Reviewerly/exHarmony huggingface dataset:
Extraction and Representation of Financial Entities from Text

Abstract In our modern society, almost all events, processes, and decisions in a corporation are documented by internal written communication, legal filings, or business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corpora commonly used in research and highlights related work and state-of-the-art approaches to extract and represent financial entities and relations.The second part of this chapter considers applications based on knowledge graphs of automatically extracted facts. Traditional information retrieval systems typically require the user to have prior knowledge of the data. Suitable visualization techniques can overcome this requirement and enable users to explore large sets of documents. Furthermore, data mining techniques can be used to enrich or filter knowledge graphs. This information can augment source documents and guide exploration processes. Systems for document exploration are tailored to specific tasks, such as investigative work in audits or legal discovery, monitoring compliance, or providing information in a retrieval system to support decisions.
Bayesian Dense Inverse Searching Algorithm for Real-Time Stereo Matching in Minimally Invasive Surgery

This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed method is built on the fast ''dense inverse searching'' algorithm, which estimates the disparity of the stereo images. The overlapping image patches (arbitrary squared image segment) from the images at different scales are aligned based on the photometric consistency presumption. We propose a Bayesian framework to evaluate the probability of the optimized patch disparity at different scales. Moreover, we introduce a spatial Gaussian mixed probability distribution to address the pixel-wise probability within the patch. In-vivo and synthetic experiments show that our method can handle ambiguities resulted from the textureless surfaces and the photometric inconsistency caused by the Lambertian reflectance. Our Bayesian method correctly balances the probability of the patch for stereo images at different scales. Experiments indicate that the estimated depth has higher accuracy and fewer outliers than the baseline methods in the surgical scenario.
Few-Shot Knowledge Transfer for Fine-Grained Cartoon Face Generation

Cartoon face generation is a task that aims to convert real-face photos to exquisite cartoons. In this paper, we are interested in generating fine-grained cartoon faces for various groups, such as women, men, kids and the elderly. Although the cartoon faces of these groups share similar style, the appearances in various groups could still have some specific characteristics. In our setting, we assume that one of these groups consists of sufficient training data while the others only contain few samples, thus we can study how to transfer knowledge among groups and learn group-specific characteristics with only few samples. To tackle this problem, we propose a multi-branch translation model architecture and a two-stage training process. First, a basic translation model for the common group (which consists of sufficient data) is trained. Then, given new samples of other groups, we extend the basic model by creating group-specific branches for each new group. Group-specific branches are updated directly to capture specific appearances for each group while the remaining group-shared parameters are updated indirectly to maintain the distribution of intermediate feature space. To validate the effectiveness of our method, we further collect a cartoon-face dataset consisting of four groups with carefully designed characteristics. Experiments show that our approach is capable to generate high-quality cartoon faces for various groups. Codes and datasets are available at https://github.com/payne53/few_shot_face2cartoon.
Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping

Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.
Improved K-Means Text Clustering Algorithm Based on BERT and Density Peak

Since the K-Means algorithm was proposed, it has been widely concerned by researchers. Its advantage lies in its simplicity and efficiency, but it also has shortcomings. Aiming at the problems that random selection of initial clustering centers in K-Means algorithm may lead to the algorithm falling into a local optimal solution, making the clustering results unstable and the K-value need to be determined manually, an improved K-Means text clustering algorithm based on BERT and density peak value is proposed(BD-K-Means). First, the BERT model is used to generate the vector representation of the text, and then the density peak clustering algorithm is used to obtain the cluster center. However, aiming at some shortcomings of the density peak clustering algorithm, the Gini coefficient is introduced to find the best cutoff distance, and then the clustering center is obtained from adaptation through the drop change ratio. Finally, the obtained initial centroid and k-value are input into the K-Means algorithm. The experimental results show that the algorithm is better than K-Means algorithm, K-Means++ algorithm, DBSCAN algorithm and DCC-K-Means algorithm in F1 value and ARI value. It can effectively improve the text clustering effect and make the clustering result more accurate.
Comparative analysis of CNN and Viola-Jones for face mask detection

Abstract According to the World Health Organization, the Coronavirus (COVID-19) pandemic is causing a worldwide emergency, and one safe way to cover oneself is to wear masks. This pandemic constrained governments everywhere in the world to force lock-downs to avoid the transmission of infection. Reports show that wearing masks at work diminishes the danger of infection. We assemble our model by utilizing the concept of deep neural learning and AI. The dataset comprises pictures with masked faces and non-masked faces. Several computer algorithms are there for face detection. But this analysis centers around two of the most widely recognized procedures: The Viola-Jones algorithm and the Convolution Neural Networks. We will check whether the individual in the image/video wears a mask or not with a CV and Deep neural learning. Not only finding out about face mask detection, but this project also introduced the chance to delve into the field of computer algorithms.
The Awareness and Usage of Big Data for Cancer in Korea: A Survey Study

Objectives: A growing interest in big data for cancer research and treatment motivated us to investigate the demand for it by healthcare users.Methods:The survey was conducted from December 3, 2019 to January 7, 2020, in Korea.Respondents from 18 healthcare organizations participated in the survey.Among the 172 questionnaires received, 164 responses were used for the final analyses.Results: The majority of respondents showed a high awareness of big data related to cancer (n = 148, 90.2%).However, only about half of the respondents were aware of how big data related to cancer is used (n = 85, 51.8%).Among the respondents with experience using big data (n = 83, 50.6%), more than half used big data only about once a year (n = 43, 51.8%).The majority of respondents had particularly high demand for big data associated with "chemotherapy" (n = 154, 94.5%), followed by "cancer type at diagnosis status", "clinical stage, " and "recurrence." The main considerations for releasing cancer big data were "trustworthiness of data" (63.2%), "provision of valuable data" (58.9%), and "improvement of data accuracy" (55.8%).Conclusions: The study identified that even though respondents have a high awareness of and demand for big data related to cancer, it is not being sufficiently utilized at present.To increase the utilization of big data for cancer research and treatment, it is necessary to consider its purpose and how to make it available in line with the specific requirements of the healthcare industry, hospitals, and academia.
CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing

In the age of smart manufacturing, there are typically multitude of sensors that are connected to each assembly line. The amount of data generated could be used to create a digital twin model of the complete process; wherein virtual replicas of the device and the process can be created before and during the process. An important aspect is automatic anomaly detection in the manufacturing process. Anomaly/outlier detection identifies data-points, events and/or observations that deviate from the dataset’s normal behaviour. A major problem in predicting anomaly from datasets is the limited accuracy that can be achieved. Several state-of-the-art techniques provide very high accuracy (>95%). However, these result in a considerable increase in the required time, thereby limiting its use to non-real-time applications. This paper proposes a cluster-aided two-step (CATS) approach for anomaly detection wherein two unsupervised detection techniques are employed in serial. The technique used for the first step is density-based spatial clustering for applications with noise (DBSCAN), while the second technique is local outlier factor (LOF). The output of the first-step technique is fed to the second technique, thereby utilizing the knowledge generated in the first step. An extensive simulation analysis indicates that the proposed CATS algorithm results in >95% accuracy for the outlier population is above 15% with a prediction time of lesser than 85 s.
Integration of Smart Universities in the Region as a Basis for Development of Educational Information Infrastructure

The article offers a unique mechanism for step-by-step integration of smart educational structures into a single integration platform. A special feature of the proposed integration infrastructure in smart universities is the possibility of using various educational institutions as stakeholders in the overall structural composition of interaction. Bringing the integration model into a single educational scheme is carried out taking into consideration the concept of adaptation maximum. A model of the infrastructure platform formation for smart universities integration has been developed. The advantages of the model are the accumulation of a huge digital potential, which can be used by any educational institution to increase smartness. The experience of smart universities integrating is considered on the example of Russian universities.
Enhancing The Performance of Network Traffic Classification Methods Using Efficient Feature Selection Models

In the era of secure communication and the constantly changing pattern of internet applications, traditional packet classification methods fail to achieve the accuracy needed for diverse network management functions. Recently Machine Learning (ML) techniques have been used to design viable packet classification solutions. However, due to the complexity and dynamic feature of internet traffic, efficient packet classification is still challenging for various machine learning algorithms. In this paper, we propose the adoption of feature selection methods through dimensionality reduction to enhance the classifiers' performance. We evaluated the performance of four well-known classifiers, including K-nearest neighbour (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) with and without feature selection. We used two feature selection methods, including principal component analysis and Autoencoder. Experimental analysis is performed on real network traffic datasets with binary and multi-class categories. We assessed each classifier's performance using precision, recall, f-score, accuracy, and ROC. Experimental results show that the Precision, Recall, and F-score for the Multi-class problem are improved by 4.7 %, 6%, and 9%, respectively, after adopting either PCA or Autoencoder methods. The classification accuracy is also improved by up to 13%. We can also conclude that Autoencoder performed better for the KNN and LR, while PCA achieved comparable results for both the SVM and DT classifiers.
STAE: A Spatiotemporal Auto-Encoder for High-Resolution Video Prediction

Predicting high-resolution videos (≥ 256) is always a very difficult task in video prediction domain. To predict high-quality frames for high-resolution videos, both the challenging spatiotemporal representations and the computation resources are needed to be carefully considered. In this paper, we propose a SpatioTemporal Auto-Encoder for High-Resolution Video Prediction, which is named STAE. In our method, we first jointly utilize the spatial and temporal encoders to extract low-dimensional spatial and temporal features from the high-resolution video input, which can preserve the spatiotemporal information from the input and significantly reduce the computation load for the following modules. In addition, we design a SpatioTemporal Attention based Memory (STAM) to predict the spatiotemporal features for future frames using the encoded low-dimensional features. Then the predicted spatial and temporal features are decoded back to the high-dimensional data space using the spatial and temporal decoders. Finally, the predicted high-dimensional spatial and temporal representations are jointly utilized to predict the future frames. All modules in STAE are built on the basis of 3D neural networks to improve the local perception to videos. Experimental results show the proposed method outperforms diverse state-of-the-arts on widely used datasets and the computation load is relative low.
Unsupervised Classification Approach for Anomaly Detection in Big Data Streams

Detection of anomalies is the identification of unusual items, events or observations that raise suspicions by significantly differing from the majority of the data. And it is a technique used to identify unusual patterns, called outliers, which do not conform to expected behavior. For several applications such as financial and communication services, public health, and climate studies, detection of anomalies is important. In this paper, we are proposing a two-step methodology for classification of anomalies in big data streams. Initially, the class labels for the data stream are generated by using dense stream algorithm predicted class labels are then used to classify and train and classify gated recurrent unit (GRU)-based recurrent neural networks. In our proposed algorithm, dense stream clustering algorithm needs to be performed periodically when there is drastic change in data. GRU networks can then take the data to effectively classify the anomalies.
LSTM and ResNets Deep Learning Aided End-to-End Intelligent Communication Systems

Deep learning (DL) technology enables communication systems to provide intelligent transmissions to adapt to time changing wireless channel conditions. In this paper, with considerations that information bits are usually grouped into blocks or sequences for transmissions, we propose a DL end-to-end intelligent communication system. In our design, we construct a neural network (NN) constituted by long short-term memory (LSTM) units and residual networks (ResNets) architecture, to process the information-bearing sequences. More explicitly, we propose to add a forward feed path to compose the ResNets, thus the residual learning can be implemented to accelerate the convergence. Moreover, with the LSTM units, information-bearing sequences can be processed sequentially by the NN. Thus for larger number of sequences, better symbol error rate (SER) performances can be achieved since the features of the correlation between messages delivered at different time slots will be extracted to improve the detection performances. Subsequently, simulation results are provided to demonstrate that the proposed LSTM and ResNets based intelligent transmission systems can achieve better performances than benchmark systems over wireless channels undergoing the multiplicative fading and additive noises, while providing satisfactory robustness and convergence performances.
APENAS: An Asynchronous Parallel Evolution Based Multi-objective Neural Architecture Search

Machine learning is widely used in pattern classification, image processing and speech recognition. Neural architecture search (NAS) could reduce the dependence of human experts on machine learning effectively. Due to the high complexity of NAS, the tradeoff between time consumption and classification accuracy is vital. This paper presents APENAS, an asynchronous parallel evolution based multi-objective neural architecture search, using the classification accuracy and the number of parameters as objectives, encoding the network architectures as individuals. To make full use of computing resource, we propose a multi-generation undifferentiated fusion scheme to achieve asynchronous parallel evolution on multiple GPUs or CPUs, which speeds up the process of NAS. Accordingly, we propose an election pool and a buffer pool for two-layer filtration of individuals. The individuals are sorted in the election pool by non-dominated sorting and filtered in the buffer pool by the roulette algorithm to improve the elitism of the Pareto front. APENAS is evaluated on the CIFAR-10 and CIFAR-100 datasets [25]. The experimental results demonstrate that APENAS achieves 90.05% accuracy on CIFAR-10 with only 0.07 million parameters, which is comparable to state of the art. Especially, APENAS has high parallel scalability, achieving 92.5% parallel efficiency on 64 nodes.
Voicing concerns: The balance between data protection principles and research developments in forensic speech science

The status of forensic speech recordings among existing data protection guidance is not clear. The inherent nature of voice and the way in which forensic speech casework is currently allocated mean that there are additional barriers to incorporating real casework data into research activities. The key objective of this work is to explore data protection solutions that could enable the forensic speech science community to responsibly use real casework data for research and development purposes. While reviewing relevant guidance and rulings, issues such as proportionality, opportunism and data minimisation are addressed, as well as where voice sits in relation to the definition of "biometric data". This paper ultimately places forensic speech recordings in the data protection context to illuminate the specific issues that arise for this data type.
How the Education System Should Respond to the Technological Development and Informatization of the Society

The education system always reflects technological changes taking place in society. On the one hand, education reacts to such changes, on the other hand, in the bowels of the education system new members of the society that affect technologicalization are trained. Currently, about half of the world’s population is of an age characterized by the widespread use of information technology. For this part of global society, the use of modern technology in education is natural. This article discusses the process of technological societal development, characterized by a sequence of industrial revolutions. It shows that, arising from the development of informatization tools, information revolutions are superimposed on such revolutions. The characteristic features of the taking place right now fourth industrial revolution, which are significant from the point of view of the educational system development, are discussed. A well-reasoned forecasting of a new information revolution related to the emergence of automated translation systems is carried out. For each of the aspects of such revolutions, the set of proposed recommendations for improving the content, methods, tools and other components of educational systems is described. The article also considers the negative consequences for society and its educational system that can entail new information and industrial revolutions. The approaches by which the education system can reduce the negative aspects of the transition of society to a new informational and technological stage of its development are discussed.
An Algorithm for Dependency-Preserving Smart Home Updates

The update process of devices in a smart home is a complex endeavour. Individual device updates can influence the functionality of others, as devices may depend on other devices. The problems that are caused by breaking updates manifest only after the updates are performed and reverting them is generally impossible. In this paper, we propose an algorithm that is able to consider dependencies in a smart home system and, based on this, finds updates that preserve existing dependencies. These updates are not inspected in isolation but in combination with other updates. However, ideal solutions often depend on user preferences and the implications of one update over another can have far-reaching consequences that require filtration and recommendations.
Emulating complex networks with a single delay differential equation

A single dynamical system with time-delayed feedback can emulate networks. This property of delay systems made them extremely useful tools for Machine Learning applications. Here we describe several possible setups, which allow emulating multilayer (deep) feed-forward networks as well as recurrent networks of coupled discrete maps with arbitrary adjacency matrix by a single system with delayed feedback. While the network's size can be arbitrary, the generating delay system can have a low number of variables, including a scalar case.
A Novel Approach to Text Summarisation Using Topic Modelling and Noun Phrase Extraction

Over the past few years, one of the remarkable developments that happened on the web is the rapid growth of textual data. This substantial increase, however, induces a complication in the retrieval of vital information from the digitized collection of data. The conventional technique used to tackle this problem is Automatic Text Summarisation. This technique extracts the essential words or sentences from the data and summarises it without affecting the semantics. Automatic text summarisation is classified into two, Extractive and Abstractive. The Extractive method summarises a document by selecting the important words or sentences from it, based on some attributes while the Abstractive method attempts to generate its summary from the semantics of the data. In this paper, we propose a novel approach in Extractive text summarisation by using a new sentence scoring parameter. The experimental results show that the proposed sentence scoring parameter improves the performance of the Extractive text summariser, when compared with other summarisation models. To validate our proposed model, we compared it with four commonly used summarisation models on grounds of ROUGE-1 score and F1 score.