Information Processing Techniques Research Articles

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Abstract This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.

CRITERIA FOR QUALITY ASSESSING OF ABSTRACT TRANSLATION

Statement of the problem. Informatization of the global space and the growing need of intercultural interaction have an impact on all spheres of human life. Techniques of fast, efficient and adequate information processing are becoming more and more practically significant. The study considers abstract translation as the most demanded and economical means of obtaining of information. In connection with this, the objective was set – to determine the criteria for quality assessing of abstract translation. Results. The article analyses the works of domestic and foreign authors, considering the criteria for determining the quality of abstract translation as well as didactic methods and stages of creating of abstract translated text as an element of linguistic, cognitive and analytical processes. The papers of participants of All-Russian student contest of abstract translation were used as a material for practical research to solve the tasks. Conclusion. The practical significance of the study lies in the considering of an abstract translation within the framework of professional bilingual communication; a qualitative and quantitative analysis of contest papers is computed; a scale of criteria for assessing the quality of abstract translation is formed.

Engines for predictive work extraction from memoryful quantum stochastic processes

Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.

Retracted: Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

Retracted: Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

Application of graphic image processing technology in 2D game interface design

Abstract This paper uses big data information processing techniques for the underlying algorithm of interface interaction, calculates the number of game interface data points, and satisfies the hierarchical feature distribution of interface interaction. Using cross-compilation for information clustering, identifying fuzzy clustering central function feature classification, describing the underlying data statistical independent variables, and extracting graphical local feature information. Balancing constraint encoding and dictionary coefficient weighting parameters, processing edge feature images, and matching grayscale transformed image effects are conveyed. The results show that the image graphics technology has an accuracy rate of 87.25% for image recognition, the user satisfaction rate for the 2D game design interface is 78.60%, and users aged 30-40 are the largest group of game consumers. It indicates that graphic image processing technology can collect and obtain relevant information and data, thus providing important technical support for the development of the social game industry and promoting the deepening development of an information society.

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).

Pythagorean fuzzy information processing based on centroid distance measure and its applications

By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages.

Remote Sensing-Based Assessment of Soil and Water Pollution in Deep Excavation Scenario

Soil and water resource mismanagement can negatively intensify global poverty and jeopardize ecology. Soil can be contaminated by heavy metals, organic chemicals like biological pathogens, pesticides, micro plastics and nano particles. Contamination decreases the soil’s capability to yield food thereby affecting food production by means of pollution and disease. Moreover, soil contaminants move into rivers damaging the water quality. Numerous technologies have been established to tackle water and soil remediation, such as deep excavation technique where transportation of contaminated soils or water is done to remote unpopulated sites. Recent development in Remote Sensing and geographic information processing techniques have led to exciting new opportunities for investigating and closely monitoring environmental factors that influence key land and soil management approaches. Hence, this paper uses the Deep Excavation and Remote Sensing based Assessment Model (DERSAM) to assess the soil and water pollution in contaminated areas. The data are taken from the Europe soil contamination map to classify the contaminated and non-contaminated areas. This data obtained from the high-resolution satellite of Remote Sensing (RS) technique and Geographical Information System (GIS) with the parallel expansion of a fully assimilated geospatial database system that provides monitoring and feedback at suitable spatial scales. Thus, such data can be utilized for long-term environmental management and monitoring of remediation and rehabilitation of excavation areas. The numerical outcomes show that the recommended DERSAM model increases the land use and land cover change prediction by 90.2%, the classification ratio by 98.2%, the pollution reduction ratio by 96.6%, the soil contamination detection ratio by 95.9%, and the overall performance by 97.2% compared to other existing approaches.

IoT Enabled Sensory Monitoring System for Fog Optimal Resource Provisioning Method in Health Monitoring System

Fog is data management and analytics service. In this paper gains and most effective novel approach to provide IoT enabled services in healthcare application using Fog Computing. In this research the data is collected from Google Scholar, Science Director and MEDLINE database. IoT based Fog Computing techniques are proposed for delivering quality of services to the user. Optimal Resource Provisioning method is proposed to find edges, service level agreements and administration services for IoT client. The DeepQ residue information processing technique is applied for connecting data centre of the cloud and computing paradigms technique is finding the depth reference of Fog levels. The proposed Optimal resource provisioning algorithm is examining the dataset and TensorFlow tool is used for simulating environment. Fog computing layer consist of IoT sensor data inputs, data centres for the cloud and connected layers for simulations. The Deep belief network is generated based on above inputs using 256 X 256 X 3 layer system and 5000 trained data, 1000 test data are taken for simulations. Each dataset simulation is recording using supervised and unsupervised learning methods. Based on above results IoT enable Fog Computing data management and analytics systems provided 95% accuracy and the compared with existing computing techniques our proposed systems shows better efficiency with respect to safety and convenience.

Short-term load forecasting system based on sliding fuzzy granulation and equilibrium optimizer.

Short-term electricity load forecasting is critical and challenging for scheduling operations and production planning in modern power management systems due to stochastic characteristics of electricity load data. Current forecasting models mainly focus on adapting to various load data to improve the accuracy of the forecasting. However, these models ignore the noise and nonstationarity of the load data, resulting in forecasting uncertainty. To address this issue, a short-term load forecasting system is proposed by combining a modified information processing technique, an advanced meta-heuristics algorithm and deep neural networks. The information processing technique utilizes a sliding fuzzy granulation method to remove noise and obtain uncertainty information from load data. Deep neural networks can capture the nonlinear characteristics of load data to obtain forecasting performance gains due to the powerful mapping capability. A novel meta-heuristics algorithm is used to optimize the weighting coefficients to reduce the contingency and improve the stability of the forecasting. Both point forecasting and interval forecasting are utilized for comprehensive forecasting evaluation of future electricity load. Several experiments demonstrate the superiority, effectiveness and stability of the proposed system by comprehensively considering multiple evaluation metrics.

Performing SU(d) Operations and Rudimentary Algorithms in a Superconducting Transmon Qudit for d=3 and d=4

Quantum computation architecture based on $d$-level systems, or qudits, has attracted considerable attention recently due to their enlarged Hilbert space. Extensive theoretical and experimental studies have addressed aspects of algorithms and benchmarking techniques for qudit-based quantum computation and quantum information processing. Here, we report a physical realization of qudit with upto 4 embedded levels in a superconducting transmon, demonstrating high-fidelity initialization, manipulation, and simultaneous multi-level readout. In addition to constructing SU($d$) operations and benchmarking protocols for quantum state tomography, quantum process tomography, and randomized benchmarking etc, we experimentally carry out these operations for $d=3$ and $d=4$. Moreover, we perform prototypical quantum algorithms and observe outcomes consistent with expectations. Our work will hopefully stimulate further research interest in developing manipulation protocols and efficient applications for quantum processors with qudits.

A Novel Business Scheduling Approach for Enterprises via Vision Sensing-Based Automatic Documental Information Extraction

Currently, the prevalence of various Internet intrusion technologies has brought much challenge to the enterprise management. For many core documents, the information leakage may lead to the loss of secrets of enterprises. Therefore, some core official documents in enterprises are in the format of papers, rather than electronic format. As a consequence, it is of significance to develop automatic information processing techniques for official documents in the format of papers, so as to improve the working efficiency of enterprises. In this paper, a novel business scheduling approach for enterprises via vision sensing-based automatic documental information extraction is proposed. For the first stage, the vision sensing-based optical character recognition (OCR) technique is utilized to extract textual information from official documents in the format of papers. For the second stage, the deep neural network is utilized to output business scheduling results on the basis of digital recognition contents from the first stage. Finally, the experimental simulation is also carried out to verify efficiency of the proposal.

Using psychological characteristics of situations for social situation comprehension in support agents

Support agents that help users in their daily lives need to take into account not only the user’s characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However, research shows that it is important to also determine the meaning of a situation, a step which we refer to as social situation comprehension. We propose using psychological characteristics of situations, which have been proposed in social science for ascribing meaning to situations, as the basis for social situation comprehension. Using data from user studies, we evaluate this proposal from two perspectives. First, from a technical perspective, we show that psychological characteristics of situations can be used as input to predict the priority of social situations, and that psychological characteristics of situations can be predicted from the features of a social situation. Second, we investigate the role of the comprehension step in human–machine meaning making. We show that psychological characteristics can be successfully used as a basis for explanations given to users about the decisions of an agenda management personal assistant agent.

Optoplasmonic biosensor for lung cancer telediagnosis: Design and simulation analysis

Applying quantum technology to dispatch face-to-face medical activities has generated significant interest. Unfortunately, the work on remote medical treatment soliciting quantum medication and information processing techniques is hard to observe. In this research, we proposed the Mach–Zehnder interferometer (MZI) based optoplasmonic biosensors (OPBs) with two homodyne detectors for remote-based lung cancer detection using classical and quantum mechanical principles. From the classical basis (Drude-Lorentz model and Kretschmann configuration), the influence of silver nanoparticles (Ag NPs) layers and biomolecule concentration on the performance of biosensors has been investigated. The different types of cancer cells for CL1-5, A549, and HT-29 have been used to analyze the sensitivity, and 319, 332, and 365 (deg/RIU) have been achieved, respectively. In addition, from quantum mechanical principles, the biosignals were conveyed through quantum teleportation in the form of the quantum state of light via fiber optics cable to enable precise remote detection of lung cancer. The obtained sensitivity and teleportation fidelity clearly reveal, the best candidacy of the proposed optoplasmonic biosensor for lung cancer telediagnosis.

The Informational Role of Imagery in Financial Decision Making: A New Approach

What is the incremental information in imagery? This paper introduces the concept of visual attention and proposes a new methodology to identify the effect of information content provided in images, above and beyond that of contemporaneous textual information. Our objective (non-behavioral) approach introduces two sets of criteria for analyzing the effects of images - visual clarity and information content/reinforcement, which together inform individual investors in fi nancial decision making. This flexible methodology can be seamlessly integrated with various modern information processing techniques, from simple keyword matching to advanced semantic analysis or topic modeling. Applying our methodology to a hand collected data set of equity crowdfunding campaigns, we show that the existence of perceptible images is signi ficantly associated with greater investment, and that visual clarity measures are found to incrementally affect investor decision making after controlling for textual sentiment and fi nancial information. Speci fically, more vibrant (intense) images with more focal points are associated with higher funding. Importantly, we find that the information reinforcing dichotomy we propose is a key determinant of (limited) visual attention: informationally additive images are those for which visual clarity measures are signi ficant, and those which seem to be associated with greater investor attention and investment. Finally, the methodology we develop in this paper provides the groundwork for a new framework within which to discuss (limited) attention, information, news, and investor reactions in today's digital and visual age.

Interview of Ivan Sutherland

Ivan E. Sutherland was born in Hastings, Nebraska, in 1938. He received a BS from the Carnegie Institute of Technology (1959) in electrical engineering, an MS from Caltech (1960) and a PhD in electrical engineering from MIT (1963) <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sutherland's dissertation, Sketchpad: A Man Machine Graphical Communication System, described a groundbreaking interactive computer-aided design system, one whose graceful interaction and functionality inspire admiration among computer graphics professionals even today <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">From 1964 to 1966, Sutherland was director of the Information Processing Techniques Office of the Advanced Research Projects Agency (ARPA), where he guided computer research across the United States. In 1968, he joined colleague David Evans to build a center of computer graphics research at the University of Utah and to found with him the Evans and Sutherland Computer Corporation <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">From 1976 to 1980, Sutherland served as chairman of computer science at Caltech, and from 1980 onwards, was a vice president of the consulting firm Sutherland, Sproull and Associates, acquired by Sun Microsystems in 1990 to form the basis of its corporate research laboratory <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sutherland has won many awards for his work, including the ACM Turing Award (1988) the IEEE John von Neumann Medal (1998). He is a Fellow of the Computer History Museum.

Density matrix reconstruction using non-negative matrix product states

Quantum state tomography is a key technique for quantum information processing but is challenging due to the exponential growth of its complexity with the system size. In this work we propose an algorithm which iteratively finds the best non-negative matrix product state approximation based on a set of measurement outcomes whose size does not necessarily grow exponentially. Compared to the tomography method based on neural network states, our scheme utilizes a so-called tensor train representation that allows straightforward recovery of the unknown density matrix in the matrix product operator form. As applications, the effectiveness of our algorithm is numerically demonstrated to reconstruct the ground state of the XXZ spin chain under depolarizing noise.

An improved butterfly optimization algorithm for training the feed-forward artificial neural networks.

Artificial neural network (ANN) which is an information processing technique developed by modeling the nervous system of the human brain is one of the most powerful learning methods today. One of the factors that make ANN successful is its training algorithm. In this paper, an improved butterfly optimization algorithm (IBOA) based on the butterfly optimization algorithm was proposed for training the feed-forward artificial neural networks. The IBOA algorithm has the chaotic property which helps optimization algorithms to explore the search space more dynamically and globally. In the experiments, ten chaotic maps were used. The success of the IBOA algorithm was tested on 13 benchmark functions which are well known to those working on global optimization and are frequently used for testing and analysis of optimization algorithms. The Tent-mapped IBOA algorithm outperformed the other algorithms in most of the benchmark functions. Moreover, the success of the IBOA-MLP algorithm also has been tested on five classification datasets (xor, balloon, iris, breast cancer, and heart) and the IBOA-MLP algorithm was compared with four algorithms in the literature. According to the statistical performance metrics (sensitivity, specificity, precision, F1-score, and Friedman test), the IBOA-MLP outperformed the other algorithms and proved to be successful in training the feed-forward artificial neural networks.

Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

The cultivation of reading ability runs through a child’s life. Children are in a critical period of early reading ability formation. Only based on the actual development characteristics of children and adopting effective training methods, can early childhood educators better meet the needs of children’s long-term development. Based on the data of 22 classes in 7 public kindergartens in X City in Mainland of China, this study first compared the effects of two different teaching modes and then analyzed the relationship between early reading amount and expressive ability of children. Independent sample T test, OLS regression, and Lasso regression were used to demonstrate the correlation between early reading amount and expressive ability, and the possible mechanisms of family background are also explored. The results show the following: firstly, heuristic teaching mode has positive teaching effect. The effect quantities of heuristic teaching mode on transmission ability, communication ability, transfer ability, and generalization ability are 0.21, 0.25, 0.31, 0.30, and 0.32, respectively, which belong to medium intensity, indicating that heuristic teaching mode has a good intervention effect on improving students’ expression ability. Secondly, there is a high correlation between early reading and children’s expressive ability. Thirdly, there are two aspects of the influence of early reading amount on children’s expressive ability: explicit influence and implicit influence. Fourthly, the association between early reading and different presentation skills is not the same. To sum up, we should actively encourage the use of heuristic teaching mode, realize the heterogeneity of early reading amount, and promote the improvement of children’s expressive ability through both explicit and implicit paths.

Automatische Bildanalyse und künstliche Intelligenz in der Nephropathologie

HintergrundDie digitale Transformation der Pathologie durch die weite Verbreitung von sog. Whole-slide-Scannern bietet zahlreiche Möglichkeiten für die Nephropathologie, insbesondere hinsichtlich der Implementierung von Computerassistenz. Aktuell werden intensiv die Möglichkeiten von Systemen, die auf der Nutzung von Deep Learning, einer speziellen Technik der Informationsverarbeitung, beruhen, erforscht.FragestellungZiel ist die Ermittlung des gegenwärtigen Standes der Forschung hinsichtlich Anwendungen von Deep-Learning-Methoden zur Bildanalyse in der Nephropathologie.Material und MethodenLiteraturrecherche im Web of Science (WOS) und in Pubmed. Für Abbildung 1 wurde die folgende Suchabfrage im WOS genutzt: ALL=(Digital Pathology AND AI OR Deep Learning OR Machine Learning).ErgebnisseEs liegen zahlreiche Anwendungen von Deep-Learning-basierten Methoden zur Unterstützung in der Nephropathologie vor. Diese fokussieren im Wesentlichen auf die Segmentierung und Quantifizierung der Nierenhistologie, wobei zunehmend auch die diagnostische Klassifikation und die Generierung synthetischer Daten erforscht werden. Die Translation dieser Systeme in den diagnostischen Alltag ist gegenwärtig noch nicht erfolgt. Es fehlt dazu beispielsweise an prospektiver Evidenz, die den Nutzen dieser Methoden in der klinischen Versorgung darlegt.DiskussionDie Implementierung der digitalen Nephropathologie mit Assistenz durch auf Deep Learning basierenden Methoden hat ein großes Potenzial. Die translationale Lücke sollte in Zukunft durch multizentrische, prospektive, interdisziplinär durchgeführte Studien gefüllt werden.