Reviewerly/exHarmony huggingface dataset:
Role of swarm intelligence for health monitoring and related actions

The previous ten years have seen a lot of study into healthcare services and related technology advancements. With the introduction of the new coronavirus, each nation now places a high priority on healthcare. As a result, an Internet of Things (IoT)-based health monitoring system is the greatest option for such an outbreak in this regard. IoT is a modern Internet breakthrough that is a rapidly developing study topic, particularly in healthcare. To be more precise, the IoT has shown promise in linking a variety of medical equipment, sensors, and healthcare experts in order to deliver high-quality medical treatment in a remote place. Personalized healthcare surveillance has grown at such a rapid rate, thanks to the increased usage of peripheral sensors and smart devices. Even if the physician is a considerable distance away, IoT surveillance assists in the avoidance of illness as well as the correct diagnosis of one’s present physical condition. Patient care has increased, healthcare expenses have decreased, medical cares have become more accessible, and the healthcare company’s operational performance has grown. The current report provides an up-to-date overview of IoT-based technologies’ prospective healthcare applications. The development of the use of the healthcare IoT in tackling different healthcare challenges has been documented in this chapter from the standpoint of technological innovations, healthcare services, and implementations. In addition, possible healthcare IoT system problems and concerns are explored. In conclusion, the present study offers a consistent set of knowledge on the many sectors of implementation of healthcare IoT with the goal of assisting future academics who are interested in working in the field to acquire insight into the issue.
Residue feature analysis with empirical mode decomposition for mining spatial sequential patterns from serial remote sensing images

An extensive growth of serial remote sensing images paves the way for abundant data intended for the sequential spatial pattern determination in several fields like monitoring of agriculture, development of urban areas, and the vegetative area.However, conventional spatial sequential pattern mining is not applied efficiently or directly in the aspect of serial remote sensing images.Therefore, a residue feature analysis with empirical mode decomposition is proposed so as to enhance the spatial sequential pattern mining efficacy from the raster serial remote sensing images.At first, input images are being extracted by means of minima and maxima pattern by computing the mean of envelops and the intrinsic mode function components.If the intrinsic mode function condition is satisfied, then it is being subtracted from the original image; finally, the image is decomposed into many intrinsic mode functions and residue.The experimental outcomes attained indicate that the proposed strategy is proficient of mining spatial sequential pattern from the images of serial remote sensing.Though the support values of the patterns might not be attained accurately, the presented scheme guarantees that the whole patters are being extracted at lower consumption of time.
Three-Dimensional Path Planning of UAVs in Complex Urban Terrains: A Case Study of Emergency Medicine Delivery in Shanghai (China)

Unmanned aerial vehicles (UAVs), widely known as drones, are used in various domains for tasks including geological prospecting, e-commerce business, and emergencies. Because of the necessity for fast and efficient delivery for emergency medicine distribution, drones can play crucially important roles by their ability to pass through complex urban environments. Drones might therefore aid people living under strict lockdown conditions during surges cases of COVID-19 or other communicable diseases. Nevertheless, distribution routes are usually planned in two-dimensional space. Moreover, restricted areas in urban aerial domains might be overlooked because of complex environmental considerations. To boost the feasibility of drone use, three-dimensional (3D) path routing can be applied when planning aerial distribution routes for drones, such as those used for delivering emergency medicines. This study specifically examines a more reliable method of using heuristic algorithms and software ArcGIS. After collecting location data of chronic patients in lockdown areas from the Shanghai official information system database, 3D visualization of the terrain and complex airspace was done using ArcGIS. Secondly, UAV routing constraints are summarized according to current laws and regulations for UAV operation at low altitudes. Furthermore, feasible solutions are incorporated into this model. Finally, after improved ant colony optimization (ACO) application to 3D route planning problems, programming was done using MATLAB (ver. 2017b). Assuming guaranteed safety and compliance with regulations, the solutions demonstrate the algorithmic efficiency and provide a satisfactory route plan for emergency medicine delivery that might guide emergency delivery system routing design in similarly complex urban environments.
Top Accurate Cell Detection and Segmentation Using A Pen, Agile Digital Image Processing for Deep Learning Models

Abstract Introduction/Objective The Hematopathology labs suffer from insufficient productivity, profitability and increased liability. The predicted shortage in the Hematopathologists in the USA is 25% by 2040. Medical errors and results variability due to fatigue and stress could also increase. At least 99% specificity or 99% sensitivity is required in automated visual review of peripheral blood and bone marrow smears to be generalizable and work-fit. Fortunately, the growing digital Hematopathology scanners market besides the vastly developed artificial intelligence (AI) and machine deep learning (DL) technologies could make this realized soon. The AI/DL production cycle includes (1) images collection, (2) image processing/data science, (3) technology, (4) engineering, (5) testing and validation. Methods/Case Report Integration with a Hematopathology lab using digital scanners and/or integrated cameras on microscopes is the mean for whole slide images (WSI) collection. Image processing refers to AI/DL spoon-feeding with accurate, diverse and highly-dissimilar materials for learning. This requires providing hundreds of thousands examples of (a) WSI context correction for stain and resolution normalization, (b) cellular dimensions on the WSI patches for detection, (c) cellular segmentation/boundaries determination for counting (d) cellular labels for classification. Comparing with the confirmatory testing results including flow cytometry and immunohistochemistry is essential in both image processing and validation. Currently, image processing resembles 80-90% of the AI/DL cycle/pipeline which may take years of hectic work. Our proposed developed tool enables the Hematopathologists to use pens and draw on the boundaries of each cell in a field/patch. 3-15 seconds are needed for each cell. The required ground truth/masks for segmentation and dimensions for detection are generated in real-time with no need for data scientists. Results (if a Case Study enter NA) NA Conclusion Similar to the automated agile development-operation loop concept (DevOps), enabling the Hematopathologists to perform image processing directly will reduce the production cycles from years to weeks. No technology can perform qualitatively better than the human eyes, the proposed pen-based cell detection and segmentation tool results in 10-50 fold increase in quality and quantity. Promisingly, there is a trend among the AI/DL platforms companies to automate the AI/DL coding.
A Novel Framework for Concept Drift Detection using Autoencoders for Classification Problems in Data Streams

In streaming data environments like weather forecasting, health care monitoring, network traffic monitoring and energy consumption etc., data characteristics and probability distributions are likely to change over time, posing challenges for classification models to predict accurately. In such non-stationary environments where the patterns in data can change quickly, the pre-trained machine learning models may become outdated and hence, there is a need to update the model to maintain an acceptable predictive performance. Existing approaches to drift detection, particularly supervised and unsupervised, have inherent problems. Supervised methods detect drift based on the error rate and assume that labels are available immediately after prediction which may not be possible in several real-world scenarios. Unsupervised methods on the other hand suffer from high rate of false alarms and curse of dimensionality (i.e., the complexity faced in detecting drift across an increased number of features). To develop a more applicable technology, in this paper, we are concerned with unsupervised drift detection i.e., to detect drift independent of the labels. A recent set of experiments has revealed that it is possible to make an autoencoder learn the data distribution of a pre-defined set of classes and distinguish between the different class samples as they arrive. Based on this, we propose an Autoencoder-based Drift Detection Method (AE-DDM) which monitors the distribution of reconstruction loss in an incoming batch stream based on a thresholding mechanism to generate warnings and detect drift. AE-DDM has been tested on four synthetics (RBM, Hyperplane, Stagger and Gaussian)and one real word dataset (NOAA) with sudden and gradual drifts. AE-DDM starts generating warnings and then conforms the drift with zero delays in case of sudden drift with reduced false alarms. The classification results of ten most used machine learning classifiers on the NOAA dataset show that the detected drift is real. In this context, the potential contributions of our research are: 1) a novel framework for unsupervised drift detection based on the deep learning autoencoder technology, 2) A brief comparison of available drift detection approaches with proposed properties of an ideal drift detector, c) creation of a synthetic Gaussian dataset for drift detection research, and d) use of count threshold in conjunction with a batch threshold to combat false alarms in drift detection.
PERANCANGAN PETA ZONA GEMPA DAN TSUNAMI KOTA PADANG MENGGUNAKAN TEKNIK 3D ISOMETRIC ART

The influence of information technology in disaster mitigation continues to increase. For example, the internet and digital application in delivering information related to the development of aid distribution to disaster victims. In this study, the application of technology and digital map of the earthquake and tsunami zone in Padang City will be developed with isometric visualization, which will make it easier for the public to understand and read important information on the map, so that disaster mitigation are more targeted. Primary data collected from field observation in the form of evacuation routes and shelter buildings were transformed into visual data. Secondary data in the form of a map of the earthquake and tsunami prone zone in Padang City were obtained from relevant agency and references to book and journals related to the earthquake and tsunami. The data were then processed into 3D Isometric Art Map with the graphic application using the digital imaging method. The result of the study is in the form of 3D Isometric Art Zone Map by showing the digital imaging form. This map shows information in the form of the position of the Padang City to earthquake and tsunami hazard, evacuation zones and disaster services.
Fuzzy Modelling to Describe the Pollutant Concentration in Fluids

The study of the concentration dynamics of a pollutant substance in a fluid is a classic problem of fluid mechanics given by the transport equation $u_{t}+cu_{x}=0$, where $u=u(x,t)$ denotes the pollutant concentration along a horizontal pipe of a fixed cross-section in the positive $x$ direction at he time $t0$ and $c$ represents the fluid propagation velocity. In view of that, the velocity of propagation of the fluid is a physical quantity, obtained, generally in an approximate form, which makes such quantity uncertain. In this paper, we propose to obtain the concentration when the constant $c$ represents the fuzzy set. The concentration was obtained by using the Zadeh's Extension Principle. Through the concentration obtained, we analyze the influence of uncertainty on the fluid propagation velocity in the concentration dynamics and explore possible practial applications in case-studies of engineering, environmental and soil sciences.
Design of Neural Network Assisted Blockchain based Peer-to-Peer Electrical Energy Trading Platform

As traditional power grid is restructuring into smart grid, the concept of peer-to-peer electricity market is becoming a topic of immense interest among the researchers. With this blockchain enabled electricity trading platform has been gaining momentum owing to its capability to provide secure transactions. Work has been reported in recent times employing blockchain technology for implementing decentralized electricity marketing. Most of the work focuses on designing electricity marketing rules only on the smart contract. In the presented work a neural network based solar and wind power prediction model is shown working in tandem with blockchain based simple auction peer-to-peer electricity marketing mechanism. This gives direct advantage to the prosumers to auction their surplus power in day ahead marketing by having right knowledge of solar and wind power prediction of their plant. This reduces risk factor for prosumer paying penalty in case the promised energy delivery is not fulfilled and at the same time load to demand balance can be maintained. A smart contract for peer-to-peer electricity marketing rules has been implemented on Ethereum blockchain platform and deployed on Ropsten Test Network. The neural network based solar and wind power prediction model has been designed on MATLAB R2018b. At the end a demonstration is done considering a scenario of prosumer auctioning solar and wind power to different consumers on blockchain Ropsten test network. This research work does not consider the power distribution network constraints.
Using AI and IoT at the Edge of the network

In recent years, IoT devices have been widely used in a variety of sectors such as industry, smart farming, and smart homes. Its application requires performing high computational analysis in real-time. The research era of Artificial Intelligence has witnessed an intense development conducted by millions of research and applications that extend from systems recommendation to video/audio surveillance. AI algorithms have been deployed to IoT data to bring intelligent decisions for IoT applications. These numerous data increase the time of the data transition to the cloud, which becomes the bottleneck of the cloud-based architecture. The edge computing technology brings the AI algorithms to the Edge of the network to improve latency, bandwidth, and data privacy, and guarantee the high accuracy of the AI algorithms. Recently Federated learning (FL) is a machine learning technique that distributes the training among edge devices near to the data source in light of increasing privacy and leveraging from the massive data distributed among numerous edge devices. Therefore, in this paper, we introduce recent research that demonstrates the effectiveness of this approach and present the architectures, models, and methods that implement FL with IoT devices.
An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15.
Applying Deep Learning Cancer Subtyping Algorithms Trained on Physical Slides to Multiphoton Imaging of Unembedded Samples

Abstract Introduction/Objective Deep learning algorithms on digital images of physical tissue slides have shown potential improvements in accuracy and precision of diagnostic interpretation of neoplastic histology. Clustering-constrained- attention multiple-instance learning (CLAM) is one such method that identifies diagnostic sub-regions to accurately classify whole slides. Often, algorithm performance degrades when deployed on datasets that differ from the original set and it is subject to physical slide preparation variability. New multiphoton imaging modalities have potential workflow and quality advantages over physical slides, producing images analogous to whole slide imaging (WSI) without cutting artifacts, but performance of existing algorithms trained on digitized physical slides and applied to multiphoton images remains completely unknown. Given this, we aimed to test the performance of CLAM algorithms for subtyping renal cell carcinoma (RCC) and lung cancer (LC) applied to pseudo-colored multiphoton WSI. Methods/Case Report Clinical RCC and LC surgical resection samples were processed and imaged by Clearing Histology with MulitPhoton microscopy (CHiMP, Applikate Technologies, Fairfield, CT), producing digital images of un- cut, un-embeded tissue to generate H&amp;E-like optical slices. Multiphoton images were downscaled to 0.5 um/px to match algorithm target resolution. CLAM models for subtyping RCC (chromophobe, clear cell, papillary) and LC (squamous &amp; adenocarcinoma) previously trained using TCGA and CPTAC whole slide images of physical slides were applied directly to CHiMP multiphoton images without adjustment. Reference cancer subtype classifications were provided from physical and digital slides. Results (if a Case Study enter NA) For the subtypes included during training, multiphoton WSIs of RCC and LC were accurately subtyped by the CLAM models without stain normalization nor network fine tuning producing high prediction levels. Subtypes not included during the training (namely oncocytoma for RCC) resulted in low scoring model predictions (below 0.85), indicating specificity of identification. Multiple slide levels improved interpretation of several difficult cases for CLAM predictions. Conclusion This preliminary data suggests that CLAM models trained on standard H&amp;E WSIs for RCC and LC subtyping are applicable to pseudo-H&amp;E multiphoton WSIs without domain adaptations. This implies that diagnostic histologic features have been learned by these CLAM models and are efficiently recognized in digital histology images produced via CHiMP.
Analysis of Stability Big Data Environment of Intelligent Financial Data Abnormal QoS System based on Wolf Pack Algorithm

A new swarm intelligence algorithm, the Wolf Pack Algorithm has been proposed in this paper, and the convergence of the algorithm is proved based on the Markov chain theory. It reduces the risk of the algorithm falling into local optimum due to the excessively large penalty parameter. Inspired by the reproduction mode of wol ves, a big data environment analysis for the stability of the QoS system for abnormal data is proposed based on the binary wolf pack algorithm. Moreover, the Convolutional Neural Network with 4 hidden layers is used to classify and evaluate the constructed time series financial data. Data testing and analysis are performed using actual financial data. It is believed that the supervision system and relevant laws and regulations need to be improved first; secondly, the big data is used to collect personal credit records so as to establish a sound credit system as soon as possible; finally, through big data and computer technology, risk control methods are innovated to enhance the stability of Internet finance.
Minimizing Cross Intersections in Graph Drawing via Linear Splines

The generation of aesthetically pleasing graph layouts is the main purpose of Graph Drawing techniques. Recent contributions delved into the usage of Gradient-descent (GD) based schemes to optimize differentiable loss functions, built to measure the graph layout adherence to given layout characteristics. However, some properties cannot be easily expressed via differentiable functions. In this direction, the recently proposed Graph Neural Drawer (GND) framework proposes to exploit the representational capability of neural models in order to be able to express differentiable losses, specifically for edge intersection, that can be subsequently optimized via GD. In this paper, we propose to improve graph layout readability leveraging linear splines. We exploit the principles behind GND and use a neural model both to identify crossing edges and to optimize their relative position. We split crossing edges introducing linear splines, and threat the control points as novel “fake” vertices that can be optimized via the underlying layout optimization process. We provide qualitative and quantitative analysis over multiple graphs and optimizing different aesthetic losses, that show how the proposed method is a viable solution.
Is entanglement a unique resource in quantum illumination?

It is well-known that quantum illumination with a two-mode squeezed vacuum state as an initial entangled bipartite state achieves $6$ dB quantum advantage in the error probability compared to classical coherent-state illumination. Is entanglement the only resource responsible for the quantum advantage? We explore this question by making use of squeezing operations. Finally, we conclude that the answer to the question is negative.
Zero-Shot Recommendation as Language Modeling

Recommendation is the task of ranking items (e.g. movies or products) according to individual user needs. Current systems rely on collaborative filtering and content-based techniques, which both require structured training data. We propose a framework for recommendation with off-the-shelf pretrained language models (LM) that only used unstructured text corpora as training data. If a user $u$ liked \textit{Matrix} and \textit{Inception}, we construct a textual prompt, e.g. \textit{"Movies like Matrix, Inception, ${<}m{>}$"} to estimate the affinity between $u$ and $m$ with LM likelihood. We motivate our idea with a corpus analysis, evaluate several prompt structures, and we compare LM-based recommendation with standard matrix factorization trained on different data regimes. The code for our experiments is publicly available (https://colab.research.google.com/drive/1f1mlZ-FGaLGdo5rPzxf3vemKllbh2esT?usp=sharing).
Knowledge Retrieval using Foon

Flexible task planning is still a significant challenge for robots. The inability of robots to creatively adapt their task plans to new or unforeseen challenges is largely attributable to their limited understanding of their activities and the environment. Cooking, for example, requires a person to occasionally take risks that a robot would find extremely dangerous. We may obtain manipulation sequences by employing knowledge that is drawn from numerous video sources thanks to knowledge retrieval through graph search.