Reviewerly/exHarmony huggingface dataset:
Advances in Text Classification Based on Machine Learning

This paper will introduce the machine learning system based on text classification technology in detail. Through professional research and investigation, we can find out three advanced studies of text classification technology in the current era, such as spatial dimension reduction, classification methods and evaluation methods. Through various exploration and discovery, we can improve the learning experience and practice of relevant personnel.
Distortion Removal and Deblurring of Single-Shot DWI MRI Scans

Diffusion Weighted Imaging (DWI) is one of the standard MRI images that are used for the diagnosis of brain tumors. However, the acquired DW images suffer from artifacts such as EPI (echo-planar imaging) distortion. These distortions are corrected using blip-up and blip-down images that are separately acquired for distortion removal. In addition, DWI MRI images have shorter acquisition times, but suffer from poor resolution. Multi-shot diffusion weighted imaging allows the acquisition of higher resolution images but require longer acquisition times and necessitate the use of new and expensive hardware. In this paper, we perform distortion removal of EPI-DWI images from blip-up images using a previously proposed framework and design a suitable deblurring technique for generating higher resolution DWI images from low-resolution undistorted EPI-DWI images. Our technique aims to allow the use of deblurred EPI-DWI images for performing accurate medical diagnosis and multi parametric longitudinal analysis in brain tumors. We use data augmentation, dilated convolution, and ELU (exponential linear unit) to design a suitable architecture that achieves superior performance in terms of accuracy.
Application of image processing technology in cable detection of underwater robot

Manual inspection of underwater cables has many problems, such as heavy labor intensity, high operation difficulty and low work efficiency. It is more effective to use underwater camera to capture images of underwater cables than manual inspection. The addition of subsea detection and construction equipment raises bottom sediment in the water and causes the water to become turbid and cloudy, it also reduces the contrast of the underwater image and adds irrelevant background information. Therefore, a method of cable location in turbid waters based on image processing is proposed. The method firstly applies the dark channel prior dehazing algorithm. An adaptive histogram equalization algorithm is used to improve the light distribution of underwater image after defogging. Then, we use the local adaptive dynamic threshold segmentation method to achieve the rough segmentation, and the fine segmentation of cable image is carried out by selecting each connected domain region. Finally, the cable pixel coordinates are obtained by the method of center line extraction and frequency domain transformation. Experimental result shows that the proposed algorithm has good effects in terms of clarity.
Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals

Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these principles provide valuable benchmarks to assess the quality of health resources developed by local health authorities. In this paper, we developed machine learning classifiers for health professionals with or without Chinese proficiency to assess public-oriented health information in Chinese based on the definition of effective health communication by the WHO. We compared our optimized classifier (SVM_F5) with the state-of-art Chinese readability classifier (Chinese Readability Index Explorer CRIE 3.0), and classifiers adapted from established English readability formula, Gunning Fog Index, Automated Readability Index. Our optimized classifier achieved statistically significant higher area under the receiver operator curve (AUC of ROC), accuracy, sensitivity, and specificity than those of SVM using CRIE 3.0 features and SVM using linguistic features of Gunning Fog Index and Automated Readability Index (ARI). The statistically improved performance of our optimized classifier compared to that of SVM classifiers adapted from popular readability formula suggests that evaluation of health communication effectiveness as defined by the principles of the WHO is more complex than information readability assessment. Our SVM classifier validated on health information covering diverse topics (environmental health, infectious diseases, pregnancy, maternity care, non-communicable diseases, tobacco control) can aid effectively in the automatic assessment of original, translated Chinese public health information of whether they satisfy or not the current international standard of effective health communication as set by the WHO.
A Study on Cloud Methodology

Cloud computing has an exploding growth in demand of resources. Resource provisioning is one of the most challenging problems faced in cloud computing due to resource dynamic nature of application. This resource can lead to two major problems i.e. under provisioning and over provisioning. How to implement resource provisioning is major concern in cloud computing. This paper systematically analyses the realise of the objectives, performance on various QoS attributes and issues faced in could computing environment.
Long Sentence Segmentation Model based on Machine Translation

To solve the problems of inaccurate results and high recovery rate of traditional translation algorithms, this paper proposes a long sentence segmentation model based on machine translation. The method consists of a segmentation model and a reordering model. Firstly, the regularization matching algorithm is applied to the segmentation of long sentences, and the number of words in the sentences can be reduced through the combination of sentence components. Then, the segmentation model is trained with the word alignment information generated by the traditional statistical machine translation model, and a large number of linguistic features are used to make rules to identify and correct segmentation errors. Finally, we test the performance of our method on special corpus. The experimental results show that, compared with the traditional translation algorithm, the accuracy rate of the proposed algorithm is 5.72% higher, and the average recovery rate is 7.19% lower, which effectively solves the problems of stiff translation and poor readability.
Do We Need the Users’ Voice? An Empirical Research Example Comparing Views of Service Providers and Ex-Prisoners: Implications for Practice

Abstract User involvement in service development is seen as important to the credibility of these interventions but involving prisoners or ex-prisoners in this process can be problematic because of the vulnerability of this group as well as security issues. Questions arise as whether front line workers can instead reflect the perspectives of their clients accurately during service development events. Further, we query whether an alignment of perspectives is important for effective professional-prisoner relationships and offender self-efficacy when engaging in rehabilitation and reintegration programmes. To explore these questions, this chapter, in a case study third sector mentorship organisation, compares and contrasts the views of ex-prisoners and their mentors. Q methodology is employed to make this comparison. We find that mentors perspectives are most in tune with the most pessimistic perspectives of their clients: the most lonely, indigent and ill group of the exoffenders they work with. They do not share the optimistic views that characterise other groups of offenders in receipt of their service. The chapter explores the implications of these different views for exoffenders, their mentors and the participation of the offender in service innovation.
A Brief Revolution of Evolution and Resurgence on Machine Learning

The most prominent branch of Artificial intelligence is Machine learning, experiencing exponential growth in almost all fields over the decades. It's been a very challenging journey for computer scientists and researchers from many decades to sustain and achieve stability so far in this particular field. This paper intends to present an overview of machine learning, from its origin, its incessant evolution through these decades. This review helps the researchers and data scientists to gain insight and foster the scholarly discussions of the achievements of AI and ML, it also provides a proper perspective on obtained results for all the researches intended to explore machine learning.
Benemérita Universidad Autónoma de Puebla (BUAP). A Transversal Model to Support Educational Continuity Fostering Resilience, Innovation, and Entrepreneurship

Abstract Throughout its history, Benemérita Universidad Autónoma de Puebla (BUAP), a public institution, has always been a guide of social and economic development of the state of Puebla. From one moment to another, the state was unexpectedly immersed in the Covid-19 pandemic that caused the sudden migration from a mostly face-to-face instructional model to an online one. Immediately, the institution summoned different staff members to define and analyze the situation and then establish actions to guarantee educational continuity. As part of this analysis, they identified opportunities specifically at the upper secondary level, which includes high school. In particular, one opportunity was to offer entrepreneurship courses in an online format, capitalizing on the experience gained from the undergraduate program. Given the characteristics of the pandemic, the analysis also highlighted that the crisis would have severe economic effects on the students and their families, diminishing their income. It is estimated that the entrepreneurship education program reached 18,000 people, including students, faculty members, and parents. Over 10,000 more people will be reached throughout the rest of the year. Therefore, it is essential to strengthen the resilience, innovation, and entrepreneurial capacity of the university ecosystem.
UNI-Event: Student’s Events and Activities Mobile Application

An event can be defined as a thing that happens or taken place at a specific location during certain period of time which can be a planned public or social occasion, or it can be competition that can be broken down into several particular contests, based on the Oxford dictionary. People around the world used events as a concept to gather in order for them to accomplish their desired goal together. University is one of the places that have a high number of events conducted in a definite period of time, specifically during the on semester, where there are lots of individuals, clubs and organizations who handles events with their goals in mind. As for the perspective of the students in the university, those events should be considered to be their co-curriculum activities as it provides the students a different content and experience of learning when compared to standard lecture session. Now days, there are less variation of students who involved in these co-curricular activities as the same group of people have the higher tendency to get involved in the events. This paper aims to conduct an analysis regarding the involvement of students in co-curricular activities in university through the existing system of joining the events. By analyzing the existing system of involving into events will support the purpose of this project in which developing a new system that able to solve the existing problems in order to make the process became more efficient and interactive. Co-curricular activities are very important for the students to get involve in because there are lots of benefits that will come alongside the process, such as improvement in soft skill. Hence, the number of students involve in the events and activities should be increasing through the implementation of the new system. The existing system analyzed is specifically in University Technology PETRONAS and may differ with the other university where the culture and approach are different. The main concept explained in this paper about differentiation of the existing system and new system should be able to apply to all of the students across the board as the more efficient way of executing things will always be moredelighting.
Solving Complex Classification Problems using Multiobjective Evolutionary Optimization

Classification is one of the fundamental tasks performed in medical and healthcare applications. Previous studies in this field have proposed classification methods based on fuzzy rules, fuzzy logic or neural networks. This work applies a genetic algorithm to the classification of medical data, with a special emphasis on the classification of breast cancer cases. A key characteristic of the algorithm lies in the multi-dimensional values of its original objective function. This allows our method to enable simultaneous consideration of many optimization criteria. The choice of these criteria is based on statistical procedures such as correlation analysis and confidence intervals. The aim of this work was to examine, how the choice of the optimization criteria affected the results of the classification. Such an approach can demonstrate encouraging results in overall classification performance, which makes it a promising solution for automated medical data analysis.
Design Approach to Platform Agnostic Service API Modeling for Interoperability of Cross-enterprise Vehicle Applications

In the last few years, the collaboration of services between the service-oriented, cross-enterprise vehicle application frameworks has gradually increased to generate novel and more complicated vehicle services for the automotive industry. In these service collaboration scenarios, where heterogeneous application frameworks participate to realize complex vehicle services, a source of discord that emerges is that the service providers must always check, before the service deployment, whether the clients or service consumers on the other side of the communication link are compatible with a given service's API (Application Program Interface). While using standardized templates like ontologies for API's semantic specifications are crucial for a service discovery and semantic interoperability, nevertheless, accessing these service APIs' semantic data using a standardized and understandable syntactical specification template is also equally substantial to ease services interoperability. In fact, such complex service collaboration scenarios motivate this research work which proposes a design approach towards standardized, domain-specific, platform-agnostic semantic and syntactic specification of vehicle services API models. This paper also uses a typical vehicle domain case study to illustrate the design approach and a reference mapping between the platform-agnostic semantics specifications of a vehicle service API ontological model and its corresponding language-neutral, syntactic representation using the OpenAPI standard.
Detection of small objects in complex long-distance scenes based on Yolov3

In view of the difficulty in detecting and managing illegal buildings, this article uses high-definition cameras mounted on a tower to regularly capture images to construct the “Suspected Illegal Building Information” dataset. Due to the high distance of the tower from the ground, most of the collected data images contian many small targets, and there are many types of target objects to be detected and target size is quite differences, resulting in low detection accuracy. Based on the above problems, this paper makes the following improvements based on the Yolov3 algorithm: (1) using K-menas re-clustering anchor boxes, (2) introducing CIoU optimization loss function, (3) adding 104×104 scale for feature extraction, (4) using Soft-NMS instead traditional NMS algorithm, in addition, the data set is enhanced and optimized. The experimental results show that, compared with the original Yolov3, the improved algorithm has improved the detection accuracy of this experimental data set by 19.1%, which can well meet the needs of the project.
Use of machine learning in bone cancers

Machine learning models that assist physicians in the decision-making process have been developed and implemented in the last few years. Generally, such models are used to classify digital data into clinically relevant subclasses, such as diagnostic tumor types (osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, etc.) or histological tissue subclass (viable, necrotic, or healthy tissue) from digital histology data. Practically a model, which encodes a complex mathematical function, is developed by learning a set of features from training datasets of digital images (e.g., radiology and/or histology). It then uses the learned features to infer information from previously unseen datasets. Experience in osteosarcoma to date emphasizes the use of two major model types, based on the underlying model architecture. The first class contains traditional machine learning models, implementing supervised learning to extract a set of well-defined features from the input data to produce the output classification. The second class is based on emerging deep learning architectures, such as convolutional neural networks, that implement a supervised learning approach to an annotated training dataset and uses learned features to classify future input data. However, a key particularity of deep learning models is that the features learned are abstract to the user, numerical encodings that cannot be directly associated to human expert defined features of specific classes. We review recent progress on developing computer-based classifiers for bone cancer that include both traditional machine learning models and convolutional neural network architectures.
Virtual learning environments for digital electronics using augmented reality

The 2020 pandemic represents another challenge for universities concerned with guaranteeing Educational Quality and the training of their graduates, which must be in accordance with the demands of the current global labor market. Distance Education, one year after becoming the global resource, has become monotonous and routine for many students. At the same time, augmented reality has become more accessible in many ways and represents a viable option that complements learning environments in a motivating way. Its application in education has been reported with success in various disciplinary areas and its advantages are listed in various investigations. In this work, a methodology for the development of virtual learning environments through augmented reality is reported, which can be developed and applied in any subject of higher education, with which it is presumed that AR can be a tool of general application for the creation of innovative learning objects and environments that improve learning outcomes.
Research on Named Entity Recognition Method Based on Improved LSTM-CRF Model

Abstract Because the computer cannot directly understand the text corpus in the NLP task, the first thing to do is to represent the characteristics of the natural language numerically, and the word vector technology provides a good way to express it. Because Word2vec considers context and has fewer dimensions, it is now more popular words embedded. However, due to the particularity of Chinese, word2vec cannot accurately identify the polysemy of words. In this paper, a lightweight and effective method is used to merge vocabulary into character representation. This approach avoids designing complex sequence modeling architectures. for any neural network model, simply fine-tuning the character input layer can introduce vocabulary information. The model also uses the modified LSTM to bridge the enormous LSTM and the Transformer model. The interaction between input and context provides a richer modeling space that significantly improves testing on all four public datasets.
Removal of Gaussian White Noises from the image by probability map prediction based Deep learning approach

Deep learning methods have gained an increasing research interest, especially in the field of image denoising. Although there are significant differences between the different types of deep learning techniques used for natural image denoising, it includes significant process and procedure differences between them. To be specific, discriminative learning based on deep learning convolutional neural network (CNN) may effectively solve the problem of Gaussian noise. Deep learning based optimization models are useful in predicting the true noise level. However, no relevant research has attempted to summarize the different deep learning approaches for performing image denoising in one location. It has been suggested to build the proposed framework in parallel with the previously trained CNN to enhance the training speed and accuracy in denoising the Gaussian White Noise (GWN). In the proposed architecture, ground truth maps are created by combining the additional patches of input with original pictures to create ground truth maps. Furthermore, by changing kernel weights for forecasting probability maps, the loss function may be reduced to its smallest value. Besides, it is efficient in terms of processing time with less sparsity while enlarging the objects present in the images. As well as in conventional methods, various performance measures such as PSNR, MSE, and SSIM are computed and compared with one another.
Discrete Auto-regressive Variational Attention Models for Text Modeling

Variational autoencoders (VAEs) have been widely applied for text modeling. In practice, however, they are troubled by two challenges: information underrepresentation and posterior collapse. The former arises as only the last hidden state of LSTM encoder is transformed into the latent space, which is generally insufficient to summarize the data. The latter is a longstanding problem during the training of VAEs as the optimization is trapped to a disastrous local optimum. In this paper, we propose Discrete Auto-regressive Variational Attention Model (DAVAM) to address the challenges. Specifically, we introduce an auto-regressive variational attention approach to enrich the latent space by effectively capturing the semantic dependency from the input. We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse. Extensive experiments on language modeling tasks demonstrate the superiority of DAVAM against several VAE counterparts. Code will be released.
A Demonstrator for Interactive Image Clustering and Fine-Tuning Neural Networks in Virtual Reality

We present a virtual reality (VR) application that enables us to interactively explore and manipulate image clusters based on layer activations of convolutional neural networks (CNNs). We apply dimensionality reduction techniques to project images into the 3D space, where the user can directly interact with the model. The user can change the position of an image by using natural hand gestures. This manipulation triggers additional training steps of the network, based on the new spatial information and new label of the image. After the training step is finished, the visualization is updated according to the new output of the CNN. The goal is to visualize and improve the cluster output of the model, and at the same time, to improve the understanding of the model. We discuss two different approaches for calculating the VR projection, a combined PCA/t-SNE dimensionality reduction based approach and a variational auto-encoder (VAE) based approach.