Reviewerly/exHarmony huggingface dataset:
Machine Learning for Multimodal Electronic Health Records-Based Research: Challenges and Perspectives

Electronic Health Records (EHRs) contain rich information of patient health status, which usually include both structured and unstructured data. There have been many studies focusing on distilling valuable information from structured data, such as disease codes, laboratory test results, and treatments. However, relying on structured data only might be insufficient in reflecting patients' comprehensive information and such data may occasionally contain erroneous records. With the recent advances of machine learning (ML) and deep learning (DL) techniques, an increasing number of studies seek to obtain more accurate results by incorporating unstructured free-text data as well. This paper reviews studies that use multimodal data, i.e. a combination of structured and unstructured data, from EHRs as input for conventional ML or DL models to address the targeted tasks. We searched in the Institute of Electrical and Electronics Engineers (IEEE) Digital Library, PubMed, and Association for Computing Machinery (ACM) Digital Library for articles related to ML and DL-based multimodal EHR studies. With the final 94 included studies, we focus on how EHR data from different modalities were combined and interacted using conventional ML and DL techniques, the influence of the relationship between multimodal data on their fusion strategy, and how these algorithms were applied in EHR-related tasks. Further, we investigate the advantages and limitations of these fusion methods and indicate future directions for ML-based multimodal EHR research.
The Laplace Microarchitecture for Tracking Data Uncertainty

This article presents Laplace, a microarchitecture for tracking machine representations of probability distributions paired with architectural state. Laplace uses in-processor distribution representations, which are approximations of probability distributions just as floating-point number representations are approximations of real-valued numbers. The article presents two sets of instruction set architecture (ISA) extensions to 1) provide a mechanism to initialize distributional information in the microarchitecture; and 2) to allow applications to query statistics of the distributional information without exposing the uncertainty representations above the ISA. Unlike existing methods for uncertainty tracking, which require software to be rewritten in a domain-specific language or extensive source-level changes, Laplace achieves all of these benefits while requiring no changes to existing binaries to track uncertainty through them. Compared to repeated Monte Carlo re-executions of applications on a conventional microarchitecture, Laplace achieves the same level of uncertainty tracking accuracy with 2,076× fewer executed instructions on average (up to 21,343× fewer).
Synthetic-to-real: instance segmentation of clinical cluster cells with unlabeled synthetic training

The presence of tumor cell clusters in pleural effusion may be a signal of cancer metastasis. The instance segmentation of single cell from cell clusters plays a pivotal role in cluster cell analysis. However, current cell segmentation methods perform poorly for cluster cells due to the overlapping/touching characters of clusters, multiple instance properties of cells, and the poor generalization ability of the models.In this article, we propose a contour constraint instance segmentation framework (CC framework) for cluster cells based on a cluster cell combination enhancement module. The framework can accurately locate each instance from cluster cells and realize high-precision contour segmentation under a few samples. Specifically, we propose the contour attention constraint module to alleviate over- and under-segmentation among individual cell-instance boundaries. In addition, to evaluate the framework, we construct a pleural effusion cluster cell dataset including 197 high-quality samples. The quantitative results show that the numeric result of APmask is > 90%, a more than 10% increase compared with state-of-the-art semantic segmentation algorithms. From the qualitative results, we can observe that our method rarely has segmentation errors.
The basics of deep learning

Deep learning is one of the hottest subfields in artificial intelligence. This chapter mainly introduces the basics of deep learning. First, the forward neural networks are introduced, followed by the deep neural networks, including convolutional neural networks and recurrent neural networks. Then the learning objectives and training process of deep learning are presented. Finally, the computational and memory complexity of deep neural networks are analyzed for deploying them on edge computing terminals.
Research on Railway Relay Operation State Recognition Based on Improved Extreme Learning Machine

Aiming at the problem that it is difficult to identify the real state of railway relay due to the multiple random changes of the running state, a relay running state identification method based on PSO-ELM was proposed. Firstly, a variety of performance degradation parameters affecting the operation state of railway relays were extracted by relay contact life test, and preliminary selection was carried out by CCA correlation analysis, then, random forest importance score was used to analyze the contribution degree and classification ability of the preliminary selection results in different state intervals. The feature parameters with a total contribution degree higher than 80% were selected as the second selection results. Finally, the second selection results were input into the PSO-ELM model to complete the identification and prediction of the state. Through the analysis of measured data, the method proposed in this paper can effectively identify the running state of railway relays.
An Interpretable Model for Real-time Tracking of Economic Indicators Using Social Media Data

Measures of public opinion on economic matters are vital in creating accurate official statistics needed to design appropriate policy interventions and shape private investment decisions. This is traditionally done using public opinion polls and representative surveys. However, this process is time and money intensive and currently suffers from reduced public participation. As a result, official statistics are usually delayed and are not frequently available at the resolution needed for better policy interventions. Hence, researchers have looked into anonymized digital data to continuously sense information about public behaviors, especially those related to consumer sentiment index and unemployment insurance claims. However, past studies relied on linear models with simplistic assumptions and thus provided limited extrapolatory power and no insights as to why these predictive methods work. Worryingly, the strong correlations reported in these studies disappeared when the original models were tested with newer social media data. We propose a novel interpretable machine learning model, called Group Additive Gaussian Processes, to provide accurate and near real-time estimates of economic indicators about public behaviors using social media data, along with insights into the model behavior. Our model exploits the underlying structure in data and encodes interpretability in the modeling framework. It is based on Gaussian Process regression, which provides a robust non-parametric Bayesian learning framework that produces calibrated uncertainty measures along with its predictions. A key challenge in the learning task is learning from limited training data. We demonstrate how our model not only learns but also generalizes well in these scenarios. Through extensive evaluation we show how our model performs on two important indicators of economic health-consumer confidence index and unemployment insurance claims data. Further, we demonstrate how our model can reduce the need to conduct surveys by producing highly accurate and frequent estimates in between the surveying periods.
The Impact of the Blockchain Technology on the Smart Grid Customer Domain: Toward the Achievement of the Sustainable Development Goals (SDGs) of the United Nations

Our world needs access to affordable, reliable, and sustainable energy, along with climate adaptation and protection if we are to be sustainable, equitable, and inclusive. The purpose of this survey is to determine how blockchain technology can transform the smart grid in the customer domain of the maturity model, and how it can enhance achievement of the Sustainable Development Goals of the United Nations. We concluded from our study that the blockchain has the potential to contribute significantly to the achievement of goal number seven, Affordable and Clean Energy, and its interlinkages with goals number eleven, Industry, Innovation, and Infrastructure, and goal number eleven, Sustainable Cities and Communities, and goal number twelve, Responsible Consumption and Production, and finally goal thirteen, Climate Action. In this paper we provide a strong foundation upon which future research can be built and various elements can be combined for achieving the sustainable goals.
Adaptive and Global Approaches Based Feature Selection for Large-Scale Hierarchical Text Classification

High-feature dimensions is one of the obvious challenges facing large-scale hierarchical classification problem. One promising way to overcome such problem is reducing the features size by removing irrelevant, noise and redundant features. Filter-based feature selection methods are commonly used to construct smaller subset of most relevant features in either adaptive or global approach at each non leaf node within the hierarchy. Both the Feature selection approaches do not only reduce time and space complexity but also improve classification accuracy of the learning models. In this study, the effectiveness of the main approaches for constructing relevant features subset, that is adaptive and global approaches are analyzed using the two filter-based methods suitable for text classification. Experiment evaluation conducted on 20NG, IPC, and DMOZ-small datasets shows that implementing feature selection adaptively has more capability of selecting most relevant features, thus, achieving the best classification accuracy. Moreover, further evaluation shown integrating feature selection with LSHC records improve performance when compared to without feature selection.
Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression

Genetic Programming (GP) based symbolic regression is prone to generating complex models which often overfit the training data and generalise poorly onto unseen data. To address this issue, many pieces of research have been devoted to controlling the model complexity of GP. One recent work aims to control model complexity using a new representation called Adaptive Weighted Splines. With its semi-structured characteristic, the Adaptive Weighted Splines representation can control the model complexity explicitly, which was demonstrated to be significantly better than its tree-based counterpart at generalising to unseen data. This work seeks to significantly extend the previous work by proposing a multi-objective GP algorithm with the Adaptive Weighted Splines representation, which utilises parsimony pressure to further control the model complexity, as well as improve the interpretability of the learnt models. Experimental results show that, compared with single-objective GP with the Adaptive Weighted Splines and multi-objective tree-based GP with parsimony pressure, the new multi-objective GP method generally obtains superior fronts and produces better generalising models. These models are also significantly smaller and more interpretable.
Spark-based Network Log Analysis

In order to analyze the rules of the user&#x2019;s search intent. A statistical method of user search behavior based on Spark is proposed; based on the four indicators of correctness, precision, recall and F1-store evaluated by the classification model, the three classifiers of naive Bayes, logistic regression, and decision tree are compared. , The Naive Bayes classifier was selected as the classification model to realize the classification of the user&#x2019;s search term intentions. The experimental results show that the number of RDD partitions is closely related to the model training time. In the Sogou Chinese classification data set, the number of RDD partitions is 6 When the time, the model training time is 69.12s, and its training time is the least. Naive Bayes is able to classify 10 topics of user search intent, among which historical and technological topics account for the highest proportions, respectively: 18% and 15%.
Research on fronthaul scheme based on DRoF

Abstract With the rapid increase of global data traffic, storage and transmission of massive data is a challenging problem. Therefore, data compression is necessary. Based on DRoF, mobile fronthual has the advantages of high data rate, good connectivity and system capacity, and becomes the key part of communication system transmission. In this paper, an optimized vector quantization data compression algorithm based on DRoF system is proposed, which mainly studies OFDM baseband modulation signals. Firstly, the modulation signal is transformed by DCT. Then the optimized fast-global K-means clustering algorithm is used for quantization to overcome the problem that the original algorithm may choose noise points as the cluster center. Finally, Huffman coding is used to reduce the code length of the quantized data. Simulation results show that the proposed algorithm has good CR and EVM performance within the error tolerance range. It has a high degree of compression and signal recovery.
Parallelization of Algorithms by the OpenCL Library for Solving a System of Linear Algebraic Equations

The problem of accelerating the solution of a system of linear algebraic equations (SLAE) with the use of parallel calculations is considered. The object of research is the numerical methods of SLAE calculation by different processors. The calculation time of the task of parallelization of the algorithms for calculating the system of algebraic equations using the OpenCL library is estimated. The paper describes the algorithms for the numerical solution of SLAE with the estimation of the time complexity of algorithms that perform parallel calculations. The parallelization is performed on the CPU and GPU, depending on the settings specified for the OpenCL library. The running time of an algorithm running on a graphics processor is less than on a central one, regardless of the type of algorithm and the size of the coefficient matrix. The Gaussian iteration time deviated from the parabola, which was obviously due to the inhomogeneity of the equation system and the GPU load. It is established that the time of execution of iterations by the Gaussian method can sometimes deviate significantly from the parabolic one. Prospects for further research may be to use this library to parallelize valid mathematical calculations with the predominant use of GPUs.
Scholarly Artifacts Knowledge Graph: Use Cases for Digital Libraries

Scholarly communication process is constantly evolving to include new artifacts that present a part of the research. The traditional practice of conference and journal publications being the key artifacts to publish have changed to include new or complementary artifacts that help the audience grasp the research outcomes as much as possible as a single unit or whole. We also witnessed this trend with the library community, where different stakeholders – users, library patrons, or management – require that new research artifacts, such as scientific blogs, datasets, or citation links, be included in the library catalogs alongside existing artifacts. Knowledge Graphs (KG) have been applied as means to bring together data from heterogeneous sources, within or across multiple domains, and this has shown to be an effective approach. In this work, we adopt them to bring research artifacts of different types in a more compact research unit. This includes the approach to specify it and its components considering the library environment, and the accompanying methodology used to implement it. Finally, we explore the value and potential use cases from this integration for the library community in the domain of economics.
Sports Target Tracking Based on Discriminant Correlation Filter and Convolutional Residual Network

During the sports tracking process, a moving target often encounters sophisticated scenarios such as fast motion and occlusion. During this period, erroneous tracking information will be generated and delivered to the next frame for updating; the information will seriously deteriorate the overall tracking model. To address the problem mentioned above, in this paper, we propose a convolution residual network model based on a discriminative correlation filter. The proposed tracking method uses discriminative correlation filters as basic convolutional layers in convolutional neural networks and then integrates feature extraction, response graph generation, and model updates into end-to-end convolutional neural networks for model training and prediction. Meanwhile, the introduction of residual learning responds to the model failure due to changes in the target appearance during the tracking process. Finally, multiple features are integrated such as HOG (histogram of oriented gradient), CN (color names), and histogram of local intensities for comprehensive feature representation, which further improve the tracking performance. We evaluate the performance of the proposed tracker on MultiSports datasets; the experimental results demonstrate that the proposed tracker performs favorably against most state-of-the-art discriminative correlation filter-based trackers, and the effectiveness of the feature extraction of the convolutional residual network is verified.
Differential Privacy

This chapter recaps the original differential privacy definition and introduces the seven dimensions detailed in this Brief to show how it can be modified or extended.
Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society.Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so.Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society.The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs.The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively.The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players.A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately.Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration.The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment.Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people.This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words.Finally, these are converted into the respective words/sentences within a reasonable time.This provide a real time solution for the deaf people to communicate and interact with the society.
Counterfactual Representation Augmentation for Cross-Domain Sentiment Analysis

Cross-domain sentiment analysis aims to adapt sentiment classification models trained on one or more source domains to the target domain, which can effectively alleviate the problem of insufficient labelled data in specific domains. Unlike most previous methods of adjusting models based on observed data, we propose a novel counterfactual representation augmentation (CRA) method, which aims to improve target-domain generalization by constructing new counterfactual representations for training. Specifically, we train a domain discriminator to learn the domain discrepancy between the source and target domains on unlabeled data, and use a gradient editing method to directly construct counterfactual representations, which reduces the inductive bias of the source domain and augments the training data. Moreover, we further leverage an ensemble-based training method to indirectly encourage the target-domain classifier to rely more on robust features for prediction. Extensive experiments on a widely-used cross-domain sentiment classification benchmark dataset show that our method consistently surpasses different baseline methods on different tasks, demonstrating the strong ability to improve domain generalization. We also find that our model can effectively adjust the decision boundary to make the classifier more robust and generalized, through extensive qualitative and quantitative analysis.
Crowd detection and analysis for surveillance videos using deep learning

Due to the reduced costs, the availability of surveillance systems has increased many folds. Surveillance videos have proved to be very important for crime investigation. The recent technological advancements have made it possible to analyze the video to extract valuable information from it. Mass or crowd gatherings can be seen at a lot of places like airports, sports stadiums, various religious, educational, and entertainment-related events, etc. Video surveillance at such crowded places can prove to be very helpful in crowd management. It is very important to identify the presence of a crowd and detect the number of people in the gathering. This can prove very useful for the detection of sudden troupe build-up, to avoid riots. Moreover, it can also be very useful in the Covid-19 pandemic situation to avoid people gathering at a place. In this paper a framework for crowd detection is presented. Presence of crowd is found by counting unique people and then performing crowd analysis. Crowd analysis is performed by detecting the gender and age of people in the crowd.
Towards 6G

Sixth generation (6G) wireless communications have commenced in research after the deployment of fifth generation (5G). It's anticipated that 6G networks can take abundant heterogeneous devices and infrastructures with greater efficiency and safety over various resources, storage, computing. Conversely, this objective is hindered by a number of trust-related issues that are frequently ignored in network plans. Blockchain is a distributed ledger technology that has arisen in the recent decade with fundamental traits (e.g., transparency, traceability, immutability, decentralization, anonymity). Blockchain can inaugurate cooperative trust among distinct network entities and simplify various intrinsic features like privacy protection, tracing, trusted data interaction, verified certification for trusted users, monitoring functionalities for wireless networks, thus awarding a new paradigm towards 6G. This chapter provides the role of blockchain in wireless communication technologies.