Reviewerly/exHarmony huggingface dataset:
The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia

The coronavirus spreads quickly through human-to-human transmission via close contact and respiratory droplets such as coughing or sneezing. Various studies have been carried out to deal with Covid-19. However, the cure for this virus has not been found until now. Based on data from the covid19.go.id page retrieved on January 1st, 2021, which was updated by the Ministry of Health, the overall number of confirmed cases was 1,078,314 active cases reaching 175,095 or 16.2% of confirmed cases, recovered 873,221 or 81.0% of confirmed cases, and death 29,998 or 2.8% of the confirmed cases. This study compares the two algorithms of data groups to analyze clustering patterns to determine the best data processing method. The data in this study sourced from the Ministry of Health, contained 4 attributes, including confirmed cases, treatment, recovery, and death cases. In this study, only 2 attributes were used: the confirmed and death cases. From the data analysis and processing results through a comparison between the K-Means method and the K-Medoids for clustering the spread of the coronavirus in Indonesia, a conclusion is derived. With the Davies Boulden index value from K2 to K9 values, it turns out that the K-Means method gets the smallest value at the K-5 of 0.064, while K-Medoids at the k-2 value of 0.411. Thus, from the two methods used, it can be concluded that the best method for clustering the spread of the coronavirus outbreaks in Indonesia is the K-Means method.
The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms

Abstract Background Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 ( n = 2976), GSE81538 ( n = 405), and GSE163882 ( n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 ± 0.023 and 0.891 ± 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 ± 0.012, and the weighted average of AUC was 0.931 ± 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 ± 0.037 and 0.883 ± 0.016, and that for NHG were 0.764 ± 0.052 and 0.882 ± 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients.
TeleGate: Immersive Multi-User Collaboration for Mixed Reality 360°Video

When collaborating on virtual content within 360° mixed reality environments it is often desirable for collaborators to fully immerse themselves within the task space, usually by means of a head-mounted display. However, these socially isolate any co-located collaborators, removing the ability to communicate through important gestural, facial, and body language cues. We present TeleGate, a system that instead utilises a shared immersive display to allow collaboration within remote environments between an arbitrary number of users, keeping collaborators visible while allowing immersive and interactive collaboration within remote environments.
Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list.Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients.Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model.This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process.To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients' survival chance.Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task.Furthermore, these two approaches are compared and evaluated using a real clinical case study.The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC).
‏An Enhanced Approach to Improve the Security and Performance for Deduplication

Cloud service providers providing users with efficient and effective storage and transmission of data. To reduce storage costs and save bandwidth, cloud service providers are attracted to use data de-duplication feature. Cloud users are interested in using the cloud safely and privately to protect the data they share on the cloud. Therefore, they encrypt the data before uploading it to the cloud. Since the intent of encryption conflicts with the de-duplication function, the data de-duplication feature becomes a hard problem. Existing de-duplication methods are ineffective in terms of both security and efficiency. They are either vulnerable to brute force attacks that enable the attacker to retrieve files, or they are computationally expensive. That is what drives us to suggest a method for removing duplicate data that is both performance and security effective. We'll start with a description of the implementations and functionality of de-duplication strategies, then move on to the literature that proposes various approaches to de-duplication and the security and efficiency problems that existing approaches face. Via the use of the AES-CBC algorithm and hashing functions, we have proposed an enhancement to improves the performance and protection of data de-duplication for users. Without the involvement of a third party, users' keys are created in a consistent and safe manner. We prove the efficacy of the recommended solution by putting it into practice and comparison with the existing techniques.
Designing Path of Collision Avoidance for Mobile Manipulator in Worker Safety Monitoring System Using Reinforcement Learning

Mobile manipulator is a robot for the next generation of processes. The mobile manipulator can increase process efficiency by being combined with the collaborative robot system. However, due to worker safety issues, simultaneous control of the mobile manipulator is still difficult. In this paper, we present the designing path of collision avoidance of a mobile manipulator for the safety of workers. By monitoring the position and velocity of a worker modeled as a cylinder, the mobile manipulator designs a path to avoid the worker using reinforcement learning. We learned the robot using simulation, and we checked the mobile and manipulator move to the destination by avoiding the worker.
Fall Detection using Human Skeleton Features

Falls are one of the leading causes of death and serious injury in people, especially for the elderly. In addition, falls accidents have a direct financial cost for health systems and, indirectly, for the productivity of society. Among the most important problems in fall detection systems is privacy, limitations of operating devices, and the comparison of machine learning techniques for detection. This article presents a fall detection system by means of a k-Nearest Neighbor (KNN) classifier based on camera-vision using pose detection of the human skeleton for the features extraction. The proposed method is evaluated with UP-FALL dataset, surpassing the results of other fall detection systems that use the same database. This method achieves a 98.84% accuracy and an F 1-Score of 97.41%.
Enhancing the Composition Task in Text Entry Studies: Eliciting Difficult Text and Improving Error Rate Calculation

Participants in text entry studies usually copy phrases or compose novel messages. A composition task mimics actual user behavior and can allow researchers to better understand how a system might perform in reality. A problem with composition is that participants may gravitate towards writing simple text, that is, text containing only common words. Such simple text is insufficient to explore all factors governing a text entry method, such as its error correction features. We contribute to enhancing composition tasks in two ways. First, we show participants can modulate the difficulty of their compositions based on simple instructions. While it took more time to compose difficult messages, they were longer, had more difficult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant’s intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate.
Respipe: Resilient Model-Distributed DNN Training at Edge Networks

The traditional approach to distributed deep neural network (DNN) training is data-distributed learning, which partitions and distributes data to workers. This approach, although has good convergence properties, has high communication cost, which puts a strain especially on edge systems and increases delay. An emerging approach is model-distributed learning, where a training model is distributed across workers. Model-distributed learning is a promising approach to reduce communication and storage costs, which is crucial for edge systems. In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost of ResPipe and demonstrate the trade-off between resiliency and communication cost. We implement ResPipe in a real testbed consisting of Android-based smartphones, and show that it improves the convergence rate and accuracy of training for convolutional neural networks (CNNs).
Micro-Ct Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference

Existing medical image super-resolution methods rely on pairs of low-and high-resolution images to learn a mapping in a fully supervised manner.However, such image pairs are often not available in clinical practice.In this paper, we address super resolution problem in a real-world scenario using unpaired data and synthesize linearly eight times higher resolved Micro-CT images of temporal bone structure embedded in the inner ear.We explore cycle-consistency generative adversarial networks for super-resolution and equip the model with Bayesian inference.We further introduce Hu Moments distance as the evaluation metric to quantify the shape of the temporal bone.We evaluate our method on a public inner ear CT dataset and have seen both visual and quantitative improvement over state-of-the-art supervised deep-learning based methods.Further, we conduct a multi-rater visual evaluation experiment and find that three inner-ear researchers consistently rate our method highest quality scores among three methods.Furthermore, we are able to quantify uncertainty in the unpaired translation task and the uncertainty map can provide structural information of the temporal bone.
FG-RS: Capture user fine-grained preferences through attribute information for Recommender Systems

Recommender system uses user-item historical interactions to portray user preferences. Due to the problem of data sparseness, auxiliary information is introduced to describe user preferences, such as user/item attribute information. However, some of these methods only consider user(item) attributes when modeling user preferences. Although another part of the methods interact with user attributes and item attributes, this interaction doesn't consider the potential preference of a certain attribute of the user to a certain attribute of the item. This makes the model unable to capture fine-grained user preferences. In fact, capturing fine-grained user preferences in terms of attributes is very effective in improving the recommendation effect. Therefore, in this paper we propose a recommendation method to capture user fine-grained preferences through attribute information. Specifically, there are two elaborately designed modules, interactive preference module and attribute fine-grained preference module. The former uses user-item historical interactions to model user's interaction preferences. In order to obtain the user's fine-grained preferences, the latter interacts with each attribute of the user and all the attributes of the item and uses the attention mechanism to mine the user's more fine-grained preferences on attributes. Extensive experiments on three publicly available datasets demonstrate the effectiveness of the proposed method.
DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool

Named Entity Recognition (NER) aims to identify and classify entities into predefined categories is a critical pre-processing task in Natural Language Processing (NLP) pipeline. Readily available off-the-shelf NER algorithms or programs are trained on a general corpus and often need to be retrained when applied on a different domain. The end model’s performance depends on the quality of named entities generated by these NER models used in the NLP task. To improve NER model accuracy, researchers build domain-specific corpora for both model training and evaluation. However, in the clinical domain, there is a dearth of training data because of privacy reasons, forcing many studies to use NER models that are trained in the non-clinical domain to generate NER feature-set. Thus, influencing the performance of the downstream NLP tasks like information extraction and de-identification. In this paper, our objective is to create a high quality annotated clinical corpus for training NER models that can be easily generalizable and can be used in a downstream de-identification task to generate named entities feature-set.
A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.
SAPHYR: Swiss Atlas of Physical Properties of Rocks: the continental crust in a database

Abstract The Swiss Atlas of Physical Properties of Rocks (SAPHYR) project aims at centralize, uniform, and digitize dispersed and often hardly accessible laboratory data on physical properties of rocks from Switzerland and surrounding regions. The goal of SAPHYR is to make the quality-controlled and homogenized data digitally accessible to an open public, including industrial, engineering, land and resource planning companies as well as governmental and academic institutions, or simply common people interested in rock physics. The physical properties, derived from pre-existing literature or newly measured, are density, porosity and permeability as well as seismic, magnetic, thermal and electrical properties. The data were collected on samples either from outcrops or from tunnels and boreholes. At present, data from literature have been collected extensively for density, porosity, seismic and thermal properties. In the past years, effort has been placed especially on collecting samples and measuring the physical properties of rock types that were poorly documented in literature. A workflow for quality control on reliability and completeness of the data was established. We made the attempt to quantify the variability and the uncertainty of the data. The database has been recently transferred to the Federal Office of Topography swisstopo with the aim to develop the necessary tools to query the database and open it to the public. Laboratory measurements are continuously collected, therefore the database is ongoing and in continuous development. The spatial distribution of the physical properties can be visualized as maps using simple GIS tools. Here the distribution of bulk density and velocity at room conditions are presented as examples of data representation; the methodology to produce these maps is described in detail. Moreover we also present an exemplification of the use of specific datasets, for which pressure and temperatures derivatives are available, to develop crustal models.
A defense method based on attention mechanism against traffic sign adversarial samples

A traditional neural network cannot realize the invariance of image rotation and distortion well, so an attacker can fool the neural network by adding tiny disturbances to an image. If traffic signs are attacked, automatic driving will probably be misguided, leading to disastrous consequences. Inspired by the principle of human vision, this paper proposes a defense method based on an attentional mechanism for traffic sign adversarial samples. In this method, the affine coordinate parameters of the target objects in the images are extracted by a CNN, and then the target objects are redrawn by the coordinate mapping model. In this process, the key areas in the image are extracted by the attention mechanism, and the pixels are filtered by interpolation. Our model simulates the daily behavior of human beings, making it more intelligent in the defense against the adversarial samples. Experiments show that our method has a strong defense ability for traffic sign adversarial samples generated by various attack methods. Compared with other defense methods, our method is more universal and has a strong defense ability against a variety of attacks. Moreover, our model is portable and can be easily implanted into neural networks in the form of defense plug-ins.
A Sociotechnical Systems Analysis of Knowledge Management for Cybersecurity

Knowledge management (KM) is a tool to tackle cybersecurity issues, provided it emphasizes on the interrelated social, organizational, and technological factors involved in cybersecurity. This paper proposes a sociotechnical systems analysis framework of KM systems for cybersecurity. Specifically, it applies a sociotechnical systems approach to investigation of constructs of KM systems for cybersecurity and identifies five major constructs of KM systems for cybersecurity: roles of KM in cybersecurity, organizational framework of KM for cybersecurity, cybersecurity analytics process, tools of KM for cybersecurity, and system architecture of KM for cybersecurity. The five constructs in the proposed sociotechnical systems analysis framework are analyzed. The paper makes contribution to the growing information systems literature by presenting a special case of sociotechnical systems analysis. The sociotechnical systems analysis framework provides guidelines for the development of KM systems for cybersecurity in organizations.
GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications

HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of −0.93 dB, −0.6 dB, and −1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation.
Facade-X: An Opinionated Approach to SPARQL Anything

The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g. RML), to know how to query and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to combine multiple languages (e.g. SPARQL Generate). In this paper, we explore an alternative solution and contribute a general-purpose meta-model for converting non-RDF resources into RDF: Facade-X. Our approach can be implemented by overriding the SERVICE operator and does not require to extend the SPARQL syntax. We compare our approach with the state of art methods RML and SPARQL Generate and show how our solution has lower learning demands and cognitive complexity, and it is cheaper to implement and maintain, while having comparable extensibility and efficiency.