Reviewerly/exHarmony huggingface dataset:
한국어와 중국어 인칭 표현의 계량적 분석

Personal pronouns and other noun expressions could be considered as person expressions. This study aims to organize concrete examples which were extracted from Korean and Chinese corpora and analyze the corpus data to accurately identify the usage frequency of personal pronouns and substitute expressions, and reexamine the features and causes of their usage based on the theories of language typology. For this purpose, by using Korean and Chinese corpora with empirical techniques, the actual frequency of appearance, using rate, and distribution of Korean and Chinese person expressions were investigated. The characteristics of them were inductively summarized based on the results of quantitative and corollary analysis of corpus. When summarizing, it is necessary to use qualitative research methods to interpret the three research questions and to identify the cause of the analysis results. If these three questions are proved, the relation of personal pronouns and substitute expressions can be regarded as a verbal existence. (Shanghai International Studies University)
Asymptotic analysis for $$O_N^+$$-Temperley–Lieb quantum channels

Studies on conservation of quantum symmetries have been initiated by recent papers (Brannan et al. in Commun Math Phys 376(2):795–839, 2020; Lee et al. in Quantum channels with quantum group symmetry, 2020). We, in this paper, investigate how quantum symmetries can be applied to study additivity questions for covariant quantum channels, particularly for the so-called $$O_N^+$$ -Temperley–Lieb channels. Their information-theoretic properties were analyzed in Brannan et al. (Commun Math Phys 376(2):795–839, 2020), but the associated additivity questions remained open due to highly nontrivial structures. We present a new approach to analyze $$O_N^+$$ -Temperley–Lieb channels with small perturbations in terms of Bures distance within the asymptotic regime $$N\rightarrow \infty $$ and apply continuity bounds of classical capacity, private classical capacity and quantum capacity to study strong additivity questions.
The need for AI to optimize dual-use PV installations to extract maximum value

There has been increasing application of solar when combined with shared land use. Current examples are PV systems floating on water, and also dual-use land where agriculture is interwoven with PV panels. There are clearly challenges with these types of implementations in that either the geography makes the installation more difficult, or the activity requires a need to share both the land and the light "harvesting" of photons. Because of these challenges, it is necessary to optimize the implementation of the PV and other land-use to extract meaningful value beyond just a demonstration project. But the diversity and complexity of the configurations do not lend themselves to simple analysis. Given this complexity, it is necessary to develop flexible algorithms which can take multiple variables into account where the data may be from multiple complex sources. This is the very configuration where Advanced Neural Net applications can assist and guide the installation and management of the combined land-use activities.This paper will describe the following: (1) the key advantages of AI/ML, along with the current issues, (2) the need for transferrable algorithms development, and (3) the overall methodology for implementation and extension to the broader PV dual-land use segment. Specific data will be presented on the novel AI/ML proposed. A key attribute with this novel AI/ML is one can scale and transfer the template developed without re-customization. The key point of the paper is that – as with any niche application of PV and especially in a dual-use mode, there are substantial challenges which must be overcome, and the use of AI/ML will be a useful tool to exploit the advantages and elevate the application to a more broadly used mode of PV.
Research of Fault Diagnosis Method for New High-speed Railway Single Power Distribution Station Based on Bayesian Network

This paper presents a Bayesian network reasoning method for fault diagnosis of high-speed railway single power distribution station. In this method, the fault model of Bayesian network is established according to the causality of fault element, protection and circuit breaker, using the alarm information received by the high-speed railway distribution SCADA system, the fault element and fault action protection or circuit breaker can be inferred and diagnosed on the model. This paper obtains the actual electrical wiring and circuit breaker protection configuration of a high-speed railway distribution station through investigation, Bayesian network modeling and reasoning were carried out on GeNle software platform, The feasibility and correctness of the method are verified.
Zebra Crossing Detection and Time Scheduling Accuracy, Enhancement Optimization Using Artificial Intelligence

Zebra crossing detection is a fundamental function of the electronic travel aid. It can locate the zebra crossing and estimate its direction to help the visually impaired to cross the road safely. In contrast to the conventional methods, a regression approach is adopted to detect zebra crossing based on convolutional neural networks. Specifically, a fixed‐size window slides across the image captured at the intersection. The image patches are sequentially fed to the logistic regression model to identify the zebra crossing. Then the image patch of zebra crossing is fed to the regression model to predict the direction. The parameters of models are optimized by the ANN back propagation algorithm before predictions. Compared with existing methods, the proposed method can improve the precision‐recall performance of the zebra crossing identification and reduce the root mean square error of predicted directions.
Paragon Bots

Paragon is a MOBA-type game developed by Epic Games, built with Unreal Engine 4 (UE4). Relatively late in the project a decision was made to add artificial intelligence (AI)-controlled players into the game. This chapter describes some of the extensions AI programmers made to the vanilla UE4 AI systems as well as some of the simple systems tailored specifically for Paragon player bots. In Paragon, players control heroes. A hero is a character on a team that can use abilities to debuff or deal damage to enemy characters and structures, or to buff his or hers own teammates. It is important in a MOBA for heroes to understand positioning in a lane based on areas of danger and safety. An experienced AI developer might be surprised that all Paragon bots use the same behavior tree. With the time and resources constraints we were under, we could not afford to develop separate trees for every hero, or even every hero type.
Further Examination of YouTube’s Rabbit-Hole Algorithm

In the past couple of years, YouTube has been criticized for its algorithm radicalizing users. The purpose of this research is to further refute the claims that YouTube’s algorithm radicalizes people. An analysis of the previous research by Mark Ledwich will be used as a base. Anecdotes gathered by the Mozilla Foundation will also be analyzed. An overview of the problems with the algorithm, as stated by a developer, will be introduced. All of this is to illustrate that the current algorithm is a “rabbit-hole” and is not healthy for the user. Rather, the algorithm does a good job at prioritizing a user’s preferences for content which could lead to unintended problems. The unintended problems are what need to be fixed with the YouTube algorithm. Potential solutions are provided and could be used as suggestions for YouTube to investigate to improve the algorithm.
Design Automation of Single Photon Counting Method for Quantum Random Number Generation

EDU-QCRY1 is one of the quantum key distribution devices in order to generate quantum random bit. In this paper, we conduct a mechanism to produce a key sequence based on single photon counting using EDU-QCRY1 Single Photon Detector module for receiving data which can be acquired using the receiver’s device automation using a Light Dependent Resistor parameter. The design of quantum random generator is using protocol of Quantum Key Distribution (QKD) then compared to true table of the BB84 protocol. The average speed of the quantum bit in the automation device is 0.0604 seconds with estimation for 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> quantum random bit around 16 hours experiment. Within the random bits, some difference occurs when the sender and receiver have a different basis. This research proves the efficiency of automation bit storage on the EDU-QCRY1 Single Photon Detector for generating a quantum random bit and has the valid result as the experiment in performing key distribution simulations using EDU-QCRY1 devices.
Prediction of Weather Forecasting by Using Machine Learning

Weather forecasting has become important now a day because of varying climatic conditions around the world. Many technologies have been introduced to predict the weather whose accuracy is around 70%.Weather forecasting is used in machine learning. It is a powerful technique to predict the weather with more accuracy. Weather dataset is collected and analysed and algorithms on it to predict the weather. Using a Back Propagation Neural Network the error rate becomes less and many factors that are involved to predicting the weather gives the accurate results. To compare and evaluate the performance of above model and the programming was carried out using Visual Studio as a tool.
The Modularity of Inconsistent Knowledge Bases with Application to Measuring Inconsistency

Inconsistency is one of the important issues in knowledge systems, especially with the advent of the world wide web. Given a context of inconsistency characterization, not all the primitive conflicts in an inconsistent knowledge base are independent of one another in many cases. The primitive conflicts tightly associated with each other should be considered as a whole in handling inconsistency. In this paper, we consider the modularity of inconsistency arising in a knowledge base, which provides a promising starting point for parallel inconsistency handling in very large knowledge bases. Then we propose a modularity-based approach to measuring inconsistency for knowledge bases.
Explanatory Rule Generation for Advanced Driver Assistant Systems

Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.
Digital sets: power and subjectivity in the era of new media

This paper is a reply to Evgeniy Maslanov’s article “Challenges of Digitalization for Technogenic Civilization”. Emphasizing the total impact of digitalization on modern society, the author tends to agree with E. Maslanov that at the moment human existence is proceeding in a hybrid mode, for which the “real-virtual” dichotomy seems irrelevant. Being in this mode, a person leaves a digital footprint on the network, which forms her or his digital identity. The latter turns out to be “transparent”, vulnerable to instrumental influence from outside. Digital footprints are totalized into gigantic amounts of big data that can be used for social manipulation, as evidenced by the Cambridge Analytica case study. The manipulative activity of Cambridge Analytica fits into the context of the post-truth policy, which tends to ignore the facts in favor of false information that brings pragmatic benefits to the subject of the statement. This article argues that the complexity of social dynamics characteristic of the digital age is balanced by new means of description and analysis that enable us to better understand and navigate modernity. Nevertheless, a total description and thus stabilization of social reality seems impossible, given its advantageously network nature. The flip side of digitalization includes increased control within labor relations, blurring the boundaries between work and home as blurring the distinction between the “real” and the virtual. Along with it, the digital space is becoming a space of emancipation, where individuals-singularities form sets freed from violent unity (in the terms of Michael Hardt and Antonio Negri). The multitudes unfold their subjectivity in opposition to the social system, to which they oppose networked col-lective action and solidarity. The article concludes about the dialectical role of digitalization, which can serve as both a means of control and a means of emancipation. Thus, the challenges of digitalization seem not so much a technological call as a social one.
Technique for Estimating Position of the Pirate identification using Machine Learning Algorithms from an image

Information era, piracy converted a pivotal issue for the survival of the movie industry. Because of freebooting, the cinema industry suffers a substantial quantity of financial lose. The important task is to govern the position of the pirated person in the theater. An opinion to determine the image's tilt angle has been considered and interpreted for the transferring of data. This process can be used to govern the location of the person in the cinema theater. This planned work uses the Image steganography, Contour reframing, Deep Image prior and the PEM technique to find the image's tilt angle. Experimental output results show the validity of the future work.
SCIENTIFIC AND METHODOLOGICAL BASIS FOR THE PREPARATION OF AN ELECTRONIC EDUCATIONAL AND METHODOLOGICAL COMPLEX ON THE TOPIC «THE STRUCTURE OF THE ATOM»

The article describes the problem of low availability and inaccessibility of new information technologies in the Kazakh language in universities, analyzes the pros and cons of electronic textbooks based on theoretical data. The textbooks used by teachers show the effectiveness and usefulness of multimedia, in order to make them more valuable, it is necessary to add digital resources, such as academic games and interactive websites. The questions of the popularity of textbooks compiled using a computer are touched upon, the analysis and testing of programs for compiling electronic textbooks is carried out. The importance of methodological support of the textbook in the creation of electronic information tools is analyzed. Automation of all the main stages of training with electronic textbooks is provided before monitoring the content of the educational material and making recommendations for final grades, thanks to which all mandatory educational materials are replaced by bright, interesting, with a share of intelligence game moves, i.e. widely used multimedia, including interactive and voice. For students and teachers of chemistry, the authors have compiled an electronic textbook in the Kazakh language, the effectiveness of the textbook has been analyzed and studied in accordance with theoretical data and surveys, according to the results of the study, positive indicators of the assimilation of educational material have been demonstrated.
A New Two-Step Calibration Method for Structured Light Systems Using Block Gages

The calibration process is a crucial step for measuring systems due to their inherent errors and tendency to drift over time. Block gages are one of the most widely used calibrators. Advanced optical systems are continuously being developed to cope with the challenge of reconstructing different object dimensions with minimum error, cost, and time. In the present study, a two-step calibration method for structured light systems (SLSs) is proposed. A calibration pattern and set of stepped block gages are used to calibrate the SLS in the first and second steps respectively. Using block gages opens the prospects for SLSs to be more widely used in several metrological applications. The calibration curve for a SLS is obtained using the proposed method. The proposed calibration method was validated by reconstructing a test object and evaluating its size. Experimental results show the effectiveness of the proposed method. Using the proposed calibration method, measurement error was significantly reduced by 71%.
An Approximation Method of Parallel Processing System with Big Data

Big data has been amassed and observed at bursting dimensions. The immense capacity of unstructured data from digital society has intensified at an unimaginable bound, motivating processing power and data pre-processing. The conventional simulations to manipulate gigantic data can be problematic as they cannot be practically used. The decision-maker is experiencing these problems from all activities in the workplace community, enterprise, and scientific experiments. The troubles arise from the process of data collection, the way to prepare a suitable storage space, and the improvement of processing speed. These burdens are growing for big data processing. Besides, there are some points such as data conflict, severance, confidentiality, extensibility, and time-series. Parallel processing system is a key to quantify metrics, and to investigate time-series. Recently developed simulation, so called Massive Online Analysis, gives results from multiple datasets and the results align with ones from the approximation. Cost-effective and speedup metrics are examined to confirm the performance of parallel processing system.
Parameter Dependencies and Optimization of True Random Number Generator (TRNG) using Genetic Algorithm (GA)

In the hostile environment, use of pseudo-random numbers in encryption algorithm has become difficult due to increased computing power of attackers. To overcome from the hackers true random number generator solving by giving unique random sequence. Authors recommend based on experiment to add parameters dependencies of analog Phase-Locked Loop (PLL) based on TRNG, and it also tries to optimize few parameters using Genetic Algorithm (GA). The proposed approach selects optimum values for different parameters and increases flexibility, resource utilization, throughput. Digital architecture for optimized TRNG is obtained using Altera platform. Implementation the optimized TRNG on ALTERA QUARTUS-II DE0 board gives enhancement R and S parameters by 42.18% and 38.67% respectively.
PSGU: Parametric self-circulation gating unit for deep neural networks

Activation functions are of great importance for the performance and training of deep neural networks. High-performance activation function is expected to effectively prevent the gradient from vanishing and help network converge. This paper provides a novel smooth activation function, called Parameterized Self-circulating Gating Unit (PSGU), aiming to train an adaptive activation function to improve the performance of deep networks. Compared with other works, we propose and study the self-circulation gating property of activation function, and analyze its influence on the signal transmission in network by controlling the flow of information. Specifically, we theoretically analyze and propose the initialization based on PSGU, which adequately explores the properties in neighborhood of the origin. Finally, the proposed activation function and initialization are compared with other methods on commonly-used network architectures, the achieved performances of using PSGU alone or combining with our proposed initialization are over par with the state of the art.
On the automatic text detection and recognition algorithms for maritime images

In view of the increase in illicit maritime activities like piracy, sea robbery, trafficking of narcotics, immigration and illegal fishing, an enhance of accuracy in surveillance is essential in order to ensure safer, cleaner and more secure maritime and inland waterways. Recently, the field of deep learning technology has received a considerable attention for integration into the security systems and devices. Convolutional Neural Networks (CNN) are commonly used in application of object detection, segmentation and classification. In addition, they are used for text detection and recognition, mainly applied to automatic license plate recognition for the highway monitoring, rarely to the maritime situational awareness. In the current study, we propose to analyse the practical feasibility of applying an automatic text detection and recognition algorithm on ship images. We consider a two-stage procedure that localizes the text region and then decodes the prediction into a machine-readable format. In the first stage the text region in the scene is localized with computer-vision based algorithms and EAST model, whereas in the second stage the predicted region is decoded by the Tesseract Optical Character Recognition (OCR) engine. Our results demonstrate that the integration of such a feature into a vessel information system will most likely improve the overall situational awareness.
Artificial Intelligence Decision and Validation Powered Smart Contract for Open Learning Content Creation

Educational learning contents, usually developed by instructional designers to provide learning specification and impart knowledge and skills to learners, are often only updated or changed in the yearly course revisions. This can be presented with some challenges, especially to courses on emerging subjects and catering to diversified learners, which includes the ability to provide adaptive and updated learning contents to the learners, and the opportunity to continually incorporate feedback from all stakeholders. Characteristic of Blockchain, such as secure, privacy, and consensus makes the technology adaptable for open and collaborative applications. In this paper, we propose a framework that uses artificial intelligence with blockchain smart contracts to support an open platform that gives stakeholders flexible access to content creation where individual learners can obtain extra contents as needed and updated knowledge can be shared and built upon easily to enrich the learning contents.
Improving Caption Consistency to Image with Semantic Filter by Adversarial Training

Benefiting from the larger-scale dataset, image captioning has achieved remarkable success to generate more humanlike captions. However, for the specific tasks (e.g., stylized image captioning) trained with the small-scale dataset, the visual objects and semantic diversity are generally insufficient. Although the generated captions are suitable, it still lacks in depicting the image with comprehensive visual objects, which leads to a reduction in the fluency and accuracy expressions. To address this issue, we proposed an image captioning system based on an adversarial training strategy. To improve the accuracy, a semantic filter module is implemented to obtain the informative context from the semantic vectors. With a two-separated LSTM architecture, our model learns the image features and semantic vectors at the global and local levels. Through adversarial training, the generated caption can be integrated with accurate information and expressed in a fluent style. Experiment results show the outstanding performance of our approach to capture semantic knowledge on the FlickrStyle10K dataset. The linguistic analysis demonstrates our model succeeds in improving the accuracy and fluency of generated captions.