Computer Information Processing Research Articles

The intelligent prediction and assessment of financial information risk in the cloud computing model

The intelligent prediction and assessment of financial information risk in the cloud computing model

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

AI EOS using Wolframe Alpha

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops, and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language

UTILIZING ARTIFICIAL INTELLIGENCE IN EDUCATION TO ENHANCE TEACHING EFFECTIVENESS

Artificial Intelligence in Education (AIEd) has been evolving for some time, and the advent of GPT chat at the end of December 2022 has opened up new opportunities, potentials, and challenges in educational practice. Advances in computational technology and information processing have led to widespread applications of Artificial Intelligence (AI) in the field of education. Over the last 20 years, the number of papers on AIED has been steadily increasing, with a dramatic rise since 2015 until the present. In its brief history, AIEd has undergone several paradigm shifts. This research aims to explore the use of AI in education by examining the publication trends sourced from metadata from Google Scholar, PubMed, CrossRef, OpenAlex, and Scopus. The development and application of Artificial Intelligence (AI) technology, particularly in education, significantly supports educational reform and profoundly influences the learning styles of learners. Artificial Intelligence in Education (AIED) can assist teachers in preparing teaching materials, presentation media, and accurate evaluations. Furthermore, AIED can help students adapt their traditional learning styles according to their differences, thus realizing intelligent teaching that meets students' learning needs. Teachers' positive perceptions of educational technology (ET) are beneficial for using AI technology to aid teaching positively, which in turn can enhance teaching effectiveness. Overall, the trend of AIEd development has successfully empowered learner personalization, enabling learners to think critically and innovatively, and fostering personalized learning.

Superimposed Bragg gratings and entangled biphoton dispersion management

Quantum optical integrated circuits have heralded a paradigm shift in the realm of quantum information processing. Integrated photonics technology now empowers the creation of intricate optical circuits on single chips. While optical integrated circuits used to pose formidable challenges for numerous quantum applications, they have, in recent times, evolved to satisfy stringent requirements across a spectrum of research and industrial domains. Today, it is imperative to delve into research aimed at both crafting and preserving quantum properties within photonic substrates. Superimposed Bragg grating structures have emerged as valuable components within optical applications, poised to play pivotal roles in the development of integrated circuits. Nevertheless, these structures exhibit an inherent drawback in the form of dispersion, which can potentially compromise the preservation of quantum states. In this study, we meticulously scrutinize the physical attributes of these structures to elucidate the factors contributing to undesirable dispersion effects. We also investigate the correlation between two photons at the termination point of the structure. The superimposed Bragg grating structure under scrutiny boasts periods of both 1 and 3 micrometers, an overall length of 100 micrometers, and radiates at a wavelength of 1.55 micrometers. By subjecting photons to this medium individually or in tandem and analyzing their correlation function, we aim to pinpoint elements that effectively safeguard the quantum properties inherent in the system. This research endeavor is poised to yield valuable insights that will substantially influence the design of quantum integrated circuits, enhancing their efficacy in computational tasks and quantum information processing.

Research on text classification based on neural networks

With the rapid development of information technology, the processing of massive text data has become increasingly important. As a common computer information processing task, text classification has attracted a wide range of research interests. This paper aims to explore the text classification method based on neural network and analyze the key technologies. In order to solve the problem of text time series data classification, this paper uses the text time series data of occupancy detection and applies these neural network models in deep learning, including recurrent neural network, long short-term memory and gated recurrent unit, and trains the neural network through supervised learning. Inputting the room attribute data to these trained neural network models and judging the occupancy of the room. At the same time, observing the experimental results of these neural network models, including training loss, test loss and accuracy, to further study the performance of neural network in processing text time series data classification. This papers experiment aims to evaluate the performance of neural network in text classification and makes a detailed analysis through the experimental results. The goal of the research is to find an effective solution based on neural networks for the classification of text sequence data. Through the analysis of the experimental results, it can be concluded that the method based on neural network is feasible and effective in text sequence data classification. These analysis results will help to further promote the development of text classification technology and provide guidance and reference for practical application.

Semi-device-independent quantum random number generator with a broadband squeezed state of light

Random numbers are a basic ingredient of simulation algorithms and cryptography, and play a significant part in computer simulation and information processing. One prominent feature of a squeezed light is its lower fluctuation and more randomness in a pair of orthogonal oriented quadratures, thus it prompts a significant application in not only quantum information and quantum precision measurement but also an excellent entropy source for true random number generation. Here we report a generation of a high-efficiency semi-device-independent quantum random number based on a broadband squeezed light, where a reliable randomness source is unnecessary and a noisy local oscillator is allowed for homodyne detection. The equivalent generation of private random bits is at a rate of 580.7 Mbps. In addition, the use of squeezed light at 1.3 μm enables the transmission of entropy sources and local oscillators at the metropolitan scale, thus expanding the potential applications of quantum random number generators based on non-classical state of light.

Improvement of pricing on the market of organic products

Modern organic agriculture is increasingly gaining momentum against the background of worsening economic, social and environmental problems. In recent years, interest in environmentally friendly production has been growing in many areas. In particular, agriculture in this direction satisfies the needs of mankind for quality products and has a positive effect on restoring the natural fertility of the soil, which contributes to the maintenance of the natural balance. The purpose of this study is to analyze the pricing process on the market of organic products and the justification of recommendations for improving the pricing methodology and determining the equivalent price for organic agricultural products. The work uses: dialectical method, methods of systematic and comparative analysis, economic-statistical and graphic methods, methods of computer information processing. It has been established that the leaders of organic agriculture are countries that have formed systemically influential supranational institutions and societies. When comparing the economic efficiency of production, it was determined that an agricultural enterprise with the production of organic products receives less gross production, net income and profit per 1 ha of farmland than conventional ones. The price analysis of the organic market in Ukraine revealed the need to modify generally accepted approach to pricing. The proposed equivalent price can be a guideline for improving the price policy in organic production.

Relieving youth work stress anxiety through computer information processing technique

BackgroundIn today’s fast-paced, technology-oriented business environment, anxiety among young people arising from work-related stress is a growing concern. The use of computer information processing techniques has been identified as a potential intermediary in alleviating this anxiety. Computer information processing technique has a potential mediating role in alleviating young people’s work stress and anxiety, but comprehensive intervention is still needed to integrate other factors to improve their mental health and professional life quality.Subjects and MethodsThe study involved a population of young workers exhibiting anxiety symptoms due to work-related stress. The Stanford Acute Stress Response Questionnaire (SASRQ) and the 3-min Confusion Assessment Scale (3D-CAM) were utilized to measure participants’ stress and anxiety levels pre-and-post intervention. The intervention involved the implementation of computer information processing techniques in their work routine. The outcomes were statistically analyzed using SPSS 23.0.ResultsAfter the application of computer information processing techniques, a significant reduction in SASRQ and 3D-CAM scores was observed, indicating decreased levels of work stress anxiety among the population. In addition, as a result of being able to manage tasks more efficiently, participants reported increased job satisfaction and improved work-life balance.ConclusionsThe results underscore the potential of computer information processing techniques in alleviating work-induced stress anxiety among young employees. Such techniques not only provide improved work efficiency but could also contribute to better mental health among the youth workforce.AcknowledgementScience Research Fund Project of Yunnan Education Department in 2019 (No. 2019J0999).

Cross-Cultural Adaptation and Spanish Validation of the Computerized Information Processing Assessment Battery (COGNITO).

Mild cognitive impairment (CI) has an exponential increase in its prevalence and causes functional deficits and dependence. Its early detection allows for timely treatment and greater therapeutic efficacy. However, mild cognitive impairment (MCI) is currently underdiagnosed. Although recent decades have seen a rise in computerized instruments for the detection and early diagnosis of MCI, showing numerous advantages over the classic paper-and-pencil methods, such as standardized stimulus presentation. However, their limitations include the use of self-administered application without professional supervision. Few of these instruments have Spanish-adapted versions. To translate, adapt, and validate the computerized Information Processing Assessment Battery (COGNITO) battery in the Spanish population and to develop a portable administration system that facilitates its application in different settings. COGNITO was then administered to 232 Spanish participants (18-89years) without cognitive impairment, after which preliminary normative data were obtained. Strong positive correlations were found between the main cognitive domains assessed by COGNITO and the variables of age, educational level, and MEC score. The gender variable only correlated with visuospatial skills, with men outperforming women. The test-retest correlations conducted after 4 weeks with 89 participants revealed adequate reliability coefficients ranging between.63 and.66 (visuospatial skills = 0.35). Internal consistency coefficients were satisfactory in Attention-Executive Functions and Memory domains. The Spanish adaptation of COGNITO shows adequate psychometric characteristics of validity and reliability. The preliminary normative data provided may contribute to the early detection of cognitive impairments associated with both normal aging and various types of neurological pathology. This tool has great utility and versatility for neuropsychological practice.

Comparing student perceptions of in-class, online synchronous, and online asynchronous instruction

This article compares the efficacy of undergraduate teaching for in-class, online synchronous, and online asynchronous modes of instruction with implications for adapting the modes of instruction in computer information processing. The study uses a repeated measures design with pairwise comparisons while controlling for instructor, students, and course content across these modes of instruction. The study examines the following six perceptions held by students: instructional aspects such as instructor facilitation, student-to-student interaction, instructor-to-student interaction, course design, learning outcomes, and student satisfaction. The study also examines actual student performance on homework, quizzes, and exams. All findings were interesting and insightful. Student perceptions of almost all instructional aspects were significantly higher for the in-class mode than the two online modes of instruction. However, actual student performance was significantly better for online modes of instruction. The discussion section triangulates these statistical results with a review of 3000+ open-ended student responses to explain results, provide guidelines, and suggest future research. Keywords: Asynchronous instruction; distance education; E-learning; in-class; online; synchronous instruction

Emotional intelligence and individuals’ viewing behaviour of human faces: a predictive approach

Although several studies have looked at the relationship between emotional characteristics and viewing behaviour, understanding how emotional intelligence (EI) contributes to individuals’ viewing behaviour is not clearly understood. This study examined the viewing behaviour of people (74 male and 80 female) with specific EI profiles while viewing five facial expressions. An eye-tracking methodology was employed to examine individuals’ viewing behaviour in relation to their EI. We compared the performance of different machine learning algorithms on the eye-movement parameters of participants to predict their EI profiles. The results revealed that EI profiles of individuals high in self-control, emotionality, and sociability responded differently to the visual stimuli. The prediction results of these EI profiles achieved 94.97% accuracy. The findings are unique in that they provide a new understanding of how eye-movements can be used in the prediction of EI. The findings also contribute to the current understanding of the relationship between EI and emotional expressions, thereby adding to an emerging stream of research that is of interest to researchers and psychologists in human–computer interaction, individual emotion, and information processing.

High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity

Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.

A Multiple Case Study of Career Construction and Career Development Processes of People with Adventitious Blindness

A Multiple Case Study of Career Construction and Career Development Processes of People with Adventitious Blindness

Integrated silicon photonic MEMS

Silicon photonics has emerged as a mature technology that is expected to play a key role in critical emerging applications, including very high data rate optical communications, distance sensing for autonomous vehicles, photonic-accelerated computing, and quantum information processing. The success of silicon photonics has been enabled by the unique combination of performance, high yield, and high-volume capacity that can only be achieved by standardizing manufacturing technology. Today, standardized silicon photonics technology platforms implemented by foundries provide access to optimized library components, including low-loss optical routing, fast modulation, continuous tuning, high-speed germanium photodiodes, and high-efficiency optical and electrical interfaces. However, silicon’s relatively weak electro-optic effects result in modulators with a significant footprint and thermo-optic tuning devices that require high power consumption, which are substantial impediments for very large-scale integration in silicon photonics. Microelectromechanical systems (MEMS) technology can enhance silicon photonics with building blocks that are compact, low-loss, broadband, fast and require very low power consumption. Here, we introduce a silicon photonic MEMS platform consisting of high-performance nano-opto-electromechanical devices fully integrated alongside standard silicon photonics foundry components, with wafer-level sealing for long-term reliability, flip-chip bonding to redistribution interposers, and fibre-array attachment for high port count optical and electrical interfacing. Our experimental demonstration of fundamental silicon photonic MEMS circuit elements, including power couplers, phase shifters and wavelength-division multiplexing devices using standardized technology lifts previous impediments to enable scaling to very large photonic integrated circuits for applications in telecommunications, neuromorphic computing, sensing, programmable photonics, and quantum computing.

Plasmonic Generation of Spatiotemporal Optical Vortices

We investigate the transformation of spatiotemporal optical signals using the Kretschmann configuration with an additional dielectric layer, which can be referred to as the “generalized Kretschmann setup”. We demonstrate that in the considered structure, it is possible to achieve the condition of generating a reflected optical pulse containing a spatiotemporal optical vortex, which appears to be impossible in the conventional Kretschmann configuration. High-quality generation of spatiotemporal optical vortices using the investigated structure was confirmed by the results of rigorous numerical simulations. The obtained results are promising for applications in analog optical computing and optical information processing systems.

Dispersion-less Kerr solitons in spectrally confined optical cavities

Solitons are self-reinforcing localized wave packets that manifest in the major areas of nonlinear science, from optics to biology and Bose–Einstein condensates. Recently, optically driven dissipative solitons have attracted great attention for the implementation of the chip-scale frequency combs that are decisive for communications, spectroscopy, neural computing, and quantum information processing. In the current understanding, the generation of temporal solitons involves the chromatic dispersion as a key enabling physical effect, acting either globally or locally on the cavity dynamics in a decisive way. Here, we report on a novel class of solitons, both theoretically and experimentally, which builds up in spectrally confined optical cavities when dispersion is practically absent, both globally and locally. Precisely, the interplay between the Kerr nonlinearity and spectral filtering results in an infinite hierarchy of eigenfunctions which, combined with optical gain, allow for the generation of stable dispersion-less dissipative solitons in a previously unexplored regime. When the filter order tends to infinity, we find an unexpected link between dissipative and conservative solitons, in the form of Nyquist-pulse-like solitons endowed with an ultra-flat spectrum. In contrast to the conventional dispersion-enabled nonlinear Schrödinger solitons, these dispersion-less Nyquist solitons build on a fully confined spectrum and their energy scaling is not constrained by the pulse duration. Dispersion-less soliton molecules and their deterministic transitioning to single solitons are also evidenced. These findings broaden the fundamental scope of the dissipative soliton paradigm and open new avenues for generating soliton pulses and frequency combs endowed with unprecedented temporal and spectral features.

Matrix representation of operations on graphs

This article considers the possibility of matrix execution of both unary and binary operations on graphs. Graphs, as an abstract mathematical construction, have a very wide range of practical applications. First of all, it is algorithmization and computer processing of information, electrical engineering, etc. Therefore, it is important to have a mathematical apparatus that allows you to transform the graphical presentation of information about objects into algebraic models for their further research using purely mathematical methods. If necessary, it is always possible to return from such an algebraic model to a graphical representation of the object (for example, to a graphical representation of a circuit diagram in electronics). For each of the considered operations on graphs, either a combination of algebraic operations or an easily programmable matrix processing algorithm is proposed, which can be used to represent any graph. Attention is also paid to the differences in such processing depending on the type of graphs involved in the considered operations. Some operations are more convenient to perform with adjacency matrices, and some - with incidence matrices. This article also considers these features of matrix execution of operations on graphs. All the proposed algorithms are illustrated with specific detailed examples. Thus, it is shown that for all operations on graphically presented objects, their matrix interpretation is possible. This result greatly facilitates the possibility of software implementation of work with such graphic objects.

Spatiotemporal optical differentiation and vortex generation with metal-dielectric-metal multilayers

We consider a spatiotemporal differentiator of the first order based on a three-layer metal-dielectric-metal (MDM) structure. We show theoretically and numerically that, in contrast to all-dielectric layered structures, the differentiator based on this MDM structure enables generating an optical pulse possessing an optical vortex in the spatiotemporal domain. High-quality edge detection of the incident spatiotemporal optical signal using the MDM differentiator is also demonstrated. Moreover, we investigate a ``double'' MDM structure consisting of two single (three-layer) MDM structures which implements the operation of spatiotemporal differentiation of the second order. By considering the generation of an optical pulse with several phase singularities and studying the edge-detection operation, we demonstrate that the operation of second-order spatiotemporal differentiation is implemented with high accuracy. The obtained results may find application in the creation of analog optical computing and optical information processing systems.

Optical properties of cascaded metal-dielectric-metal structures and their application to the differentiation of optical signals

We consider optical properties of cascaded structures consisting of several sequentially placed three-layer metal-dielectric-metal (MDM) structures separated by homogeneous dielectric layers. Using the scattering matrix formalism, we demonstrate that by successively “combining” MDM structures possessing reflection zeros, one can obtain a zero of a required order in the reflection spectrum of the cascaded structure. The presence of a reflection zero of a required order with respect to the angular or spatial frequency enables utilizing cascaded MDM structures as optical differentiators implementing the computation of the derivative of a required order with respect to time or a spatial variable. The presented numerical simulation results confirm the correctness of the derived theoretical description of cascaded MDM structures and the possibility of their use as high-order optical differentiators. The obtained results may find application in analog optical computing and optical information processing systems.