Input Method Research Articles

Design of Computer Information Management System Based on Machine Learning Algorithms

In order to improve the efficiency of office automation, regulate work frequency, and improve office efficiency, this paper presents a computer information management system design based on machine learning technology. Firstly, the basic design principles of computer information management systems are analyzed, and secondly, risk prediction is studied. The risk of computer information management systems is caused by the cross influence of different risk factor indicators, and has linear and nonlinear characteristics. Using a single prediction model cannot obtain accurate prediction results. Therefore, the risk prediction method for computer information management based on machine learning technology. The risk prediction method is established by using Analytic Hierarchy Method in machine learning algorithms, and the historical data is collected according to the index system. The weight of the initial prediction is determined by the combination of subjective and objective weight; In machine learning algorithms, risk prediction and benefit prediction are used as input and output methods for cloud machine learning. Through training and training, a risk prediction model is established to obtain higher prediction efficiency. The simulation results show that the prediction accuracy of this method is 95.5%, which can estimate the hazard existing in computer information management and improve the method.

Implementation issues and barriers for assessing oral health in dependent patients after stroke: A qualitative study

ObjectivesTo explore implementation issues and potential barriers for assessing oral health in dependent post-stroke patients. MethodsSemi-structured interviews were conducted with a purposively identified sample of healthcare service providers who work in two National Health Service (NHS) Trusts in the north of England. Interviews were conducted until data saturation was achieved (n = 30). Data were analysed using the constant comparative method. ResultsSix themes were drawn out in this study, which described potential barriers to assessing oral health in post-stroke patients, aspects of oral health that need assessment, streamlining the oral health assessment, input methods for oral health assessment, characteristics of assessors, and how oral care should be planned. ConclusionsAssessment of oral health for post-stroke patients has been viewed as a complex task because of several identified barriers. Several suggestions have been proposed to overcome these barriers, aiming to enable more feasible and effective oral health assessments for post-stroke patients. Clinical SignificanceThe findings from this study have the potential to contribute to developing oral health measurement instruments that might be more successfully implemented and guide oral care planning for dependent patients after stroke.

Stylus and Gesture Asymmetric Interaction for Fast and Precise Sketching in Virtual Reality

This research investigates fast and precise Virtual Reality (VR) sketching methods with different tool-based asymmetric interfaces. In traditional real-world drawing, artists commonly employ an asymmetric interaction system where each hand holds different tools, facilitating diverse and nuanced artistic expressions. However, in virtual reality (VR), users are typically limited to using identical tools in both hands for drawing. To bridge this gap, we aim to introduce specifically designed tools in VR that replicate the varied tool configurations found in the real world. Hence, we developed a VR sketching system supporting three hybrid input techniques using a standard VR controller, a VR stylus, or a data glove. We conducted a formal user study consisting of an internal comparative experiment with four conditions and three tasks to compare three asymmetric input methods with each other and with a traditional symmetric controller-based solution based on questionnaires and performance evaluations. The results showed that in contrast to symmetric dual VR controller interfaces, the asymmetric input with gestures significantly reduced task completion times while maintaining good usability and input accuracy with a low task workload. This shows the value of asymmetric input methods for VR sketching. We also found that the overall user experience could be further improved by optimizing the tracking stability of the data glove and the VR stylus.

Simultaneous input and state estimation with multi-step delay for linear stochastic systems based on infinity filtering and smoothing

This paper presents a novel method for simultaneous unknown input and state estimation (UISE) with multi-step delay for linear stochastic systems with rank-deficient direct feed-through matrix. The augmented Kalman filter and smoothing are established with an input model, where the input signal follows a specific probability distribution with finite covariance. Then, in light of the established properties of matrix limiting, the infinity filtering is deduced as the covariance matrix of the input signal tends to infinity. Moreover, the infinity smoothing algorithm is also derived to obtain the multi-step delayed unknown input and state estimation. Furthermore, the infinity filtering is strictly proved to coincide with the existing five-step recursive filter (FSRF). It is shown that some previous UISE algorithms, such as the unbiased minimum-variance (UMV) filter and recursive three-step filter (RTSF), are special cases of the proposed infinity filtering algorithm. This work reveals that the existing UMV, RTSF, and FSRF algorithms are essentially the particular limit of augmented Kalman filter and smoothing, which gives a new insight into the area of UISE.

Perbandingan Penginputan Transaksi Secara Manual Menggunakan Accurate pada Laporan Keuangan Input Transaksi Manual dengan Accurate di Laporan Keuangan

This abstract discusses the comparison between manual transaction input and using Accurate in financial reports. Manual transaction input method involves the process of manually entering data into the accounting system, such as recording purchase transactions, sales, and other expenses. Although this method can produce accurate financial reports, it requires more time and effort from the inputting party. In addition, the risk of error is also higher because data entry is done manually. On the other hand, using Accurate as a transaction input system can improve the efficiency and accuracy of financial reports. Accurate is an accounting software designed to assist entrepreneurs or accountants in managing company financial transactions. By using Accurate, transaction input can be done automatically through integration with various data sources, such as cash register systems, purchase systems, and sales systems. This reduces the need for manual data entry, so the required time and effort can be minimized. In addition, Accurate also comes with automatic validation features that can reduce the risk of human error in inputting transactions. This feature will check the entered data and provide warnings or notifications if there are errors or non-compliance with applicable accounting rules. Thus, the accuracy of financial reports can be improved. In this study, a comparison will be made between manual transaction input and using Accurate in financial reports. By comparing these two methods, it is hoped that information can be found to help entrepreneurs or accountants in choosing the most efficient and accurate transaction input method for their company.

Analysis of factors influencing Chinese vocabulary learning level based on big data technology

In recent years, significant progress has been made in the study of Chinese vocabulary acquisition, and the research content and scope have gradually expanded. Chinese vocabulary is the foundation for understanding and using language, and any language has developed on the basis of Chinese vocabulary. Many studies have shown that there are many factors that affect the acquisition of Chinese vocabulary, and the impact on Chinese vocabulary acquisition is also different. Among them, the influencing factors of Chinese vocabulary acquisition have gradually become a research hotspot, and a large number of related empirical studies have emerged. Based on the big data technology and the random effect model, this paper comprehensively analyzes whether the influencing factors of Chinese vocabulary acquisition as a second language are significant and investigates the effects of internal and external influencing factors, delayed post-test, specific factors and experimental interval time on the learning effect. External factors significantly impact Chinese vocabulary acquisition, including learning style, input factors and input methods. Through the subgroup analysis of the experimental interval, we find that the effect of Chinese vocabulary acquisition decreases with the extension of the experimental interval. Therefore, this paper holds an inverse relationship between the experimental interval and the development of Chinese vocabulary acquisition.

Inclusive games: Accessible game design for the visually impaired

Accessible game design is a key effort aimed at creating games that transcend physical and cognitive limitations. By carefully integrating a series of functions and strategies, designers empower players with various abilities to participate in the interactive field. Resolve visual barriers through high contrast visual effects, scalable fonts, and alternative color dependent information. Auditory elements are accompanied by visual cues or subtitles to ensure that the game narrative can be understood by auditory impairments. By utilizing customizable controls, motion sensitivity adjustments, and optional input methods, motion challenges are overcome. Recognize cognitive diversity by providing clear explanations, intuitive interfaces, and adjustable rhythms. This inclusive approach promotes innovation and encourages developers to explore new paths in game design, resonating with a wider range of users. As the gaming industry places increasing emphasis on usability, it emphasizes the industry's commitment to equal participation, while enhancing the creative process of each participant and enriching game gameplay.

Touch or click friendly: Towards adaptive user interfaces for complex applications.

This study evaluated the usability of a direct manipulation device (touchscreen) vs. indirect manipulation devices (mouse and touchpad) on the selected Microsoft (MS) Word tasks as per ISO-9241-11 standard. MS Word was taken as an example of a complex application. The tasks were evaluated in terms of touch-friendly or click-friendly using efficiency, effectiveness, and satisfaction parameters to propose a customized task menu. The experiment was conducted with fifty-four participants, divided into three MS Word usage-based expertise groups. Each participant performed fifty-six tasks using a mouse, a touchpad, and a touchscreen. To assess task-level usability, individual one-way ANOVAs were performed for each task to gauge both efficiency and effectiveness. It's worth noting that the touchscreen significantly outperformed other input methods in just one specific task regarding effectiveness. Consequently, an ANCOVA was employed, with task completion time as the independent variable and the number of errors as a covariate, to further investigate effectiveness. A total of 19 (34%) of the total tasks were found to be significantly efficient with a mouse, while 21 (37.5%) were significantly efficient with a touchscreen. Based on the results, a customized menu is recommended for MS Word-like applications that combine actions in touch-friendly tasks and mouse-friendly tasks separately.

The impact of visual and motor space size on gaze-based target selection

ABSTRACT Objective Previous studies have demonstrated that making a target larger is sufficient to compensate for the inaccuracy of gaze-based target selection. However, few studies systemically examined the individual contribution of the target’s motor and visual space size in gaze selection. We investigated how the motor and visual space size affect gaze-based target selection. Method Experiment 1 was used to examine the effect of the invisible expanded motor space in a 2D target selection task under hand-based and gaze-based control modes. Experiment 2 was used to examine the impact of the invisible expanded motor space on gaze-based target selection performance with different visual-space sizes. Results As the motor-space size increased, participants selected targets more efficiently and experienced less frustration and temporal demand; these effects were more pronounced in the gaze-based control mode. The selection was faster in the group with a larger visual-space size. Moreover, there were significant interactions between visual-space size and motor-space size on the throughput of gaze-based selection, which refers to the efficiency of input method that containing the accuracy and speed. These interactions also significantly affected endpoint distance variation and effort demand. Conclusions The motor-space size, even invisible, is the determining factor in facilitating the gaze selection performance. Additionally, the size of the visual space has a feedforward effect on selection speed and modulates the effect of motor space. These findings provide insights into the contribution of these two spaces and a new perspective to optimize the gaze-based interface.

A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.

Inverted input method for computing performance enhancement of the ion-gating reservoir

Physical reservoir computing (PRC) is useful for edge computing, although the challenge is to improve computational performance. In this study, we developed an inverted input method, the inverted input is additionally applied to a physical reservoir together with the original input, to improve the performance of the ion-gating reservoir. The error in the second-order nonlinear equation task was 7.3 × 10−5, the lowest error in reported PRC to date. Improvement of high dimensionality by the method was confirmed to be the origin of the performance enhancement. This inverted input method is versatile enough to enhance the performance of any other PRC.

The Influence of Input Method and Chinese Character Complexity on the Elderly Using Smartphones to Input Information – A Study from China

The usage of smartphones holds paramount importance in enhancing the ability of elderly people to live independently. This research aims to explore the various scenarios in which Chinese elderly people employ smartphones for information input, and how input method and Chinese character complexity affect their information input process. To achieve this, we conducted semi-structured interviews to investigate typical situations where Chinese elderly individuals use smartphones to input information. Based on the survey results, we devised an experiment to assess the impact of the input method and Chinese character complexity on the elderly’s objective physical load, mental load, input performance, and subjective experience during the inputting process. The findings revealed that the choice of input method significantly affects the objective physical load. Distinct input method engaged different major muscle groups during the process. As the Chinese character complexity increased, the elderly’s input performance declined, with handwriting input being particularly affected. Thus, we proposed enhancing the current handwriting interface by allowing users to input complex Chinese characters through handwritten pinyin, and simplify the start steps of the handwriting function based on the pinyin input interface. This study contributes to the development of evidence-based guidelines for the aging design of smart mobile devices, thus enhancing the elderly’s overall experience in using such technology.

What Visibilising Pedagogical Approaches do the Chinese as a Foreign Language (cfl) Teachers Adopt in the Teaching of Chinese Characters?

Abstract Chinese can be considered as one of the more challenging languages to learn for many non-native learners. Its complicated visual script, the Chinese characters, demands that teachers be proficient in utilizing diverse visual pedagogical approaches, coupled with different digital visual tools, to enhance their teaching effectiveness. Adopting a descriptive qualitative analysis method, interview transcripts from fourteen cfl teachers were examined. There were five online teachers, four university teachers and five pre-service teachers. The findings revealed that: 1) all the teachers use various visual pedagogical approaches, such as pictures, videos, Chinese character digital image files, Chinese character studying applications, and websites, to enhance the effectiveness of Chinese character learning, as well as motivate and engage learners; 2) teachers choose different pedagogical approaches to help students visualise characters as a whole unit or as several separate radical components; 3) the use of Pinyin to input characters on digital devices has been widely taught in class; and 4) online teachers, and some pre-service teachers, have replaced traditional handwriting methods with digital character input methods, whereas university teachers have not made this shift. The ways in which these perspectives relate to the form of Chinese characters and developing teaching practices are discussed.

Path tracking of varying-velocity 4WS autonomous vehicles under tire force friction ellipse constraints

In this paper, a control scheme is presented to solve the problem of path tracking for varying-velocity autonomous vehicles with higher robust performance while friction force constraints of vehicle tires and ground are considered to avoid appearing the steering lost situation actively in the limit of the tire friction forces by using four wheel steering. To achieve these goals, a new expression of the tire forces and a new controlled system are proposed for the three degree of freedom vehicle model. A longitudinal input-saturation controller is designed to track the desired longitudinal velocity and attenuate the influence of lateral and yaw motions. The virtual control input method is used to design the input-saturation controller and get the desired steering angles to track the desired path for the lateral and yaw motions while a new control problem with desired forces tracking is considered to autonomously avoid the limit of lateral tire friction forces and the steering lost situation appearing. To test these results, lane changing, U-corner turning and straight-line driving for a varying-velocity vehicle are simulated with external disturbances and model uncertainties. Simulation results show that the higher robustness and precise path tracking performances are obtained.

Audit Risk Assessment Model Construction by Incorporating Deep Learning Techniques

Abstract While risk-oriented auditing has gradually emerged as a new trend in current internal auditing, the absence of specific risk assessment methods in related internal auditing standards has made it a common challenge for many enterprise internal auditing organizations and personnel to effectively implement and conduct risk assessments. This research focuses on integrating deep learning technology to construct an audit risk assessment model, first describing the problem of audit risk assessment and then validating it using the CNN-LSTM method. We select the appropriate indicators for audit risk assessment assignment after screening the sample data to ensure their qualitative and quantitative comparability. We construct a CNN-LSTM audit risk assessment model based on the above data. We use two input methods, the convolutional neural network and the long- and short-term memory network, to enhance the assessment model’s learning ability. We then choose 12 enterprise companies as data cases, with Company A serving as the representative for the CNN-LSTM-based assessment model’s performance simulation test and risk level assessment. We compare the four models, and the CNN-LSTM model’s AUC exceeds 0.5 in the confusion matrix and ROC curve graph, confirming the usefulness of CNN-LSTM in audit risk assessment.

Optimal Period Input Design in FIR System Identification With Binary-Valued Observations and Event-Triggered Communication

In the identification of networked systems, the sender uses the event-triggered mechanism to reduce the times of sending data to save communication resources, but the receiver needs as much data as possible to improve the estimation accuracy. This paper tries to mediate the contradiction between the two by means of input design. First, based on the communication rate and the convergence rate of the identification algorithm, the problem of optimal input design is became an optimization problem with constraints. Then, the algorithm for solving this optimization problem is given. Next, the two-time-scale implementation method of optimal input is proposed, where the updated time of the parameter estimation constitutes the identified time scale, and the input is updated based on the estimated value of the system parameter and lasts for a period of time that constitutes the duration time scale of the input. Finally, the validity of the algorithm is verified by numerical simulation.

Experiment Design Taking Nonasymptotic Properties of the Model into Consideration

Experiment design for Identification is usually based on asymptotic theory, where an infinite number of samples is assumed. However, such an assumption does not hold in practical cases, and hence, the nonasymptotic properties of system Identification should be considered. This paper proposes a new method for experiment design for Identification based on the nonasymptotic confidence region of the system parameters calculated using the sign-perturbed-sums (SPS) method for multivariate autoregressive exogenous input (ARX) systems. The objective function based on the volume of the confidence region is introduced in the proposed method. Moreover, the proposed optimization problem is solved using Bayesian optimization because the proposed objective function is calculated from the data obtained only after the experiment. The validity of the proposed method was assessed in an experimental case study of a three-tank system, where the proposed method was compared with the existing D-optimal method. As a result, the model obtained using the proposed method reduced the mean squared control error of model predictive control by 22.9% from that of the existing method.

Stable state estimation in a network context

Power grid, communications, computer and product reticulation networks are frequently layered or subdivided by design. The OSI seven-layer computer network model and the electrical grid division into generation, transmission, distribution and associated markets are cases in point. The layering divides responsibilities and can be driven by operational, commercial, regulatory and privacy concerns. From a control context, a layer, or part of a layer, in a network isolates the authority to manage, i.e. control, a dynamic system with connections into unknown parts of the network. The topology of these connections is fully prescribed but the interconnecting signals are largely unavailable, through lack of sensing and even prohibition. Accordingly, one is driven to simultaneous input and state estimation methods. This is the province of this paper, guided by the structure of these network problems. We study a class of algorithms for this joint task, which if the system has transmission zeros outside the unit circle leads to an unstable and unimplementable estimator. Two modifications to the algorithm to ameliorate this problem were recently proposed involving (a) replacing the troublesome subsystem with its outer factor from its inner-outer factorization or (b) using the Kalman filter for a high-variance white signal model for the unknown inputs. The outer factor has only stable transmission zeros and so is stably invertible. The Kalman filter is stable by design. Here, we establish the connections between the original estimation problem for state and input signal and the outputs/estimates from the algorithm applied solely to the outer factor. We further show that both fixes coincide.

A numerical platform for predicting the performance of paper-based analytical devices.

This article presents a numerical platform for predicting the performance of paper-based analytical devices. The capillary flow, reaction, dissolution, and other physicochemical phenomena associated with device operation are accounted for using Darcy's law, Richard's equation and other transport equations. The platform can be used for different paper substrates, biorecognition methods, detection systems (such as optical and electrochemical detection), device patterns and dimensions, and ways in which the device is operated such as the input method of the body fluid. The device performance is quantified using indicators such as assay time, signal strength and product cost. The predictive capability of this numerical tool is verified with devices reported in the literature. It is shown that the platform can be used to identify possible improvements to these existing devices. More importantly, it can also serve as a numerical tool for synthesizing new paper-based analytical devices with minimum experimental effort.

Augmented Machine Intelligence with Human Intelligence for Cryptocurrency Price Prediction

This research explores the integration of human and machine intelligence in the financial industry. While AI systems excel at analyzing data, humans possess unique traits that contribute to accurate predictions. Inspired by the concept of Augmented Financial Intelligence, the study aims to integrate human intelligence with existing models, considering the limitations of current human input methods. Natural Language Processing and Sentiment Analysis are used to enhance prediction tasks, but limited training data and suboptimal processing can introduce noise. The study introduces a framework that integrates human and machine intelligence to enhance cryptocurrency market forecasts, analyzing six cryptocurrencies and utilizing an Elo-based rating system to identify exceptional predictors. The framework aims to minimize noise and optimize human input in financial forecasting.