Reviewerly/exHarmony huggingface dataset:
The design of university resource sharing system based on alliance chain

In the future construction of teaching resource sharing, if there is no better technology before the emergence of blockchain technology can be regarded as one of the important technologies in the system design. Nowadays, most universities and enterprises use multi-department distributed management, which leads to the scattered storage of teaching resources and the diversification of resource query platform, and it is difficult to achieve unified management and detection of educational resources, which not only increases the burden of users, so that users can not easily and quickly find learning resources. So in this paper, according to the universities and enterprises in the resource sharing of the problem of low efficiency, design a resource sharing system based on block chain technology, this system can well solve the universities and enterprises at present problems in the mode of resource sharing, improve the efficiency of resource sharing, especially for teaching resources and teaching resource allocation imbalance, low utilization rate is crucial, It can solve the problems of scattered teaching resources, lack of high-quality resources, high cost of platform management, weak copyright protection, difficult to guarantee resource security and so on.
EPT (Electrically Propelled Tricycle)

The term electrically-propelled refers to a class of technologies that uses electric power to propel the vehicle with batteries rather than making use of I.C. engines. EPT (Derive from electric propelling) uses electric energy to propel the tricycle. With EPT, a disabled person can travel from one place to another place reducing fatigue. EPT is one such vehicle that can be used by most disabled people who cannot afford a standard electric wheelchair. EPT uses E.V. (electric vehicle) technology which uses electric power to propel the vehicle from one place to another with best-in-class efficiency. This technology is widely spread in human life all around the globe. The most appealing feature about EPT is that it has a manufacturing price that is 5X more minor than the electric wheelchair. Most people handicapped from legs in a government hospital require 15-20 min to have a person assisting them to move on their conventional wheelchair. With EPT, they need not face such a problem as it is electrically propelled. The person can move without taking anyone's help for assistance. It uses the battery and electric controller kit technology, which is currently reliable to propel electric vehicles. Compared to other electric wheelchair technologies, the difference in price makes EPT unique.
The Cryptic Case of the CryptoPunks Licenses: The Mystery Over the Licenses for CryptoPunks NFTs

The CryptoPunks NFT collection, produced by Larva Labs, is the highest-grossing NFT collection to date. Sales have surpassed $1.6 billion. That figure is all the more astounding given that Larva Labs gave away all 10,000 CryptoPunks NFTs for free in 2017. Christie’s has described the CryptoPunks as “the alpha and omega of the CyptoArt movement.” Adding to their mystique is a legal mystery: What content license governs the use of the CryptoPunks artwork and characters? Shockingly, Larva Labs distributed the 10,000 CryptoPunks NFTs without any written content license in 2017. Apparently, one of the Larva Labs co-founders John Watkinson later adopted the open-source NFT License in 2019 to apply to the CryptoPunks NFTs. But that adoption was in a chat on Discord, a social media platform. Mysteriously, Larva Labs has not officially adopted the NFT License in the Terms and Conditions for the CryptoPunks NFTs on its website. This Article dissects the cryptic case of the CryptoPunks licenses.
An Enhanced Re-Ranking Model for Person Re-Identification

Presently, Person Re-IDentification (PRe-ID) acts as a vital part of real time video surveillance to ensure the rising need for public safety.Resolving the PRe-ID problem includes the process of matching observations of persons among distinct camera views.Earlier models consider PRe-ID as a unique object retrieval issue and determine the retrieval results mainly based on the unidirectional matching among the probe and gallery images.But the accurate matching might not be present in the top-k ranking results owing to the appearance modifications caused by the difference in illumination, pose, viewpoint, and occlusion.For addressing these issues, a new Hyper-parameter Optimized Deep Learning (DL) approach with Expanded Neighborhood Distance Reranking (HPO-DLDN) model is proposed for PRe-ID.The proposed HPO-DLDN involves different processes for PRe-ID, such as feature extraction, similarity measurement, and feature reranking.The HPO-DLDN model uses a Adam optimizer with Densely Connected Convolutional Networks (DenseNet169) model as a feature extractor.Additionally, Euclidean distance-based similarity measurement is employed to determine the resemblance between the probe and gallery images.Finally, the HPO-DLDN model incorporated ENDR model to re-rank the outcome of the personreidentification along with Mahalanobis distance.An extensive experimental analysis is carried out on CUHK01 benchmark dataset and the obtained results verified the effective performance of the HPO-DLDN model in different aspects.
Road sign detection and recognition system based on multi-layers convolutional neural network model trained with German Traffic Sign Recognition Benchmark

Traffic sign detection and recognition play a vital role in the development of self-driving systems. It helps to increase the safety of drivers during the driving process. However, its accuracy will be sometimes affected by certain environmental factors and the accuracy needs to be improved. This paper proposes a detection and recognition system with a 8 layers convolutional neural network that acquires different kinds of features by training with different kinds of traffic signs. The training made an outstanding contribution to our detection and recognition system, allowing our system to be more comprehensive and precise compared with most current systems, which solved the problems of limited accuracy and performance under the effects of environmental factors.
Deep Learning based Face-Mask and Shield Detection

We all are facing an immense health calamity due to the speedy transmission of Covid-19. People are dying due to this deadly virus. Physical contact with an affected individual can transmit this disease. World Health Organization (WHO) issued many ways to avoid infection of Covid-19. In communal locations, wearing a face mask is one of the most effective strategies to protect oneself. For long periods of time, several countries have been in lockdown. As the world returns to normalcy, wearing a mask in public places might be crucial. Manually monitoring the mob would be challenging. So, we devised a way for determining whether or not a person in the crowd is wearing a mask. The proposed framework can detect masks and face shields with an accuracy of 95.7%.
Can Primary School Children Be Digital Learners? A Peruvian Case Study on Teaching with Digital Tool

Digital tools are means of communication that facilitate information so that students can innovate their own learning. Thus, the purpose of this research is to analyze the pedagogical use of digital tools in the teaching of mathematics in Peruvian elementary education. Through a process of convenience sampling, two elementary school teachers from a private school (Lima-Peru) were identified and the participants were interviewed using an interview guide. Regarding the beginning of the session, it was necessary to improve learning. Also, according to the development, they allow the acquisition of strategies. Finally, in the closing guarantee the evaluation of the use of digital tools in each learning process, they play a very important role, because they allow these learning to be innovative and challenging, with some virtual tools, students are more dynamic and interact through audiovisual media and didactic applications.
Intent-Aware Optimal Collision Avoidance and Trajectory Planning for a Pursuit Vehicle

Summary This paper presents an integrated optimal control framework for velocity and steering control of an autonomous pursuit vehicle, where the control objectives satisfy the requirements of collision avoidance and moving target tracking. A distinctive feature of the proposed velocity and steering control is the application of logarithmic penalty functions to both. The control barrier imposed by logarithmic function provides a unique tool in computing a balanced trajectory with optimal tracking error, control effort and safety margin. Trajectories compliant with the safety regulations for autonomous driving have been planned based on estimated intention of the target and the obstacles. Effects of the controller weights have been extensively simulated to assess the performance of the proposed strategy in a variety of dynamic situations. The controller has been validated on a real-life robot by using a shrinking horizon control policy for iterative optimisation.
MDCADNet: Multi dilated &amp; context aggregated dense network for non-textual components classification in digital documents

Non-Textual images like charts and tables are unlike natural images in various aspects, including high inter-class similarities, low intra-class similarities, substantial textual component proportions, and lower resolutions. This paper proposes a novel Multi-Dilated Context Aggregation based Dense Network (MDCADNet) addressing the multi-resolution and larger receptive field modeling need for the non-textual component classification task. MDCADNet includes a densely connected convolutional network for the feature map computation as front-end with a multi-dilated Backend Context Module (BCM). The proposed BCM generates multi-scale features and provides a systematic context aggregation of both low and high-level feature maps through its densely connected layers. Additionally, the controlled multi-dilation scheme offers a more extensive scale range for better prediction performance. A thorough quantitative evaluation has been performed on seven benchmark datasets for demonstrating the generalization capability of MDCADNet. Experimental results show MDCADNet performs consistently better than the state-of-the-art models across all datasets.
Expression Classification of Stroke Patients Based on Improved Edge Detection Mask Algorithm and Neural Network

Stroke is the most common disease of old ages causing serious damage to human life and health. Facial recognition classification is very important in early rehabilitation feedback of stroke patients during training. In expression recognition and classification, the fuzzy expression image and unclear features will lead to the poor quality of stroke patient expression recognition effect. This paper combines image edge detection technology with image mask, proposes an improved edge detection mask algorithm (IEDM). The gradient amplitude is detected by using the pixel domain information of 8 surrounding points and combining global and local feature detection with residual neural network to recognize and classify the facial expression of stroke patients. Through the improved algorithm, it can be concluded that the recognition accuracy of the algorithm used in this paper reaches 90.16%, this algorithm has good edge detection and classification ability.
Usability of the HTC Vive virtual reality system in recreational therapy

Commercial virtual reality (VR) systems have become increasingly popular, affordable, and accessible in recent years. Resultantly, recreational activities involving VR are providing new ways to engage in leisure experiences and innovative treatments in the health care arena. Little is known, however, regarding the usability of commercial VR systems for recreational therapists and their clients. Therefore, the purpose of this study was to evaluate the usability of a popular commercial VR system, the HTC Vive, in terms of its potential use in recreational therapy practice. Using a cross-sectional design, data were collected from recreational therapy professionals (N = 8) working in diverse service settings. Following exposure to the HTC Vive equipment through a professional workshop that included didactic instruction and clinical role-play activities, participants evaluated the system’s usability for themselves as well as for the clients they serve. Data collected through both an online survey and a focus group discussion revealed strong agreement from the therapists that the HTC Vive is a usable commercial VR system for themselves as well as their clients. Specific comments related to the head-mounted display (HMD), controllers, sensors/base stations, positioning, and Steam software provide valuable insights on features viewed positively, possible adaptations for specific client groups, and unique considerations by service setting.
How Do Environmental, Social, and Governance (ESG) Factors Impact on Public Finance Performance? Risk, Efficiency, and Public Financial System Perspectives

Environmental, social, and governance (ESG) risk is becoming increasingly important in conditions of ongoing environmental and social change. This has specific implications for risk management in the public sector. Many publications have been devoted to ESG risk in the commercial sector, especially for enterprises, while there is a literature gap in ESG risk analysis in the public sector. The aim of the paper is to identify ESG factors impacting on public finance based on the ESG criteria incorporated in the public decision-making process, and diagnose relationships between public finance, ESG, risk, and efficiency. The article uses fuzzy cognitive map to diagnose ESG factors and their link to public finance. A two-stage research procedure was used to achieve the research goal. In the first stage, the identification of ESG factors that are the most important from the view of public finance was carried out. In the next step, ESG factors that were incorporated by public institutions in the decision-making process were identified, and the interdependencies between nonfinancial factors and risk, efficiency, and public financial system were analyzed. Six independent experts were asked to evaluate a total of 62 factors determined based on a review of the literature. The strongest impact of environmental factors on risk in public finance was found comparing the social and governance one. In the scope of the environment, it can be seen that the most important is the carbon intensity emissions variable, which is clearly linked to other variables from this area. The results are important from the point of view of recommendations for government policy on environmental protection, especially in the context of actions aimed at reducing greenhouse gas emissions and combating climate change.
Improving Recognition of Handwritten Kannada Characters Using Mixup Regularization

Recognition of handwritten characters is essential for the digitization of old and degraded documents. The recognition of handwritten characters differs from its printed counterpart in terms of consistency in character size, spacing, thickness. This work aims to tackle the problem of recognizing handwritten characters for the Kannada language, an official language of the Karnataka state. We develop a novel dataset called Kannada84 considering a variety of factors such as age, profession, and native language of the authors, hence representing individuals from all walks of life. State-of-the-art Convolutional Neural Networks such as VGG Network, Squeeze-and-Excitation Network, and Residual Neural Network are employed to solve the task of handwritten character recognition. To enhance the performance of these networks, we performed mixup regularization leading to a reduction in generalization error. Quantitative results such as top-1 and top-3 test accuracy on Kannada84 are demonstrated in our study in which Residual Neural Network combined with mixup had the best top-1 test accuracy of 96.92% and Squeeze-and-Excitation Network combined with mixup had the best top-3 test accuracy of 99.57%.
Lymphocyte Count Prediction Using Machine Learning and UF-1000i Sysmex

The differential white blood cell (WBC) count can tell which types of white blood cells are most involved. This analysis can be performed by manual microscopy. This standard method seems to be limited by the increasing number of microbiological analyses. In parallel, various automated devices have been developed and marketed in recent years, but remain very expensive. Most of the existing studies are based on image classification. In this study, our objective is to predict the number of lymphocytes in one hundred cells of a body fluid from the parameters extracted from the flow cytometer and using machine learning methods. The data are recorded in the microbiology laboratory of the University Hospital Center (CHU) of Oujda. 130 body fluid samples are analyzed by the Sysmex UF-1000i analyzer and the database has been concatenated with the data resulting from the manual lymphocyte count. Four machine learning algorithms are used to predict lymphocyte count: Generalized Additive Model (GAM), Adaptive Multivariate Spline Regression (MARS), Artificial Neural Network (ANN) and Support Vector Regression (SVR). A comparison of flow cytometer parameters between two groups: the group of samples with high percentage of lymphocytes and the group of samples with low percentage of lymphocytes is performed. The MARS algorithm performed better than the other three algorithms by giving the lowest error value (RMSE).
Comparative Analysis of Classification and Detection of Breast Cancer from Histopathology Images Using Deep Neural Network

Breast cancer is one of the most widely recognized diseases among women across the globe. It is cancer that develops in breast cells. Histopathological and cytological images contain adequate phenotypic data, which defines their vital role in the analysis and cure of breast cancer. The emergence of deep neural networks with the rapid advancement in the Computational resources automatically inclines the accurate detection and classification of the Breast Histopathological Images. It assists the histopathologists to achieve accurate results through progressively fast, steady, objective, and measured examination. In this paper, a systematic survey has been conducted to introduce the improvement history and locate the future competence of Deep Learning calculations in the Breast Histopathological Image Analysis (BHIA) field. This study also involves comparative analysis of the most recent related works alluding to classical Artificial Neural Networks (ANNs) and Deep ANNs.
Reconnecting Teaching Discourse in Higher Education

The suddenness of the pandemic situation caused an immediate implementation of digital environments. Despite some negative surprise effects, this new challenging situation has prompted new creative impulses for further development, for productive re-structuring, adapted to new needs and developmental trends, and a quick adaptive reestablishment of the functionality in internal and external interface management. The practices of an effective reconnection of the discourse are not only of great importance at the institutional level, but also at the educational micro-level. This chapter, based on a discourse analysis of communicative contexts in digital environments, outlines some aspects of adaptive teaching practices, in order to support the macro-cohesive mechanisms of reconnecting the interrupted discourse as well as improving the dynamics and interaction of supported, collaborative and autonomous learning processes.
Deep Learning Approach To Predict Software Development Life Cycle Model

The Software Development Life Cycle (SDLC) is a well-defined, methodical procedure for developing software that prioritizes quality. It consists of a variety of models to choose from, which differentiate among themselves in multiple ways. As a project developer, one has to choose a particular model from these SDLC models according to the forthcoming software&#x2019;s requirements. This initiates a need for choosing the most appropriate model for the development of the software, so as to streamline the entire software building process, adhering to the model&#x2019;s defined guidelines and its timeline. This paper presents a deep learning-based approach that automates the SDLC model selection process by recommending the most appropriate SDLC model as per the users&#x2019; requirements.
AR-Poetry: Enhancing Children’s Motivation in Learning Classical Chinese Poetry via Interactive Augmented Reality

Classical Chinese poetry education is vital for children in cultivating their cultural and aesthetic competences, but it can be tedious and challenging without appropriate learning materials. Considering the great potential of augmented reality (AR) with its playful interaction and immersive 3D environment, in this paper, we focus on landscape poetry and present a tablet AR application, AR-Poetry, which aims to make it motivational and enjoyable for children to learn classical Chinese poetry. We utilized the ARCS model for instruction to enhance the motivational feature of AR-Poetry. Besides, we concluded with four intuitive yet reasonable interaction modes to maximize the connection between the contents and the interaction process, as well as to fulfill the features of AR. We believe that AR-Poetry could be a useful supplement to school education and hope that our work could attract more attention to cross-cultural discussion in the field of human-computer interaction.
A Novel Linguistic-Aware Memory Structure for Enhancing the Response Generation

People will think of conversation experiences during a chat. Inspired by the phenomena, some previous works exploit relevant conversation utterances to enhance the response generation process. However, these methods only focus on semantic information while ignoring linguistic information. As a result, relevant sentences are not fully utilized. To address the problem, we design a novel memory structure to store linguistic information. Specifically, we conduct a memory bank on the relevant utterances, consisting a series of key-value pairs. And the memory structure is combined during the generation process. We conduct experiments on two public conversation datasets, and the results demonstrate the effectiveness of our memory structure.
Title-Based Document Classification for Arabic Theses and Dissertations

Text classification has been widely used in the last few years because of social media's widespread use, mainly relying on texts. In this study, we propose a method to classify Arabic theses and dissertations based on their titles. The dataset used is collected from a well-known Iraqi repository using scrapping techniques. Multi-class-based classification is used since we have many classes that represent the document specialization. The titles are preprocessed utilizing several standard procedures such as tokenization, punctuation removal, and stop word removal. For feature selection, the TF-IDF method is used. Four different classifiers are applied to measure the performance accuracy (complemented Naïve Bayes, support vctor machine, K-nearest neighbors, and random forest). The study results showed that complemented Naïve Bayes classifier proposed the best performance with 0.88 of accuracy for the testing phase. The results of the study are promising to be applied with different short text classification.