Reviewerly/exHarmony huggingface dataset:
High-resolution dynamic consistency analysis of photonic time-delay reservoir computer

We numerically investigate a time-delayed reservoir computer architecture based on a single-mode laser diode with optical injection and optical feedback. Through a high-resolution parametric analysis, we reveal unforeseen regions of high dynamic consistency. We demonstrate furthermore that the best computing performance is not achieved at the edge of consistency, as previously suggested in a coarser parametric analysis. This region of high consistency and optimal reservoir performances is highly sensitive to the data input modulation format.
Gamification of the universities educational process

Training of highly qualified specialists in modern conditions requires gamification of the educational process itself in higher education. The article is aimed at substantiating the need to expand the field of game practice in the system of higher education by including gamification of the educational process of universities and outlining the conceptual foundations of the author’s innovative method of organizing the educational and educational process in the system of higher education VIRTUOZ (‘Variable Game Solution of Difficult Educational Tasks’). The article provides a detailed review of the subject field of scientific publications of domestic and foreign researchers on games and game practices in higher education, which showed that they comprehensively consider gamification in higher education, with attention to various types of games. However, the problem of gamification of the educational and upbringing process itself, the tasks solved by it before the author, no one in the Russian Federation and abroad has investigated and implemented in practice. The author substantiates that it is advisable to gamify the organization of educational activities of the university in order to make this process meet the requirements of the transformation of national education, deeper and more justified. The VIRTUOZ method is designed to help in the effective formation of competencies in solving the corresponding specific task: writing a WRC, undergoing practical training and others. The uniqueness of the project lies, first of all, in the fact that it involves the comprehensive interaction of all participants in the educational process; is implemented on the basis of the results of a sociological survey on a particular problem to identify the most difficult tasks; simulates the game situation, based on the specifics of the university, the subject and other factors; ensures effectiveness for all participants in the educational process. The VIRTUOZ project makes extensive use of end-to-end technologies and digital tools. The VIRTUOZ method opens the way for the development and implementation of other innovative game methods for organizing the educational process in higher education.
Video‐based fast image set classification for <scp>IoT</scp> monitoring system

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. And the video monitoring system is an important basic work in the IoT system. However, many existing video monitoring system usually use feature embedding method to learn more discriminative feature representation, while this manner is very time‐consuming. And the learned features usually do not match the subsequent classifiers. To solve these issues, this paper proposes a new video‐based fast image set classification framework, which consists of fast feature learning part and representation learning based classifier part. Extensive experiments on several well‐known benchmark datasets demonstrate the effectiveness and efficiency of the proposed framework.
Evaluating digital video transcoding for forensic derivative results

Video data received for analysis often come in a variety of file formats and compression schemes. These data are often transcoded to a consistent file format for forensic examination and/or ingesting into a video analytic system. The file format often requested is an MP4 file format. The MP4 file format is a very common and a universally accepted file format. The practical application of this transcoding process, across the analytical community, has generated differences in video quality. This study sought to explore possible origins of the differences and assist the practitioner by defining minimum recommendations to ensure that quality of the video data is maintained through the transcoding process. This study sought to generate real world data by asking participants to transcode provided video files to an MP4 file format using programs they would typically utilize to perform this task. The transcoded results were evaluated based on measurable metrics of quality. As the results were analyzed, determining why these differences might have occurred became less about a particular software application and more about the settings employed by the practitioner or of the capabilities of the program. This study supports the need for any video examiner who is transcoding video data to be cognizant of the settings utilized by the programs employed for transcoding video data, as loss of video quality can affect analytics as well as further analysis.
Detection of Credit Card Fraudulent Transactions Utilizing Machine Learning Algorithms

Over the last several years, fraudulent credit card transactions have increased significantly. For financial institutions, credit card fraud is a significant problem, and reliable detection of fraud is generally challenging. According to annual study conducted in 2021, more than 50% of Americans have had experienced credit and debit card frauds, and beyond 13% of individual who utilize these cards do so on a regular basis. This corresponds to 127 million Americans that have roughly once been the victim of credit card fraud. Using the traditional technique, it is highly challenging and time-consuming to detect such fraud occurring across a large database. An effective technique to handle this kind of issue is by using technology such as AI and machine learning to create a fraud detection system which is well automated to identify and categorize such occurrences. This paper presents six supervised machine learning algorithms as Naive Bayes, SVM, Random Forest, KNN, Logistic Regression and XGBoost which is used create a classification model that correctly identifies such fraudulent. After evaluating all these algorithms, finding shows that Support vector Machine is the most reliable model among all in terms of classifying fraudulent and non-fraudulent transactions gaining the highest accuracy results.
Chinese Named Entity Recognition Based on Graph Neural Network

Named entity recognition is an important task in natural language processing, Chinese-named entities are harder to identify because of uncertainty and complexity of border composition. The Self-Attention mechanism cannot capture the direction information and relative positions between words. The loss of position information and the lack of direction information will inevitably affect the effect of named entity recognition task. In this work, we try to improve the recognition effect by introducing a graph neural network, encode word information and location information into the model by building a graph structure combined with a dictionary, and take full advantage of the pretraining effect of Lattice LSTM model to eliminate entity ambiguity. We use Transformer encoder to further extract contextual features. Finally, the conditional random field is introduced to obtain the optimal tag sequence. The experimental results show that the improved model outperforms other comparative network models in the evaluation indicators at Recall, Precision, and F1 score.
<scp>MBRSDTC</scp>: Design of a multimodal bioinspired model to improve resource scheduling efficiency with differential task‐level constraints

Abstract Scheduling of resources in cloud environments requires design of multiple pattern analysis models that include but are not limited to, inter‐task dependency pattern analysis, make‐span pattern analysis, virtual machine (VM or resource) based capacity analysis, deadline analysis, and VM‐to‐task compatibility analysis. Existing scheduling models are either highly complex, or do not integrate comprehensive analysis modules for efficient scheduling of resources to tasks. Moreover, some of these models showcase limited scalability when applied to large‐scale deployment scenarios. To overcome these issues, this text proposes design of a multimodal bioinspired model to improve resource‐scheduling efficiency with differential task‐level constraints. The proposed model initially collects multimodal information sets about tasks and underlying resources in order to augment analysis efficiency for different task types. These information sets are initially processed by a Grey Wolf Optimization (GWO)‐based scheduling model that assists in grouping tasks based on their make‐span, deadline and dependency levels. The grouped tasks are then scheduled via an incremental learning Elephant Herding Optimization (EHO) model that assists in assigning grouped tasks to capacity‐tuned resources (or VMs). Due to integration of these optimization methods, the proposed model is capable of improving the efficiency of resource scheduling by 8.5%, while reducing computational complexity by 4.3%, while improving the deadline hit ratio by 5.9%, and lowering energy consumption by 1.5% when compared with standard machine learning based scheduling techniques. Due to which the proposed model is capable of deployment for a wide variety of real‐time scheduling scenarios.
Circularity of Metapraxis and Buddhist Practice: Anthem for Children

이 글의 목적은 메타프락시스적 순환론을 통해 인간의 숙명이 어떻게 규정될 수 있는 것인지 보다 구체적으로 이해해 보는 데에 있으며, 이러한 구체적인 이해를 바탕으로 ‘교육학적 의미’에서의 인간의 숙명은 큰어른을 지향해야 한다는 점을 규명하는 데에 있다. 그리하여 이 글은 우선 ‘어린이와 어른’을 개념적으로 구분하여 이해해 보고, 어린이는 어떻게 큰어른이 될 수 있는지를 밝혀 보았다. 그런데 여기서 정작 중요한 점은 큰어른은 그냥 ‘주어지는 것’이 아니라, 어린이가 큰어른이 되고자 하는 노력 또는 교육에 의해 그 심성이 함양될 수 있으며, 이를 위해서는 사회적 역량이 집결되어야 하면서도, 한편으로 그 스스로의 ‘결단과 노력’이 반드시 뒷받침되어야 한다는 점이다. 또한 인간은 태어나는 순간 어린이의 상태 또는 시절을 운명적으로 받아들일 수밖에 없다고는 할지라도, 어른의 상태 또는 자질은 자신의 노력 또는 정상적인 교육에 의해 얼마든지 큰어른이 될 수 있다는 점에서 그 운명이 ‘결정된 상태’가 아니라 ‘결정되어져 가는 상태’에 있다고 할 수 있다. 그리하여 이 글에서 살펴본 바와 같이, 이는 불교 수행에서의 지관수행과 정혜수행의 ‘구분과 관련’에 의해 그것이 ‘어떻게 가능한지’ 보다 구체적으로 이해해 볼 수 있게 된다.The purpose of this article is to understand in more detail how human destiny can be defined through metapraxis circular theory, and based on this specific understanding, human destiny in the ‘educational sense’ should be aimed at spiritual person(大人). Thus, this article first conceptually distinguished and understood ‘children and adults’, it revealed how childrencan become spiritual person. However, the important point here is that spiritual person are not just ‘given’, but children can be cultivated by their efforts to become spiritual person or education, and for this, social competence must be gathered, and on the other hand, their own ‘decision and effort’ must be supported. In addition, even if humans have no choice but to accept the child’s state or time as fate at birth, the state or quality of an adult can be said to be in a state of being determined, not a state of being determined, in that it can become a spiritual person by its own efforts or normal education. Therefore, as discussed in this article, it is possible to understand more specifically how it is possible by ‘related to division’ between Concentration & Contemplation[止觀] and Samadhi & Vipassana[定慧] in Buddhist practice.
Influences of noisy channels on quantum coherence and entanglement of W state in noninertial frame

Abstract Via using l 1 -norm of quantum coherence and π –tangle, we investigate influences of amplitude-damping and depolarizing channels on quantum coherence and entanglement for W state of Dirac fields when two observers are in accelerating motion. We find that quantum entanglement vanishes for all values of acceleration if at least one qubit is under noisy channels. But quantum coherence does not vanish even at infinite acceleration if only one subsystem is under noisy channels. We also find that, unlike the depolarizing environment, the amplitude-damping environment can destroy symmetric properties of quantum entanglement with respect to two accelerated observers. These results will further enrich our understanding on the relation between quantum coherence and entanglement.
Blockchain-enabled Platform for a Meta Customer Loyalty Program

The competitive value of customer loyalty programs is well-known among marketing strategists. With the emergence of digital platforms, their impact has skyrocketed, with mobile applications replacing more cumbersome physical cards with unique identification numbers on embedded magnetic strips or smart chips. However, these programs generally suffer from two major drawbacks that limit their growth. Firstly, physical cards and mobile applications are restricted to particular merchants and programs. There is no mechanism for customers to transfer their loyalty points between programs or to other customers (although airlines are pioneering efforts in this direction). Secondly, the payoffs of participating in these programs and transactions are not fair to all players (customers, merchants and program operators). Finally, the operational mechanisms of such programs are rarely transparent and traceable; hence, disputes are not easily mediated. Using the innovative strengths of blockchain technology, we present a conceptual architecture for a meta or universal customer loyalty program that supports the design principles of transparency, accountability, fairness and ethics.
Voice Command Based Remote Surgery Simulator

Health has always been a vital concern of any society. Therefore, various countries invest significantly in the heath sector to acquire state of the art health infrastructure. It may be noted that not all the countries are economically strong to meet the growing need of health services. Moreover, it is not possible that all countries have same medical experts available. In such case, patients have sometimes been shifted to other countries for treatments. This is an expensive process, whereas if the desired equipment is available locally, then it is possible to carryout treatment. The problem with such an arrangement is that the expertise of medical doctors may sometimes not available locally, whereas they need to be the part of treatment or surgery. The use of advanced technologies can help in achieving such goal. This research proposed and developed a simulator that can take voice commands to present visualized simulation for the guidance of the on-site surgeon to conduct surgery. The surgery is supposed to be done in supervision of expert surgeon connected remotely and the instruction passed by remote surgeon is translated and simulated for better results in surgery. The proposed simulator is devised and tested on four different types of surgeries and the results are very promising in achieving the desired goals.
An Improved Analysis of Renewable Energy based Storage System using Solar Deep Learning Model

This earth provides enough to satisfy every man's need and not enough to satisfy everyone's greed. We consume energy at a faster rate than we produce it - coal, oil and gas are the most widely used resource sources. They take tens of thousands of years to form. Energy resources are very limited - India has only 1% of the world's energy resources. But India has 16% of the world's population. Most of the energy resources are non-reusable or renewable - non-renewable energy accounts for 80% of fuel consumption. It is said that the energy resources we have today will only last for 40 years. In this paper, an improved analysis has proposed for renewable energy based storage system using solar deep learning model. Saving energy equals huge savings for the country - 75% of our crude oil needs are met through imports. By saving fuel we also save money. By saving energy we save energy If we practice burning firewood in an economical way, the work required to collect it will be less. Energy stored equals energy produced - if we save one unit of electricity, it is equivalent to producing 2 units of electricity. Save energy to reduce environmental pollution. Energy production and use are also the biggest contributors to air pollution..
Anomaly Detection of Substation Engineering Video Object Based on Cloud Platform

Video surveillance is a kind of computer platform, using digital image processing technology, through the acquisition, analysis and management of screen information to achieve various operations. Video surveillance system plays a very important role in the power industry. In order to stabilize the substation system, it is necessary to monitor the substation project in real time. Judge whether the substation operates normally by video object anomaly detection. In this paper, the method of experimental detection and subtraction principle are used to detect the abnormalities of substation projects. The results show that the average relative error of the tower is about 2%, which verifies the accuracy and effectiveness of this method.
The Risk Analysis of Deforestation Activities based on the Artificial Intelligence based Light Weight Deep Learning Model

Deforestation can affect crop yields due to excessive rainfall or drought. The growing season of crops is extended in cold regions. Pest and disease attacks are likely to increase. Due to high heat, the decomposition of organic matter in the soil is accelerated and its quantity is likely to decrease. As carbon dioxide increases, productivity increases and crops take up more nutrients from the soil. This can reduce soil fertility. As the Earth warms, mosquito lifespans and reproductive rates increase. This causes malaria to spread. Produce and food spoilage due to favorable conditions for bacteria and fungi to grow. Depletion of the ozone layer in the atmosphere increases the incidence of skin cancers. Sea levels rise due to melting ice sheets due to high temperatures. If this continues there is a risk of drowning Maldives and Bangladesh. Groundwater becomes saline due to intrusion of saline water in coastal areas. Use of petroleum fuels should be reduced. Effective use of natural energy can reduce carbon emissions. Deforestation should be stopped completely.
Quantum radial basis function method for the Poisson equation

Abstract The radial basis function (RBF) method is widely used for the numerical solution of the Poisson problem in high dimension, where the approximate solution can be found by solving a large system of linear equations. We demonstrate that the RBF method can be accelerated on a quantum computer by using an efficient quantum algorithm for linear equations. We compare the theoretical performance of our quantum algorithm with that of a standard classical algorithm, and find that the quantum algorithm can achieve a polynomial speedup.
A Smart Artificial Intelligence based Machine Learning System for Accurate Humidity Calculations in Living Spaces

High humidity adversely affects the physical and mental functioning of a completely healthy person, because fatigue occurs very quickly in such conditions. If a person lives in a room or apartment with high humidity, he is more susceptible to infectious diseases and colds. Staying in such conditions for a long time can contribute to the development of more serious diseases: rheumatism, tuberculosis, etc. Increased humidity in the air can be dangerous for allergy sufferers. The situation in wet rooms is very favorable for the appearance of powerful allergens, which can have really negative effects on the body, not to mention the fact that they spoil the appearance and decoration of the apartment. In addition to mold and mildew, high humidity contributes to the growth of various pathogenic microorganisms and insects. Furniture deteriorates from it, an unpleasant smell appears.
Application Research of Ant Colony Algorithm in Cross Basin Reservoir Earthquake and Dam Strong Earthquake

Ant colony algorithm is a modern intelligent optimization algorithm, which is essentially a pseudo-random search and parallel algorithm. It makes some complex problems that are difficult to be solved by conventional optimization algorithms better, and improves people's ability to solve practical engineering optimization problems. for the problems of reservoir earthquake and dam strong earthquake, this paper analyzes the optimal solution under different parameter settings through the study of ant colony algorithm and process, combined with sample data.the results show that the improved ant colony algorithm can speed up the convergence speed of the algorithm and improve the overall quality of the solution.
Application of Computer Intelligent Information Management in Multi-modal Language Interaction Analysis of Live Broadcast with Goods

With the rapid development of China's economy and society and the improvement of people's living standards, people's spiritual needs are also increasing, and many kinds of intelligent tools are often needed when working. Therefore, computer intelligent management technology is used to release various goods and other forms of goods to the masses through the network platform. From the perspective of computer intelligent management, this paper expounds that there are many problems in the current domestic game bucket platform, such as lack of effective monitoring, low efficiency of information transmission, low resource utilization, and puts forward corresponding countermeasures and suggestions to improve the comprehensive competitiveness of game bucket anchors and user experience effect. After that, the computer intelligent information management was studied, and the multimodal language interaction model of live broadcast cargo was designed and tested. The test results showed that the response time of each module of the multimodal language interaction model of live broadcast cargo based on computer intelligent information management was within 20 seconds, with high response efficiency and interaction efficiency. The fault tolerance rate of the model was up to 80% or more.
Ensemble deep learning fusion for detection of colorization based image forgeries

Image forensics detects manipulation of digital images by tampering and counterfeiting process. While most works on Image forensics detect splicing, retouching and copy-move, very few have addressed colorization forgeries. Colorization or Fake colorization is a rapidly emerging area where colors of certain regions in image are manipulated with realistic colors. This is done maliciously to confound object recognition algorithms. Though some works are proposed to detect fake colorization, they can be deceived easily by introducing the pixel differences using statistical techniques. This work proposes a deep learning technique for detection of colorization forgeries which is resilient against deceiving attacks. Best set of discriminating features are extracted from Deep learning layers to recognize the differences in multiple channels of hue, saturation, value with aim to increase the accuracy of colorization forgery detection. Compared to most recent histogram based features, deep learning model is able to learn more intricate features about the distribution of intensity in hue, saturation, dark and value channels. Through experimental analysis, the proposed solution is found to provide at least 2% higher fake colorization detection accuracy compared to existing works