Reviewerly/exHarmony huggingface dataset:
Principal Component Research of the Teaching Model Based on Multimodal Neural Network Algorithm

With the deepening and improvement of the contemporary English educating reform, the lookup on the satisfactory English training has attracted greater and extra attention. The key to enhance the English training is to enhance good teaching, and English teaching model is the key measure to enhance good schooling and teaching. Based on a single neural network, it can solely describe the randomness and irregularity of English education quality and cannot describe the whole exchange traits of English education model, which makes the impact deviation of teaching model larger. Based on the in-depth learning of the contemporary state of affairs and traits of English education model, blended with the traits of neural network, this paper constructs an English teaching model primarily based on multimodal neural network algorithm. The experimental results show that the convergence speed of multimodal neural network model is 76% higher than that of single network model, the sum of squares of average error is 79%, and the average evaluation accuracy is 13.99% and 6.42% higher than that of convolution neural network model and radial basis function neural network model, respectively. It is demonstrated that the multimodal neural network model does not accelerate the convergence speed of the network or improve the prediction accuracy of the model and can quickly realize the ability of global optimization. It shows the effectiveness and accuracy of using multimodal neural network algorithm to model English teaching quality and provides a feasible solution for teaching quality model.
The development of a glove-like controller interface for VR applications

Smart gloves are wearable interfaces which provide detection of movements and gestures, kinesthetic, and even tactile feedback. These capabilities are important for immersive applications as it can bring more realistic features. The presented paper shows a concept of a working in progress application of a smart glove. The proposed hardware uses resistive flexible and electromagnets sensors to provide tactile and haptic feedback to be used in immersive applications such as the ones that use Virtual Reality (VR). The project will be done in a way that can provide user-centric requirement such as: the glove must be lightweight, comfortable, adjustable to different hands and finger shapes, and low cost to be manufactured. The intend is to use this glove as part of a biggest framework and immersive application.
Non-Uniform Blind Image Deblurring Using an Algorithm Unrolling Neural Network

In this work we develop a compact neural network that is designed to deblur images that have been affected by a non-uniform blur. We develop this network by unrolling a traditional iterative image deblurring algorithm and adapt it to independently deblur regions of an image. The network is evaluated by comparing its deblurring capabilities with that of other state-of-the-art networks, such as the SRN-Deblur network [13] and the DUBLID network [4]. We investigate the effect that varying the patch size and the size of the point spread function has on the deblurring performance of our network. We evaluate each deblurring network using the industry standard GOPRO [12] and Kohler [25] datasets.
Source Identification of Documents Based on LOOP Features

Pinpointing the ownership of documents based on printers is a challenging task. However, many methods have been proposed to identify the printers through printed documents. In this task, the chapter explores a method in simple way to identify the laser printer models. The LOOP (local optimal oriented pattern) method is applied to discriminate 10 laser printer models based on the character images. LOOP is an efficient descriptor to differentiate the images. A 10-fold cross-validation technique is used to classify the printers. The classifiers, namely Linear SVM (support vector machine) and Quadratic SVM, are applied to classify the laser printer models based on the documents at a character level. The experimental result shows that the proposed method is robust and outperforms comparable counterparts in the literature survey. The authors have achieved an average accuracy of Linear and Quadratic SVM classifiers of 99.2% and 99.8%, respectively.
On the Convergence of Multi-robot Constrained Navigation: A Parametric Control Lyapunov Function Approach

This paper studies the distributed multi-robot constrained navigation problem. While the multi-robot collision avoidance has been extensively studied in the literature with safety being the primary focus, the individual robot's destination convergence is not necessarily guaranteed. In particular, robots may get stuck in the local equilibria or periodic orbits of the multi-robot system, some of which are practically known as the deadlock and the livelock behaviors. Inspired by the combination of Control Lyapunov Function (CLF) and Control Barrier Function (CBF) for the nonlinear system's constrained stabilization, the authors present a guaranteed safe feedback control policy with improved convergence performance. The proposed Parametric CLF (PCLF) scheme adaptively determines the appropriate CLF parameterization within the in-stantaneous feasible action space. The algorithm also induces a conditional global asymptotic convergence guarantee for multi-robot system of single-integrator dynamics, and is empirically effective for nonlinear nonholonomic vehicle model. Empiri-cally, the proposed PCLF-CBF framework exhibits superior performance than state-of-the-art methods, including its de-generated counterpart of various CLF-CBF solutions.
Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks

Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. Specifically, we propose a novel deep neural model called CopyFet for FET via a copy-generation mechanism. CopyFet can integrate two operations: (i) the regular way of making type inference from the whole type set in the generation model; (ii) the new copy mechanism which can identify the semantic type of a mention with reference to the type-copying vocabulary from a knowledge graph in the copy model. Despite its simplicity, this mechanism proves to be powerful since extensive experiments show that CopyFet outperforms state-of-the-art methods in FET on two benchmark datasets (FIGER (GOLD) and BBN). For example, CopyFet achieves the new state-of-the-art score of 76.4% and 83.6% on the accuracy metric in FIGER (GOLD) and BBN, respectively.
Uncertainty from Motion for DNN Monocular Depth Estimation

Deployment of deep neural networks (DNNs) for monocular depth estimation in safety-critical scenarios on resource-constrained platforms requires well-calibrated and efficient uncertainty estimates. However, many popular uncertainty estimation techniques, including state-of-the-art ensembles and popular sampling-based methods, require multiple inferences per input, making them difficult to deploy in latency-constrained or energy-constrained scenarios. We propose a new algorithm, called Uncertainty from Motion (UfM), that requires only one inference per input. UfM exploits the temporal redundancy in video inputs by merging incrementally the per-pixel depth prediction and per-pixel aleatoric uncertainty prediction of points that are seen in multiple views in the video sequence. When UfM is applied to ensembles, we show that UfM can retain the uncertainty quality of ensembles at a fraction of the energy by running only a single ensemble member at each frame and fusing the uncertainty over the sequence of frames. In a set of representative experiments using FCDenseNet and eight indistribution and out-of-distribution video sequences, UfM offers comparable uncertainty quality to an ensemble of size 10 while consuming only 11.3% of the ensemble's energy and running 6.4× faster on a single Nvidia RTX 2080 Ti GPU, enabling near ensemble uncertainty quality for resource-constrained, real-time scenarios.
The Structural Framework Design on Information Management System of University Funding

With the increase in the number of college students, the problem of family poverty among college students has also attracted the attention of colleges and universities. There are still many problems to be solved in the link of university funding. This article takes college students as the management object and designs a set of college funding information management system to complete the information management of college funding information for college students. The system adopts distributed and modular design ideas, divided into two parts, software and hardware, and uses MySQL as the back-end database to research and design a set of B/S university funding information management system. The system not only improves work efficiency but also ensures the safety and accuracy of data. We first tested the pressure performance of the system, and the final result shows that the system has good robustness. Then, we investigated the attitudes of impoverished students from 120 colleges and universities towards the information management system of college funding from 2016 to 2021. Combining with the data obtained from the survey and sorting and analyzing the data, the survey results show that at least 83.54% of poor students believe that this system has a greater effect on the information management of poor students. In addition, more than 64.31% are quite satisfied with the system; of course, 19.01% of them are unclear. Therefore, it can be considered that the system has a large effect on information management.
Design for Equivalence

This paper introduces design for equivalence, an approach to participatory design (PD) that centers systematically excluded individuals and groups and their matters of care, while scaffolding different types of expertise, to address social injustice. Design for equivalence is a theoretical contribution that emerges from a critical analysis of Chantal Mouffe's theory of agonistic pluralism in PD. In this paper, we first review Mouffe's conceptualization of agonism arguing a key construct of her theory—the chain of equivalence—has been absent in recent PD formulations of agonistic pluralism that seek to create collective, counter-hegemonic, and emancipatory responses to complex social problems. Next, we discuss the implications of these formulations relative to PD practices, participants, and outcomes. Lastly, we develop an approach that offers a set of provisional, practical guiding principles for PD processes to challenge structural inequality by creating articulations of the common good that are strong enough to counter power.
Factors Affecting Students’ Satisfaction with University College of Applied Sciences, Gaza

This research aims to examine factors that influence student satisfaction with University College of Applied Sciences (UCAS), Gaza. To collect data from all UCAS students, a questionnaire was developed utilizing a descriptive-analytical technique. Multiple regression analysis was used to evaluate the data from 317 respondents. The findings revealed that the independent variables (quality of education services, cost of education services, and physical environment) had an impact on the dependent variable (satisfaction of UCAS students). While this research adds to previous work, it also provides data that academics and practitioners can use to improve student satisfaction.
The Status of Using E-learning Platforms Among the University College of Applied Sciences Students to Enhance Their Soft Skills

The study aimed at exploring the degree to which the University College (UCAS) students use e-learning platforms to enhance their soft skills. The researchers used the descriptive approach, on a random sample of (350) students. The researchers designed a questionnaire with five domains (communication, time, teamwork, planning, problem solving). The study revealed that there are no differences in the estimation of UCAS students for the use of e-learning platforms in enhancing their soft skills due to the variables of gender and university specialization. It also showed that there are differences in the estimation of UCAS students in using e-learning platforms to enhance their soft skills in favor of the second-level students compared to first-level students. Accordingly, the researchers recommend motivating the UCAS faculty members to employ e-learning platforms in the educational process, and training students through some specialized courses on the use of e-platforms for academic and freelance purposes.
Prototype Design of Smart Building Control System Based on Human Demand Information

Intelligent environmental control system and smart buildings have gradually entered the public view, and users can adjust the indoor environment according to their personal preferences. However, the accuracy of control systems is susceptible to the diversity and sensitivity of user behavior. In addition, although some existing products have begun to focus on the user experience, there are still shortcomings. This paper designs a human-centric prompt interface design for winter teaching indoor space. The combination of interface design and indoor environmental control system can make accurate decisions based on user feedback. Moreover, this paper discusses the user’s acceptance of this mobile panel program through questionnaires and interface usage feedback and the direction of future research.
Domain adaptation metric learning method embedded with structural information for person re‐identification in internet of autonomous unmanned vehicles

Abstract Internet of autonomous unmanned vehicles (IAUV) is a global network of sensors, robots, and autonomous vehicles. Person re‐identification (Re‐ID) is an important intelligent transportation application in IAUV, which needs to be solved using artificial intelligence algorithms. In this study, a domain adaptation metric learning method embedded with structural information (called DAML‐ESI) is designed for person Re‐ID in IAUV. Due to the lack of labeling information in the target domain, DAML‐ESI realizes person Re‐ID with the help of the discriminative and structural information of pedestrian images of related domains. DAML‐ESI projects pedestrian images selected from different domains into a common metric space and establishes a discriminative metric learning model, which requires that the positive sample pair be mapped to a point, and the distance distribution of the negative sample pair be mapped to a fixed value. The projection matrix learned by DAML‐ESI is used to eliminate the distribution differences between different domains, and the distance metric is used to ensure that the learned metric learning model has strong discriminative ability in the metric space. To verify the effectiveness of DAML‐ESI, experimental comparisons are conducted on three person Re‐ID datasets, and DAML‐ESI achieves satisfactory recognition performance.
Maximizing the Score and Minimizing the Response Time in Scrabble Game

The perfection of information is an important notion in games when considering sequential and simultaneous game. It is a key concept when analyzing the possibility of strategies and decision making for the next move. Scrabble board game is an imperfect information game where each opponent is unaware of the letters on the opposite rack. It is board game of luck, vocabulary and strategy. Our computer board game strategy focuses on maximizing the score and minimizing response time while searching for the best word possible from letters through the dictionary of words where best word could be a word containing all letters of rack and with best scoring letters (Vyas and Gahlot in A theoretical approach to reinforcement learning algorithm and Nash equilibrium to maximize the score in imperfect information game. Springer, Singapore, 2021) [1].
An Automatic GUI Generation Method Based on Generative Adversarial Network

As a technique applied with artificial neural networks, deep learning is widely used in the field of image recognition. However, a lack of available datasets leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in graphical user interface (GUI) generation, it was found that the collection of GUI datasets is a time- and labour-consuming project. This makes it difficult to meet the dataset needs of current deep learning networks. To solve this problem, we propose the user interface generative adversarial network (UIGAN), a semi-supervised deep learning model, to produce a large number of reliable GUI datasets. By combining a cyclic neural network with a generated countermeasure network, UIGAN can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the selected Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to them. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.
Using Case-Based Reasoning in System Diagnostics and Maintenance

Taking optimal decisions in many cases is related to knowledge, acquired in a relevant domain. The amount of information that forms that knowledge directly affects the quality of decisions. Neither large nor too small amounts of information lead to perfect problem solutions in areas where knowledge is particularly important. The use of case-based reasoning systems can help improve performance in situations where such knowledge is scarce or non-existent at all. Our goal is to offer a model of a system that will help diagnose and solve problems met by users when they use technical products, consisting of many software and hardware components.
Image Recognition to Detect COVID-19 Violations: Saudi Arabia Use Case

The upsurge in the number of criminal cases in Saudi Arabia is a cause for concern. More so, with the recent emergence of COVID-19, the government has forbidden specific social behaviors, which means that any breach of these prohibitions will be classified as a criminal. This work leverages the immense ability of deep learning architectures to develop and evaluate models to detect images of people or a person either violating or observing COVID-19 rules. For instance, an image of a person/s wearing a face mask would definitely fall under the category of non-violation, whereas an image of people hugging or shaking hands is an indication of a violation of COVID-19 rules. The model is trained and evaluated on a bunch of images that we have extracted from social media sites, and it produces exceptional results in the image classification assignment that we have performed.
Organization of Training in the Art Education Institution in the Context of the COVID-19 Pandemic

The pandemic COVID-19 has caused significant changes in many areas of public life, including education. This article introduces how the pandemic COVID-19 affected higher education and how it can reduce the negative impacts of the pandemic and respond to future challenges with the use of ICT. Problems and consequences related to the educational process in educational institutions of Ukraine in the conditions of quarantine restrictions are analyzed. The experience of Kyiv National University of Culture and Arts in adapting the educational process to today's challenges is presented. The main directions of the university's work on reducing the consequences of the pandemic and ensuring the stability and flexibility of the educational process in the conditions of quarantine restrictions are revealed. Accented the importance of developing digital competence for all participants in the learning process.
Evolutionary Tool for Denoising DNA Microarray Images Using CNN

Deoxyribonucleic acid (DNA) microarray technology has promised rapid improvement in recent studies. On DNA microarray images, there are several spots. Spots on microarray images represent gene expression and show the status of normal and malignant cells. One of the approaches for enhancing and analysing an image is to use digital image processing. In this paper, a convolutional neural network (CNN)-based denoising technique is developed and deployed. It is based on feature extraction from images. It is no longer necessary to manually extract features. The simulation is carried out using MATLAB to evaluate the performance parameters like peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structural similarity index (SSIM). The comparison between different filtering techniques and CNN is carried out checking for the efficiency. The traditional image noise reduction methods such as median filter method, linear filter method and Weiner filter methods are compared with CNN. The advantage is that the CNN model’s parameters may be fine-tuned by network testing and training, whereas the parameters of traditional image denoising methods are fixed and cannot be changed throughout the filtering process and resulting in a lack of configurability.
Vehicle detection method based on adaptive multi-scale feature fusion network

To improve the vehicle detection accuracy and solve the problem that small vehicles are difficult to detect, an adaptive multi-scale feature fusion network (AMFFN) is proposed to deal with the multi-scale problem, and better performance is achieved after applying it to you only look once (YOLO) v4. To improve the representation capability of features, spatial pyramid pooling modules were employed on each feature map. The proposed AMFFN fuses features of multiple scales across layers and assigns learnable weights to layers of different scales. To achieve detailed information better, we select dynamic rectified linear unit as the activation function, which can change dynamically with the input. AMFFN can be treated as a reusable module to obtain more refined features by repeatedly fusing features. To avoid the huge amount of parameters caused by the complex network, depthwise separable convolution is used to replace the normal convolution and increase the speed of detection. Experimental results show that the proposed method has higher detection accuracy and faster detection speed, and the performance is better than that of YOLO v5, which is the latest version of the YOLO series of algorithms.