Reviewerly/exHarmony huggingface dataset:
The Philosophy of Communitarianism in the Mission of a Sustainable University

Introduction. Modernity is characterized by the framework of the “neoliberal doctrine of world development”, and it defines the main vectors of the development of higher education. The “extinction” of the humanistic meaning of education, the understanding of the mission of the university solely as a contribution to public well-being through technological breakthroughs and business – all this forces us to return to the understanding of the mission of the university. The new study of the university's mission is determined by the influence of the philosophy of communitarianism on the university's activities on the track of sustainable development. Methodology and sources. The study of the problems with which the higher education system is faced, the study of the university's mission in the context of sustainable development of society and the influence of the philosophy of communitarianism and the “capability approach” are based on a systematic approach that allows us to consider the university as an integral, institutionally organized subsystem of society. The sociocultural approach allows us to consider the university corporation as a collective subject of educational, scientific and other activities focused on the values of public and personal welfare. Results and discussion. The concepts of communitarianism try to resist the dominance of neoliberalism. The main idea of these concepts is the moral improvement of society through the revival of traditional values and the education of the individual (A. McIntyre). The philosophy of “capability approach” (A. Sen, M. Nassbaum) has similar ideas on the development of moral qualities of the individual, the denial of the “egocentric essence of person”, the assertion of the priority of the general over the particular. Full-fledged human functioning depends on a set of abilities and opportunities, for example, the ability to get an education, the opportunity to participate in public and political life, to realize their own development trajectory, etc. The greater the set of these opportunities in each person's life, the higher its quality is evaluated. The philosophy of “opportunities” and the philosophy of communitarianism are interfaced with the concept of sustainable development of society. Models of “sustainable universities” that implement practices of interaction with society try to put into practice communitarian ideas, creating a balance of interests of the individual and society, public good and personal freedom. Conclusion. Combining the ideas of communitarianism with the idea of sustainable development could give a new impetus to the development of the university and change the content of its mission, and strengthen its humanitarian orientation.
Supervised Learning

Supervised learning involves training using well “labelled” training data, and on the basis of the training data, machines are able to predict the output. The labelled data means that the correct output is attached along with the corresponding input data. In supervised learning, as the name suggests, the training data acts as the supervisor and provides training to the machine to predict the correct output. This chapter discusses Statistical Decision Theory, Gaussian &amp;amp; Normal Distribution, Conditionally Independent Binary Components, Learning Beliefs Network and Nearest-Neighbour Methods.&lt;br&gt;
Theory of Generalisation

Unsupervised learning is a kind of machine learning algorithm that can be used to draw useful conclusions without the presence of labeled responses in the input data. In the previous chapter, we discussed clustering (k-means clustering, hierarchical clustering) and Principal Component Analysis. In this chapter, we will discuss training versus testing, bounding the testing error, and the VC dimension.&lt;br&gt;
Neural Network

A Support Vector Machine is used for the purpose of classification and regression. In the previous chapter, we discussed Support Vector Machine, margin and large margin methods, and kernel methods. A neural network refers to a parallel computing device that attempts to mimic the model of the brain. In the following chapter, we will discuss the early models of Neural Networks as well as the Perceptron learning model, back propagation, and Stochastic Gradient Descent.&lt;br&gt;
Decision Trees

Neural networks are a way to mimic the working of a human brain. Decision trees refer to the decision support structure that uses a tree to make decisions and draw all possible consequences. Decision trees are a way to display conditional control statements. In the following chapter, we will discuss decision trees, regression trees, stopping criterion and pruning loss functions in a decision tree, categorical attributes, multiway splits and missing values in decision trees, and instability in decision trees.&amp;nbsp;&lt;br&gt;
Unsupervised Learning

Unsupervised learning is a complex processing task involving the identification of patterns in datasets having data points that are neither labeled nor classified. Unsupervised learning is a kind of machine learning algorithm that can be used to draw useful conclusions without the presence of labeled responses in the input data. In the following chapter, we will discuss Clustering (K-means Clustering, Hierarchical Clustering), and Principal Component Analysis.&lt;br&gt;
Introduction to Python

Python is considered one of the most simple and efficient programming languages. Its object-oriented programming approach and elegant syntax make it a powerful programming language. Python is an interpreted language. Its dynamic typing and high level data structures make it an ideal language for application development in various areas and on multiple platforms. Today, Python is widely used in the areas of machine learning and data science. The following chapter discusses Python, the utility of Python in machine learning and data science, ecosystem of Python in machine learning and various libraries in Python required for machine learning.&lt;br&gt;
Improve Session-Based Recommendation with Triplet Mining and Dynamic Perturbations Graph Neural Networks

Session-based recommendation (SBR) emphasizes mining user interests to predict the next click based on recent interactions within sessions. Most current SBR methods suffer from insufficient interactive information problems and fail to distinguish session representations with high similarities, which can neglect the inherent features within sessions. To fill the gap, we propose a triplet mining enhanced graph neural networks (TME-GNN) approach to enhance the recommendation systems by mining structural and inherent information. Technically, we first generate anchor, positive and negative embeddings based on the given session and set a triplet mining task to improve the recommendation task with subtle features by pushing positive pairs close and pulling negative pairs away. Second, to robust the model, we employ a self-supervised auxiliary task by adding dynamic perturbations to the embedding space. We conduct extensive experiments to demonstrate the superiority of our method against other state-of-the-art algorithms. Our implementations are available on the following site https://github.com/Info4Rec/TME-GNN .
Support Vector Machine

Support Vector Machine (SVM) may be defined as a machine learning algorithm that can be used for regression and classification. It is generally used for classification purposes. In this chapter, we will discuss Margin and Large Margin Methods and Kernel Methods.&amp;nbsp;&lt;br&gt;
Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

Abstract Deep neural networks (DNN) based automatic modulation classification (AMC) has achieved high accuracy performance. However, DNNs are data‐hungry models, and training such a model requires a large volume of data. Insufficient training data will cause DNN models to experience overfitting and severe performance degradation. In practical AMC tasks, training the deep model with sufficient data is challenging due to the costly data collection. To this end, a novel probability density function (PDF) based data augmentation scheme and a method to determine the required minimum sampling size for data enlargement is proposed. Compared with the known image‐based augmentation scheme, the proposed waveform‐based PDF technique has low complexity and is easy to implement. Experimental results show that the required size of the training dataset is one order of magnitude smaller than the sufficient dataset in the additive white Gaussian noise channel, and effective recognition can be achieved using around 60% of the total examples under the Rayleigh channel. Moreover, the presented scheme can expand training data under frequency and phase offsets.
Recent trend analysis of convolutional neural network-based breast cancer diagnosis

One of the most common malignancies worldwide is breast cancer. Early screening and diagnosis are important to the reduction of mortality rates of patients. In order to improve the performance and accuracy of breast cancer image screening, researchers have made significant progress in Computer-aided diagnosis (CAD) systems built on convolutional neural networks (CNN). In this research, several recent CNN models of breast cancer diagnosis are discussed and explained, and multiple public datasets of breast cancer images are introduced. The detailed performances of the models are presented and compared. The limitations and potential improvements of current CNN-based CAD are discussed. Convolution neural network-based CAD are still facing challenges of shortage of public dataset and the problem of implementation in the clinical scenario. Conclusively, using a convolutional neural network to diagnose breast cancer is still at its early stage, and further developments are required to apply convolutional neural network-based cancer diagnosis to clinical practices.
Colorectal cancer classification based on histology images: comparison between DNN and CNN

According to statistics from the World Health Organization, Colorectal Cancer (CRC) is the third most commonly diagnosed cancer in the world. The detection of CRC in an early stage is crucial for on-time and proper treatment, which may significantly increase the patient's survival rate. Although computers are not qualified to replace human experts at the moment, having a referential result from CRC auto-detection and saving the time of manual diagnosis is still very meaningful. This paper compares the performances of two different neural networks classifying CRC based on a set of histology images. The labeled dataset is publicly available on the Tensorflow website, and the two neural networks are tested on the same dataset separately. The first type of neural network in this study is Convolutional Neural Network (CNN), and the second type is a Deep Neural Network (DNN). As the dataset splits into training, testing, and validation sets, the loss, accuracy, and training time are recorded by the end of each epoch. The study result shows that the CNN method is better than the DNN method in terms of CRC image classification. It takes a long time but has better performance.
A parallel processing method for long-range contextual semantic information to sentiment analysis based on aspect

Aspect-based sentiment analysis is crucial for Internet applications such as social networks and e-commerce, where the previous deep learning methods cannot process long-range semantic information in parallel. This paper proposes an aspectbased sentiment analysis method based on multiscale convolution and a double-layer attention mechanism. The technique uses pre-trained BERT to obtain the hidden semantic information of the context from the training set, then uses multiscale deep convolution and double-layer attention to process the long-distance semantic information between the target word and the context in parallel, and finally uses softmax for sentiment classification of the target word. In this paper, we use the public dataset of SemEval 2014 and the Twitter Dataset to validated the improved accuracy and F1 of the model.
Breast cancer prediction using machine learning models

This paper is to predict the presence of recurrence for breast cancer patients by citing data. As a first step we will collect relevant data on breast cancer patients from the internet. Next, we will use decision trees in Scikit-learn to determine if there will be a recurrence of breast cancer in patients who have been cured. Through a series of calculations and predictions, the accuracy of our experimental model finally reaches 0.75 accuracy. These data can help us to accomplish our target prediction well.
Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED

Automatic target recognition technology is an important research direction in the field of machine vision. Artificial ground targets, such as bridges, airports and houses, are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In view of the shortcomings of traditional edge detection methods, such as background suppression, non-prominent targets, missing positions, etc., this paper proposed an image edge detection method based on depth learning. By combining the traditional edge detection algorithm with the edge detection algorithm based on an improved HED network, the real-time target image edge detection was completed. An automatic target recognition method based on template matching was proposed. This method solved the problem of both homologous template matching and heterogeneous template matching, which has important theoretical value. First, the lines were combined to form the geometric primitives of the line group, and then the relationship of the lines in the group was determined by using the characteristic quantity of the line group. The best line group matching the target template was found in the image edge, and the homonymous points in the real-time image and the target template were calculated. The affine transformation matrix between the two images was obtained according to the homonymous points, and then the accurate position of the target in the real-time image was found.
ConTra: (Con)text (Tra)nsformer for Cross-Modal Video Retrieval

In this paper, we re-examine the task of cross-modal clip-sentence retrieval, where the clip is part of a longer untrimmed video. When the clip is short or visually ambiguous, knowledge of its local temporal context (i.e. surrounding video segments) can be used to improve the retrieval performance. We propose Context Transformer (ConTra); an encoder architecture that models the interaction between a video clip and its local temporal context in order to enhance its embedded representations. Importantly, we supervise the context transformer using contrastive losses in the cross-modal embedding space. We explore context transformers for video and text modalities. Results consistently demonstrate improved performance on three datasets: YouCook2, EPIC-KITCHENS and a clip-sentence version of ActivityNet Captions. Exhaustive ablation studies and context analysis show the efficacy of the proposed method.
A New Loss Function for Simultaneous Object Localization and Classification

Robots play a pivotal role in the manufacturing industry. This has led to the development of computer vision. Since AlexNet won ILSVRC, convolutional neural networks (CNNs) have achieved state-of-the-art status in this area. In this work, a novel method is proposed to simultaneously detect and predict the localization of objects using a custom loop method and a CNN, performing two of the most important tasks in computer vision with a single method. Two different loss functions are proposed to evaluate the method and compare the results. The obtained results show that the network is able to perform both tasks accurately, classifying images correctly and locating objects precisely. Regarding the loss functions, when the target classification values are computed, the network performs better in the localization task. Following this work, improvements are expected to be made in the localization task of networks by refining the training processes of the networks and loss functions.
Token-Revocation Access Control to Cloud-Hosted Energy Optimization Utility for Environmental Sustainability

To increase the usage of renewable energy, it is vital to maximize local energy production by properly combining various renewable-energy sources by collecting their data and storing it on the cloud. The energy optimization utility, which is used for making decisions to optimize renewable-energy resources, is hosted on the cloud to benefit from cloud capabilities in data storage. Hosting such sensitive data and utilities on the cloud has created some cybersecurity challenges. This paper presents a new token-revocation access control (TR-AC) which revokes the authorization of malicious users before authorizing them to access cloud-hosted energy optimization utilities. TR-AC employs a set of multi-authorities to measure the authentic level for each authenticated user. Although the user is authenticated to access the online system, this authentication can be revoked to utilize the energy optimization utility based on the user’s level of authentication. The cloud storage servers are not fully trusted and, therefore, have no control over access controls. Finally, the proposed TR-AC has been proven to be secure against any attacker that is not authentic according to Diffie-Hellman assumptions. In addition, performance analysis has proven that the time elapsed for both encryption and decryption in TR-AC is very small compared with previously introduced schemes. Therefore, it will not affect the performance of the cloud-hosted system.
Vision Transformer Compression and Architecture Exploration with Efficient Embedding Space Search

This paper addresses theoretical and practical problems in the compression of vision transformers for resource-constrained environments. We found that deep feature collapse and gradient collapse can occur during the search process for the vision transformer compression. Deep feature collapse diminishes feature diversity rapidly as the layer depth deepens, and gradient collapse causes gradient explosion in training. Against these issues, we propose a novel framework, called VTCA, for accomplishing vision transformer compression and architecture exploration jointly with embedding space search using Bayesian optimization. In this framework, we formulate block-wise removal, shrinkage, cross-block skip augmentation to prevent deep feature collapse, and Res-Post layer normalization to prevent gradient collapse under a knowledge distillation loss. In the search phase, we adopt a training speed estimation for a large-scale dataset and propose a novel elastic reward function that can represent a generalized manifold of rewards. Experiments were conducted with DeiT-Tiny/Small/Base backbones on the ImageNet, and our approach achieved competitive accuracy to recent patch reduction and pruning methods. The code is available at https://github.com/kdaeho27/VTCA .
A Personalized Multi-Turn Generation-Based Chatbot with Various-Persona-Distribution Data

Existing persona-based dialogue generation models focus on the semantic consistency between personas and responses. However, various influential factors can cause persona inconsistency, such as the speaking style in the context. Existing models perform inflexibly in speaking styles on various-persona-distribution datasets, resulting in persona style inconsistency. In this work, we propose a dialogue generation model with persona selection classifier to solve the complex inconsistency problem. The model generates responses in two steps: original response generation and rewriting responses. For training, we employ two auxiliary tasks: (1) a persona selection task to fuse the adapted persona into the original responses; (2) consistency inference to remove inconsistent persona information in the final responses. In our model, the adapted personas are predicted by an NLI-based classifier. We evaluate our model on the persona dialogue dataset with different persona distributions, i.e., the persona-dense PersonaChat dataset and the persona-spare PersonalDialog dataset. The experimental results show that our model outperforms strong models in response quality, persona consistency, and persona distribution consistency.
Fine-Grained Image Style Transfer with Visual Transformers

With the development of the convolutional neural network, image style transfer has drawn increasing attention. However, most existing approaches adopt a global feature transformation to transfer style patterns into content images (e.g., AdaIN and WCT). Such a design usually destroys the spatial information of the input images and fails to transfer fine-grained style patterns into style transfer results. To solve this problem, we propose a novel STyle TRansformer (STTR) network which breaks both content and style images into visual tokens to achieve a fine-grained style transformation. Specifically, two attention mechanisms are adopted in our STTR. We first propose to use self-attention to encode content and style tokens such that similar tokens can be grouped and learned together. We then adopt cross-attention between content and style tokens that encourages fine-grained style transformations. To compare STTR with existing approaches, we conduct user studies on Amazon Mechanical Turk (AMT), which are carried out with 50 human subjects with 1,000 votes in total. Extensive evaluations demonstrate the effectiveness and efficiency of the proposed STTR in generating visually pleasing style transfer results (Code is available at https://github.com/researchmm/STTR ).
NoiseTransfer: Image Noise Generation with Contrastive Embeddings

Deep image denoising networks have achieved impressive success with the help of a considerably large number of synthetic train datasets. However, real-world denoising is a still challenging problem due to the dissimilarity between distributions of real and synthetic noisy datasets. Although several real-world noisy datasets have been presented, the number of train datasets (i.e., pairs of clean and real noisy images) is limited, and acquiring more real noise datasets is laborious and expensive. To mitigate this problem, numerous attempts to simulate real noise models using generative models have been studied. Nevertheless, previous works had to train multiple networks to handle multiple different noise distributions. By contrast, we propose a new generative model that can synthesize noisy images with multiple different noise distributions. Specifically, we adopt recent contrastive learning to learn distinguishable latent features of the noise. Moreover, our model can generate new noisy images by transferring the noise characteristics solely from a single reference noisy image. We demonstrate the accuracy and the effectiveness of our noise model for both known and unknown noise removal.