Reviewerly/exHarmony huggingface dataset:
Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine

Due to the limited number of labeled samples, semisupervised learning often leads to a considerable empirical distribution mismatch between labeled samples and unlabeled samples. To this end, this paper proposes a novel semisupervised algorithm named Local Gravitation-based Semisupervised Online Sequential Extreme Learning Machine (LGS-OSELM), learning to unlabeled samples follows from easy to difficult. Each sample is formulated as an object with mass and associated with local gravitation generated from its neighbors. The similarity between samples is measurable by the local gravitation measures (centrality CE and coordination CO). First, the LGS-OSELM uses the labeled samples to learn the initialization model by implementing ELM. Second, the unlabeled samples with a high confidence level that is easy to learn are labeled with the pseudo label. Then, these samples are utilized to iterate the neural network by implementing OS-ELM. The proposed approach ultimately realizes effective learning of all samples through successive learning unlabeled samples and iterating neural networks. We implement experiments on several standard benchmark data sets to verify the performance of the proposed LGS-OSELM, which demonstrates that our proposed approach outperforms state-of-the-art methods in terms of accuracy.
Vehicle Accident Detection and QR Code Based Information System

This project entails the development of a real-world Android application in which the user can easily coordinate and maintain all of the relevant facts about a car (such as the registration certificate, insurance, and vehicle's Pollution Under Control Certificate). Quick Response Code (QR-Code) is a very efficient and quick-to-read technique used to scan a variety of items. A vehicle's specifics A secondary application for a vehicle's inspecting authority is also produced to check the information. The vehicle's credentials the authority must scan a unique QR-Code (issued separately for each vehicle inspection) into the smartphone app is a good idea. The basic UI (User Interface) enables for faster identification of objects. The availability (or lack thereof) of key paperwork relevant to that particular car. These are the applications that aid in the saving of a substantial amount of time and resources now spent on this activity In light of the security threats posed by, In addition to exposing user's personal information, more attention has been paid to data security through the use of QR codes and access control to those particulars.
Survey of Approaches to Non-Realistic Neural Style Transfer

Despite researchers interest toward style transfer problem, there is still no foremost method available. Difficulties in problem formalization make a comparison of methods especially complicated. This paper covers twelve widely used algorithms in order to provide their comprehensive description and advantages one over another.
Research on fixing the best parameter to image denoising by proximity algorithms

Algorithms play an extremely important role in daily life. Most algorithms have some basic custom parameters. When using the algorithm, the parameter value greatly affects the speed and optimal result of the algorithm. The purpose of this paper is to explore the ideas of efficient parameter adjustment and avoid spending much time on invalid experimental operations. In order to get closer to the current research frontier, this paper takes the proximity algorithm of the denoising image model as an example. This paper introduces the current hot image denoising and introduces the fixed point proximity algorithm of the experimental object. In this paper, the denoising effect of the model is tested by adding noise to grayscale images randomly. The compiler uses MATLAB to facilitate matrix calculation. The experimental study focuses on gradually reducing the optimal solution pair value range of the algorithm parameters from the perspective of the resulting image, the statistical regression analysis of the index, and the iterative convergence speed.
Wearable-Based Virtual Display Information Processing and Data Fusion Research

This paper is combined with the existing acquisition module design to achieve the upper computer online information monitoring system. Real-time mapping technology is adopted to change the trend of data collection potential for real-time tracking and monitoring. The monitoring data can be predicted and analyzed by changing trend, at the same time combined with SQL2008 database technology, user login system, registration system, monitoring system, data query, and data storage system. The integration and other functions are improved, so that the system not only has the advantages of information management platform but also realizes the remote client base matching layer wireless information real-time monitoring function. Data fusion technology refers to the information processing technology that uses computer to automatically analyze and synthesize some observation information obtained in time and sequence under certain criteria, so as to complete the required decision-making and evaluation tasks. The intelligent wearable online information monitoring system designed in this paper realizes wireless sensor network, to some extent feedback and monitoring of underlying real information. Through the corresponding information processing and data fusion, the user can easily and clearly get product information. Based on the existing 80 sets of data, the experiment trains and extracts 320 feature vectors, which verify the effectiveness of the method.
Capacity Loss Analysis Using Machine Learning Regression Algorithms

In this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the data from this capacitor were recorded and tests were carried out using Machine Learning (ML) algorithms for its remaining useful life. ML algorithms were selected from the algorithms that used for regression problems. In the study, Support Vector Machine (SVM), Linear Regression (LR) and Regression Trees (RT) algorithms were used. The rated powers of the analyzed capacitor are 50kVAR and 25kVAR from the active plant. The data set was created by running the capacitor continuously for 6 months and the capacity loss was examined with using ML algorithms. The algorithm that gives the best result in the regression analyzes is the LR algorithm. With the results obtained, it is possible to analyze how long the useful life of capacitors with the same characteristics have under the same stress.
I Am Me

Sam is a transgender individual navigating work and life. Linda, a colleague of Sam's, is an older employee who knows her way around their workplace, Living Life, an organization focused on assisting (dis)abled individuals with a range of life skills. When Linda discovers Sam's gender identity, Linda withdraws and changes the way she interacts with Sam. John attempts to understand both perspectives but is not given enough information to act in one way or the other. The case is explored using transgender standpoint epistemology, social penetration theory, expectancy violations theory, and transgender standpoint epistemology.
Constructing Swimming Training Model Based on Cyclic Coordinate Descent Algorithm

In this paper, the cyclic coordinate descent algorithm is used to solve the target attitude of the constrained frame. The motion is smoothed by constructing the motion offset mapping curve near the constraint frame. The motion editing and redirection can be accomplished by adjusting the displacement ratio of root nodes and estimating the initial position. The cyclic coordinate descent algorithm generates the keyframe actions corresponding to the key moments in the virtual human swimming movement cycle division. Then, the spherical linear interpolation based on four elements is used to generate the intermediate frame action in the swimming movement. The simulation results of swimming training show that the method can generate a relatively smooth human swimming process. Finally, a simple simulation of the swimming training model based on the cyclic coordinate descent algorithm is carried out to achieve a more realistic simulation effect. The experimental results show that this method is relatively complete and retains the characteristics of the original motion well, and the redirected motion is smooth and natural.
Interaction Design of Mobile-Based Product Display System

Merchandise display design is a type of targeted design that involves the planning and implementation of merchandising, product display, visual communication, and the display of the retail environment in order to increase sales and suit the needs of both producers and customers. In this paper, we propose a mobile-based merchandise display system for the needs of merchandise display and virtual reality shopping in e-commerce. First, this paper analyzes the characteristics of several VR technologies and applies the WebVR 3D interaction engine to realize the online 3D virtual display of real-world and physical products to provide real-time interaction. Furthermore, this paper proposes a behavioral delay shared network model that fully combines historical browsing information to make accurate browsing recommendations to users. For computing the user’s interest representation, the model provides behavioral delay gated recurrent neural units. Finally, to provide a solution for mobile e-commerce apps, the behavioral delayed shared network model is implemented into the display system’s recommendation module.
Prototype for Crowd-based Co-creation of Artificial Intelligence Natural Language Conversational Agents

There is growing need for intelligent digital support in healthcare, business, industry, indeed all domains. Learners in any domain need educators/experts to provide knowledge and guidance by relaying relevant and timely information. Artificial Intelligence (AI) systems such as chatbots or conversational agents, can support information delivery. However, user-centric frameworks for designing chatbot learning applications and collaborative user-centric design methodologies are lacking. This paper describes a chatbot design tool to promote co-creation through crowd-based data collection and design. This prototype has the capacity to be used as a structured method of design and data collection through cocreation for healthcare, education, and beyond.
Real-Time Communication Control in Decentralized Autonomous Sensor Networks

Minimizing the amount of communication required by a sensor network is crucial to minimizing both energy and time consumption, as well to operating covertly and robustly in communication-contested environments. This paper presents a novel intermittent communication control approach applicable to sensor networks deployed to sense and model spatio-temporal processes by nonparametric models such as Gaussian processes (GPs). The approach relies on a novel and efficient approximation of the GP average generalization error (AGE), as well as on novel GP sensor control and regression methods presented in this paper. This novel AGE approximation allows each sensor to characterize the nominal prediction performance of the learned GP model in the absence of communications. As a result, individual sensors can update the GP hyperparameters based solely on local measurements and decide to communicate only if and when their estimate of the nominal prediction performance falls below an acceptable threshold.
Facial Detection Attendance System using LBPH and KNN

Present procedure for marking attendance is exhausting and prolonged so this paper is consequently put forward to challenge all these complications. This paper hence evolve a model to distinguish each personality's face from a seized image using a set of conditions i.e. LOCAL BINARY PATTERN HISTOGRAM algorithm to track the student attendance. The overall working of this local binary pattern histogram algorithm was, first the image is divided into m*m grids. For each grid histogram is calculated in order to easily recognize the spatial features. After calculating binary pattern histogram for each cell. The results were coupled to obtain the final feature vector. This final vector is compared with vectors in the training data set using K-Nearest Neighbor's algorithm. By this algorithm the value which is closest to our final vector is obtained as a result of classification. After receiving the name of the person, the attendance of the particular person is updated in the database. This proposed algorithm decreases the work load and records routine performance of maintaining each student and further makes it easy to note the attendance.
Bringing digital resource decision-making up to date: preparing the inaugural class of digitalised preservice teachers for informed evaluation of digital resources

The virtualised schools and universities of the Covid-19 pandemic became rely heavily on educational digital resources (EDRs), so that it has made the selection and use of high-quality EDRs even more critical for quality education. This qualitative case study aims to examine decision-making criteria used by preservice teachers (PTs) in selecting and evaluating EDRs. Twenty senior PTs participated in the study, and each evaluated four EDRs using guided prompts. Open coding and text analysis on 77 EDR evaluations were conducted. Results indicate that 41 EDRs were selected for the evaluation, and PTs’ prior experience in the field, both as a teacher or an observer, influenced their selection process. Features of EDRs considered in decision-making process included types of EDRs, pedagogical and maths skill purpose, ready-made or adaptable, interactive or one-way, and cost. Also, five decision-making criteria used by PTs in EDR selection and use were the primary benefit holder, function of EDRs, opportunity for improving mathematical skill, affordances, and constraints. Findings urged framing a new generations’ perspective on evaluating EDRs. The ways to support PTs for better informed selection and implementation of EDRs were discussed.
Improving self-organizing recursive fuzzy neural network’s performance with Boston matrix

Aiming at the problem that fuzzy neural network (FNN) is difficult to be adjusted automatically its structure when there is no the threshold of loss function, as well as the problem that the neuron number of the regularization layer of FNN is adjusted by self-organizing algorithm when the structure of FNN is not stable yet, a structural design strategy of self-organizing recursive FNN based on the Boston matrix (SORFNN-BOSTON) is proposed. Compared with other self-organizing algorithms, the method used in this paper does not need to set the threshold of loss function. In addition to the indicators representing the importance of neurons in most self-organizing algorithms, the change rate is used to represent the change of the parameters of the neural network. The change rate is used to determine when the relevant parameters are stable, which further improves the reliability of the neuron adjustment process. Through the simulation of predicting Mackey-Glass time sequence, the final number of neurons in the hidden layer and the testing error are 6 and 0.110 respectively. Comparisons with other self-organizing algorithms show that the testing error decreased by 76.6% at most and 13.3% at least, which proves the practicability of the method.
Utilization of MultiLevel Drawing Technology for Computer-Based Embroidery Employing Computerized Digital Technology

In this study, the theoretical foundation of the manually operated crewel embroidery technology was suggested to enhance the artistic features of computerized embroidery. By doing so, the advantages such as mass production, more realistic pictorial patterns, and increasing artistic demand by users can be realized. Hence, the embroideries characterized by the computerized mechanical and crewel methods could be compared. Therefore, interesting results were obtained. The software called WILCOM and a single head computerized embroidery machine with 12 needles having multifunctionality called TUMX-C1201 were utilized to produce proofing size designs of embroidery frames with a selection of a base needle, color, and style of the pattern for the layered combination. Then, both computerized mechanical embroidery and crewel embroidery generate binding assays. The outcomes suggest that proofing samples with the gray value of 15 groups utilizing stitch spacing varying between 0.2 mm and 3 mm designate a gradual inclination in the upward direction when different bases of needles, namely, 10 mm, 20 mm, and 30 mm are under consideration. In other words, an obvious trend and a stable state were simply observed when they change between 0.2 mm and 1.8 mm and between 1.8 mm and 3 mm, respectively. For example, when a 50 × 50 mm area was taken as an illustrative example, 102, 24, and 5 base needles were needed, respectively. As a result, certain characteristics were observed in the visual style when computerized mechanical crewel embroidery was employed, which is, for example, a certain degree of looseness contained in the stitching of the petal pattern that created vividness and enhanced hierarchical sensation. Besides, the petals of a sunflower in three dimensions were seen concurrently. Thus, the computerized digital technology enables computer-based embroidery to draw patterns in multilevel artistic processes and was concluded. Combining manual and computer-based embroidery could not only succeed in mass production but also enhance the aesthetic aspects of embroidery works.
Improving Locality of Irregular Updates with Hardware Assisted Propagation Blocking

Many application domains perform irregular memory updates. Irregular accesses lead to inefficient use of conventional cache hierarchies. To make better use of the cache, we focus on Propagation Blocking (PB), a software-based cache locality optimization initially designed for graph processing applications. We make two contributions in this work. First, we show that PB generalizes beyond graph processing applications to any application with unordered parallelism and irregular memory updates. Second, we identify the inefficiencies of a PB execution on conventional multicore processors and propose architecture support to further improve the performance gains from PB. Our proposed architecture, COBRA, optimizes the PB execution of a range of applications with irregular memory updates, offering speedups of up to 3.78x compared to PB (1.74x on average).
Improving Non-Autoregressive Machine Translation Using Sentence-Level Semantic Agreement

Theinference stage can be accelerated significantly using a Non-Autoregressive Transformer (NAT). However, the training objective used in the NAT model also aims to minimize the loss between the generated words and the golden words in the reference. Since the dependencies between the target words are lacking, this training objective computed at word level can easily cause semantic inconsistency between the generated and source sentences. To alleviate this issue, we propose a new method, Sentence-Level Semantic Agreement (SLSA), to obtain consistency between the source and generated sentences. Specifically, we utilize contrastive learning to pull the sentence representations of the source and generated sentences closer together. In addition, to strengthen the capability of the encoder, we also integrate an agreement module into the encoder to obtain a better representation of the source sentence. The experiments are conducted on three translation datasets: the WMT 2014 EN → DE task, the WMT 2016 EN → RO task, and the IWSLT 2014 DE → DE task, and the improvement in the NAT model’s performance shows the effect of our proposed method.
Intelligent Robot English Speech Recognition Method Based on Online Database

In order to solve the problem of low accuracy of traditional English speech recognition, an intelligent robot English speech recognition method based on online database is proposed. A speech recognition device is installed on the intelligent robot as the hardware support for running the speech recognition method. The online English speech standard database is constructed to provide reference data for speech recognition. The real-time speech information is collected, and the speech signal is preprocessed by pre-emphasis, framing, windowing and other steps. According to the principle of speech signal generation, the features of speech signal are extracted, and the results of English speech recognition are obtained by similarity calculation and matching. Compared with the traditional recognition method, the experimental results show that the recognition rate of the optimised speech recognition method is improved by 1.3%, i.e. the recognition accuracy is improved.
Research on the Development Path of Manufacturing Industry and Its Economic Effect Based on Computational Visualization

In order to improve the analysis effect of the manufacturing development path and improve the economic benefits of the manufacturing industry, this paper combines the computer visualization technology to analyze the manufacturing development path and its economic effect. Moreover, this paper introduces the minimum ill-posedness criterion in the optimal placement criteria of time-domain sensors and the modal guarantee criterion in the optimal placement criteria for frequency-domain sensors. In addition, in this paper, the optimal arrangement of sensors is carried out based on the above two criteria, and the improved monkey swarm algorithm is applied to the optimization problem of the sensor of the visual system, and an intelligent computer visualization system is constructed. The experimental research results show that the research model of the manufacturing development path and its economic effects based on computational visualization proposed in this paper has certain practical effects.
Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems

We propose an amplitude shift keying-type asymmetric quantum communication (AQC) system that uses an entangled state. As a first step toward development of this system, we evaluated and considered the communication performance of the proposed receiver when applied to the AQC system using a two-mode squeezed vacuum state (TSVS), the maximum quasi-Bell state, and the non-maximum quasi-Bell state, along with an asymmetric classical communication (ACC) system using the coherent state. Specifically, we derived an analytical expression for the error probability of the AQC system using the quasi-Bell state. Comparison of the error probabilities of the ACC system and the AQC systems when using the TSVS and the quasi-Bell state shows that the AQC system using the quasi-Bell state offers a clear performance advantage under specific conditions. Additionally, it was clarified that there are cases where the universal lower bound on the error probability for the AQC system was almost achieved when using the quasi-Bell state, unlike the case in which the TSVS was used.