural Network (FFNN) to classify each sample. We evaluate different combinations of neurons in the model and perform in-depth research of the best performing network. The results show up to 99.88% of detection of malicious websites and 2.61% of false hits in the testing phase (i.e. malicious websites classified as benign), and 1.026% in the validation phase.
Evaluation Method of Damaged Status of Offshore Structures Based on Fuzzy Mathematics

In view of the current serious interference in the evaluation of the damage status of offshore structures, the evaluation accuracy is not good. A method for evaluating the damage status of offshore structures based on fuzzy mathematics is proposed. The damage characteristics of offshore structures are collected and identified, combined with fuzziness. The mathematical algorithm evaluates the damage state and builds an evaluation model. Finally, it is confirmed through experiments that the method of evaluating the damage state of offshore structures based on fuzzy mathematics has high practicability in practical applications.
Philosophy of education in modern conditions of society development

The subject of this research is the philosophy of education and its reflection on the educational process. Based on the analysis of the works of predecessors, the author presented a new structure of the philosophy of education, which enriches the understanding of its subject, goals and research methods. The author presented the philosophy of education as a pyramid, at the base of which there are generalizing provisions about man as a subject and object of research, accumulated in philosophical anthropology. The first floor of the pyramid is occupied by psychology, as a science that studies the laws of the emergence, development and functioning of the psyche. The “pyramid” is crowned by pedagogy. The author used the dialectical method, system-structural, structural-functional, as well as methods: comparison, analysis and synthesis. The main conclusions of the study are proof that the philosophy of education in its new understanding is not only theoretical comprehension of the foundations and manifestations of the educational process, but also practice, the direct embodiment of theoretical developments in education in everyday life. Using a historical and philosophical analysis, the author showed that the philosophy of education not only depends on the state of development of social philosophy (and philosophy in general), but also through its methodological apparatus, implements established philosophical (worldview) paradigms in various pedagogical practices.
Design and Implementation of Analysis System for Popular Scenic Spots

Modern society has a more urgent need to go out and play. In order to better analyze the scenic spot information, a crawler-based data analysis software for popular scenic spots is hereby designed, which mainly includes three modules: GUI(Graphical User Interface) module, crawler module, data analysis and visualization module. All programming is implemented in Python language. The experimental results show that the system can run stably and the analysis results are accurate, helping people to better choose tourist attractions.
An Overview of Natural Language Processing Models for Abstractive Text Summarization.

Enormous amount of data is created on the internet every passing second. The International Data Corporation (IDC) projects that the total amount of digital data circulating annually around the world would sprout from 4.4 zettabytes in 2013 to 180 zettabytes in 2025. To manage this data, process it, and use it to gain valuable insight to apply in different domains; it is important to summarize long pieces of text and extract their gists in a short amount of time efficiently without much human intervention. Natural Language Processing(NLP) enabled text summarization through different models and algorithms over the years. NLP is amidst a golden era with extremely powerful models already in the public domain. This paper aims at highlighting the evolution of abstractive text summarization models from RNN based Seq2Seq models (2013) to GPT-n series models (2020).