Reviewerly/exHarmony huggingface dataset:
An Adaptive Synthetic Sample Coupled With Ensemble Multi-Filter Approaches For The High Dimensional Imbalanced Dataset

DNA microarray dataset infamously comprises a high dimensional and class imbalanced dataset, these problems pose severe challenges for the accurate identification of biomarker genes for cancer diagnosis and classification. The training learning model on the class imbalance dataset is a challenging task and became more tedious with high dimensional features. However, a small number of techniques have been proposed to address these issues simultaneously due to their complicated intersection. This paper proposes a hybrid approach of an adaptive synthetic sampling coupled with ensemble multi-filter for the high dimensional imbalanced datasets (ADASYN-EMF) method to overcome the problems of the high dimensional imbalanced datasets. An adaptive synthetic sampling approach for the imbalanced learning (ADASYN) method is used to generate the synthetic examples for positive class, while the ensemble multi-filter combined with Correlation-Based Redundancy is utilized to select the most robust gene subsets. Experiments on five DNA microarray datasets reveal that the proposed approach outperforms the state art methods. Thus, ADASYN-EMF is considered a robust technique that can handle the problem of high-dimensional imbalanced datasets.
Leveraging Autoencoder and Focal Loss for Imbalanced Data Classification

Class-imbalance is common occurrence in the machine learning and data mining community. Many classification algorithms often fail to learn the minority class information when encountering imbalanced data, resulting in the model being biased towards the majority class. Most importantly, existing learning algorithms mainly concern the class-imbalance distribution, and do not take into account the small number of samples, high feature dimensions, and other issues. To this end, this paper presents a new learning paradigm for solving above problems. To be specific, we first utilize Autoencoder to achieve dimensionality reduction and extract most discriminative features. Next, we explore focal loss to solve the class-imbalance issue and obtain better classification capability. Finally, we tackle the black-box optimization problem via Bayesian Optimization and find a suitable parameters for classification. Extensive experiments performed on three real-world datasets show our method achieves better performance.
Hierarchical Streaming Feature Selection Based on FDAF-score

In many practical classification scenarios, the data label space has a hierarchical structure, these data often have high-dimensional features, and there are irrelevant and redundant features. Thus, a variety of hierarchical classification feature selection methods are proposed. However, most of the existing algorithms ignore the temporal uncertainty and unknown of the feature space, and can only deal with static feature space data. Therefore, a new hierarchical streaming feature selection method is proposed. First, the algorithm uses the sibling strategy and the exclusion strategy in the hierarchical relationship to consider the relationship between homogeneous and heterogeneous samples. Then, the FDAF-score algorithm is integrated into the hierarchical classification streaming feature selection and used as a criterion for evaluating feature importance. Second, the improved Gaussian kernel function is used to judge the degree of redundancy between features, and the redundant features are removed. Finally, experiments on six hierarchical classification data sets show the effectiveness of the proposed algorithm.
Image Caption Generator using Deep Learning

Over the last few years deep neural network made image captioning conceivable. Image caption generator provides an appropriate title for an applied input image based on the dataset. The present work proposes a model based on deep learning and utilizes it to generate caption for the input image. The model takes an image as input and frame the sentence related to the given input image by using some algorithms like CNN and LSTM. This CNN model is used to identify the objects that are present in the image and Long Short-Term Memory (LSTM) model will not only generate the sentence but summarize the text and generate the caption that is suitable for the project. So, the proposed model mainly focuses on identify the objects and generating the most appropriate title for the input images.
Fraud Account Detection on Social Network using Machine Learning Techniques

Nowadays, a person's impact is frequently determined by the number of followers he or she has on social media. To this aim, the prevalence of false accounts is one of the most pressing issues, with the potential to disrupt a wide range of real-world and economic activity. Bot followers are dangerous to social media as these could alter perceptions of popularity and influence, which can have a ample amount of impact on every sector. As a result, new approaches must be developed to enable the detection and classification of bogus accounts. This study gives novel method for distinguishing original profiles. The method uses information gathered automatically from huge data to characterize typical patterns of fake account.
Research on mathematics test questions of high school entrance examination based on the SOLO taxonomy theory : Take the 2019-2021 Jinan high school entrance examination questions as an example

The high school entrance examination is the most important examination that junior middle school students face in their three-year study and life, which is of great significance to them. The analysis of high school entrance examination papers can help teachers carry out targeted teaching, so as to achieve better results. Based on the SOLO taxonomy theory, this paper analyzes the thinking level of Jinan high school entrance examination mathematics questions from 2019 to 2021, and divides the contents of the questions according to the curriculum standards, so as to analyze the proposition rules and characteristics of the high school entrance examination questions.Through the analysis, it is found that the content of Jinan high school entrance examination is relatively stable, and the Uni- structural level, Multi-structural level, Relational level and Extended Abstract level are examined on average. The most examined is the Multi-structural level, and the least is the Extended Abstract level; The knowledge points of the examination questions involve seven fields: number and formula, equation and inequality, function, the nature of the graph, the change of the graph, the coordinates of the graph, and statistics and probability. The examination direction is clear, all fields are grasped together, the content distribution is relatively balanced, the focus is prominent, and the goal is clear; The difficulty of the test questions tends to become simpler, not blindly pursuing difficult problems. In order to promote the further development of students' thinking level, corresponding teaching suggestions are put forward based on the research conclusions, so as to improve the teaching effect.
Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion

This research concentrates on reconstruction of the output line segments of the paper in [1]. The Line Segmenting module of [1] in some scenarios segments the alphabets and the associated matras of a line text in two separate line segments. These line segments are reconstructed using the Reconstruct Module to produce a line text with all its alphabets and its associated matras. This module uses one of the measures of dispersions, that is, standard deviation to accomplish reconstruction of output line segments. Then words are segmented from the line segments using WordSegmenting Module. This module uses one of the measures of central tendencies, i.e. mean and one of the measure of dispersions i.e, standard deviation to achieve word segmentation. Then characters are segmented from words using CharacterSegmenting Module.
Hierarchical online streaming feature selection based on adaptive ReliefF

In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm.
Named entity recognition of TCM electronic medical records based on the ALBERT-BiLSTM-CRF model

TCM electronic medical records contain a plethora of valuable clinical information. In this paper, the named entity recognition (NER) of TCM electronic medical records of orthopedics is studied based on the ALBERT-BiLSTM-CRF model. Based on the ALBERT pre-trained language model, the labeled data are encoded to complete the word embedding and obtain dynamic word vectors. Then, BiLSTM is applied to fully understand the context semantics. Finally, the concatenated vector is input to the CRF layer and decoded by the Viterbi algorithm to study NER. The model constructed in this paper recognizes four types of entities, i.e., clinical manifestations, body parts, TCM syndrome types, and TCM disease names, with F1 values of 98.31%,95.00%, 97.48%, and 99.96%, respectively. The precision rate, recall rate, and F1 value of overall entity recognition are 98.39%, 97.16% and 97.76%, respectively. As compared with the BERT-RNN-CRF, BERT-GRU-CRF, and BERT-BiLSTM-CRF models, the proposed model shows improvements in terms of precision rate, recall rate, and F1 value. In addition, the proposed model is capable of efficiently accomplishing entity recognition tasks, thus making it more suitable for the NER task of TCM electronic medical records.
Metaverse Teaching Overview

2021 is known as the "epoch-making year" of the metaverse, and "Metaverse + Education" has become one of the focus issues for educational researchers. As an important aspect of "Metaverse Education", "Metaverse Teaching" has caught worldwide attention. Based on "Metaverse Teaching", this paper begins with the concepts of "Metaverse Education", "Metaverse Teaching" and "Metaverse Teaching Design", introduces the characteristics of "Metaverse Teaching", and further analyzes the underlying logic of "Metaverse Teaching" from four aspects: contemporary theories of education, the ultimate goal of teaching designs, the development trend of education informatization and the development goal of lifelong education. The application of "Metaverse Teaching" in teaching management, ideological and political education, the design and development of teaching resources, and teaching evaluation is expounded next. Finally, it disserts the field architecture and technical support of "Metaverse Teaching".
AUV underwater 3D path planning based on particle swarm optimization-adaptive step-size cuckoo search algorithm

Aiming at the problems of unreachable search target, weak path finding and obstacle avoidance ability, and slow algorithm convergence speed when dealing with the 3D path planning of autonomous underwater vehicles (AUV) in the traditional cuckoo algorithm in complex waters, an AUV path planning algorithm PSO-ASCS (Particle Swarm Optimization-Adaptive Step-size Cuckoo Search Algorithm) is proposed, which combines the improved Adaptive Step-size Cuckoo Search and Particle Swarm Optimization. This research uses the idea of spatial layering to establish a three-dimensional model of complex waters to conduct path planning and obstacle avoidance experiments on the PSO-ASCS algorithm; The PSO-ASCS algorithm is tested and compared with the adaptive step size cuckoo search algorithm, standard cuckoo algorithm and particle swarm optimization by constructing a fitness function considering the three factors of path length, path smoothness and path hazard. Experiments show that the improved algorithm has strong global search ability and optimization performance, and the algorithm converges well, so that the AUV has the ability of efficient obstacle avoidance and path planning.
Information Superbahn: Towards a Planet-Scale, Low-Entropy and High-Goodput Computing Utility

In a 1961 lecture to celebrate MIT’s centennial, John McCarthy proposed the vision of utility computing, including three key concepts of pay-per-use service, large computer and private computer. Six decades have passed, but Mc- Carthy’s computing utility vision has not yet been fully realized, despite advances in grid computing, services computing and cloud computing. This paper presents a perspective of computing utility called Information Superbahn, building on recent advances in cloud computing. This Information Superbahn perspective retains McCarthy’s vision as much as possible, while making essential modern requirements more explicit, in the new context of a networked world of billions of users, trillions of devices, and zettabytes of data. Computing utility offers pay-per-use computing services through a 1) planet-scale, 2) low-entropy and 3) high-goodput utility. The three salient characteristics of computing utility are elaborated. Initial evidence is provided to support this viewpoint.
On Ups and Downs in Analyzing Web Activity Data: Notes from a Project

Analyzing data from the web is now one of the primary tasks, understood in a variety of manners and solved for a very wide variety of purposes. The talk describes the experience from a project, devoted to analyzing such data while drawing some more general conclusions. The project was aimed at distinguishing artificial ad-related traffic from the genuine one. The rationale is simple: The flow of money depends upon the number of clicks on/views of an ad. If so, fake clicking changes the market to the benefit of some, and to the loss of the other ones. The talk describes the problem and its conceptual framing, as well as a number of technical details, involving the issues and techniques of (1) variable analysis and choice; (2) clustering; (3) classification/classifiers; (4) potential hybrid techniques, along with citations of the most interesting results. These often imply definite general conclusions, some of them quite surprising.
Application of MCDM DIBR-Rough Mabac Model for Selection of Drone for Use in Natural Disaster Caused by Flood

Natural disasters around the world have resulted in enormous casualties and economic damage. Floods, as one of the natural disasters caused by climate change and inadequate human attitude towards nature, often create major problems for countries on all continents. Although preventive action is one of the ways to prevent the occurrence of floods, we are witnessing that they continue to happen, so the elimination of the consequences of floods and saving lives is given great attention. With the advancement of technology, modern means, machines and devices are increasingly used to rescue people from flooded areas, both to provide assistance to the endangered and their evacuation, as well as to monitor and reconnoiter flood-affected locations. The paper presents the application of the MCDM model DIBR-Rough MABAC in the selection of drones, based on the characteristics of drones, for use during floods, i.e., for surveying flooded areas and delivery of necessary materials, food and water. The presented model was successfully applied, where the optimal one was selected from a set of seven alternatives. Validation of the model was performed by analyzing its sensitivity to changes in weight coefficients. The results obtained by sensitivity analysis indicate that the mentioned model is stable to the change of the weight coefficients of the criteria, that is, that the correlation coefficients tend towards an ideal positive correlation.
Question and Answer Generation from Text Using Transformers

Natural language generation is one of the major tasks that comes under Natural Language Processing and, Question Generation (QG) and Question Answering (QA) are two NLP-based tasks that have found widespread application. The QA and QG tasks on a text paragraph aim to extract answers and generate questions, respectively. The automatic answering systems, chatbots, and trivia applications make use of these tasks and their use cases are growing. We have used a text-to-text transformer, T5 with a transfer-learning-based approach to achieve these tasks by training a single model in a multi-task fashion. The study is based on fine-tuning a T5 transformer in a multi-task way to produce questions from the text and extract answers upon providing the questions. The fine-tuning step is used to train the pre-trained transformer on data-science-related text paragraphs along with a large question answering dataset. This work aims to implement simplified data processing and training for fine-tuning the transformer model to improve the performance of the pre-trained model on technical data for QA and QG tasks.
Application of AI in Histopathological Image Analysis

Over the past decade, improvements in image analysis methods and substantial advancements in the processing power have allowed the development of powerful computer-aided analytical approaches to medical data. Tissue histology slides can now be scanned and preserved in digital form, thanks to the recent introduction of entire slide digital scanners. In such a form, they can serve as input data for Artificial Intelligence (AI) algorithms that can speed up standard procedures for histology analysis with high accuracy and precision. This research aimed to create an automated system based on AI for histopathological image analysis. The first step was normalizing H&E-stain images and then using them as input to the convolutional neural network. The best results are achieved using ResNet50 with the highest AUC value of 0.98 (±σ = 0.02). Such an approach proved to be successful in analyzing histopathological images.
QR Codes on Moscow Sites: Invisible Visibility, or Keeping Pace with the Changes?

This article focuses on the small-format multimodal text “embedded” in Quick Response (QR) codes on Moscow’s historical buildings, which aims to reveal what means this city uses to translate the national legacy to the awareness of the general public via QR codes. Despite their continuing proliferation and omnipresence in all city domains the information power and mind-framing potential of QR codes have not received a comprehensive analysis yet. This article covers the application range, metadata layout, and typology of QR coded objects, going on to a structural analysis of the multimodal texts—which combine audial, visual and verbal codes—using a multidisciplinary approach.
Leveraging Blockchain Technology in Industry 4.0 and Industrial Internet of Things (IIoT) Scenarios

Blockchain is highly known and well renowned in both the industry and academia world. Blockchain technology guarantees benefits in transparency, identification, trustability, credibility, organization, and collaboration as it works on the principles of transparency, decentralization, accountability, and immutability. The adoption of Blockchain technology in Industrial Internet of Things (IIoT) and other various sectors in Industry 4.0 presents the next step towards the future enhancement of blockchain. In this paper, we analyze and focus on various related research and applications of blockchain technology in various sectors of Industry 4.0, especially banking and finance. Despite offering a wide range of benefits and having numerous applications, the pace of integration of blockchain technology with the industry has been relatively sluggish. To overcome these issues, the paper highlights various shortcomings, research potentials, and challenges in the literature. The paper aims to illustrate the scope and importance of Blockchain technology in various sectors of Industry 4.0 as well as the risks associated with its use in parallel. Further, it intends to work as an initial point for future research to promote the application of Blockchain Technology.
Deep learning-based speaker-adaptive postfiltering with limited adaptation data for embedded text-to-speech synthesis systems

End-to-end (e2e) speech synthesis systems have become popular with the recent introduction of text-to-spectrogram conversion systems, such as Tacotron, that use encoder–decoder-based neural architectures. Even though those sequence-to-sequence systems can produce mel-spectrograms from the letters without a text processing frontend, they require substantial amounts of well-manipulated, labeled audio data that have high SNR and minimum amounts of artifacts. These data requirements make it difficult to build end-to-end systems from scratch, especially for low-resource languages. Moreover, most of the e2e systems are not designed for devices with tiny memory and CPU resources. Here, we investigate using a traditional deep neural network (DNN) for acoustic modeling together with a postfilter that improves the speech features produced by the network. The proposed architectures were trained with the relatively noisy, multi-speaker, Wall Street Journal (WSJ) database and tested with unseen speakers. The thin postfilter layer was adapted with minimal data to the target speaker for testing. We investigated several postfilter architectures and compared them with both objective and subjective tests. Fully-connected and transformer-based architectures performed the best in subjective tests. The novel adversarial transformer-based architecture with adaptive discriminator loss performed the best in the objective tests. Moreover, it was faster than the other architectures both in training and inference. Thus, our proposed lightweight transformer-based postfilter architecture significantly improved speech quality and efficiently adapted to new speakers with few shots of data and a hundred training iterations, making it computationally efficient and suitable for scalability.
Green technologies behind the Beijing 2022 Olympic and Paralympic winter games

•The advanced technologies of the first carbon-neutral Olympic Games are introduced.•Successful application experience of each technology is introduced.•This correspondence is conducive to the promotion and application of technologies.