Reviewerly/exHarmony huggingface dataset:
A Usability Management Framework for Securing Healthcare Information System

Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era.This has led to digital revolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remains a serious issue for all levels of stakeholders.Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations' reputation that translates into, financial losses and compromising software usability as well.Most of the data breaches are financially motivated, especially in the healthcare sector.The cyber invaders continuously penetrate the E-Health data because of the high cost of the data on the dark web.Therefore, security assessment of healthcare web-based applications demands immediate intervention mechanisms to weed out the threats of cyberattacks for the sake of software usability.The proposed disclosure is a unique process of three phases that are combined by researchers in order to produce and manage usability management framework for healthcare information system.In this most threatened time of digital era where, Healthcare data industry has borne the brunt of the highest number of data breach episodes in the last few years.The key reason for this is attributed to the sensitivity of healthcare data and the high costs entailed in trading the data over the dark web.Hence, usability management of healthcare information systems is the need of hour as to identify the vulnerabilities and provide preventive measures as a shield against the breaches.The proposed unique developed model of usability management workflow is prepared by associating steps like learn; analyze and manage.All these steps gives an all in one package for the healthcare information management industry because there is no systematic model available which associate identification to implementation steps with different evaluation steps.
Unsupervised Keyphrase Extraction from Single Document Based on Bert

Keyphrase extraction is the task of automatically selecting the phrases that best describe the text content in the document. Keyphrase extraction has essential research significance in text classification, text clustering, document retrieval. In this paper, we propose an unsupervised keyphrase extraction model based on Bert called BWRank. We use Bert as the pre-trained language model and use whitening operation to reconstruct document and phrase embeddings and reduce dimensionality. Finally, we add word frequency information in the scoring stage. Experiments show that our model outperforms other models on two datasets.
An efficient and lightweight image super-resolution with feature supplement network

Despite the great success of deep learning approaches in image super-resolution tasks in recent years, a large proportion of the approaches emphasize designing complex network structures to continuously deepen the network to achieve superior reconstruction performance. However, some shortcomings need to be addressed, such as the large number of parameters required for training the complex network structure, which makes the model difficult to optimize and train. To address the above problems, we propose an accurate and lightweight neural network which is called the feature supplement network (FSN) to achieve image reconstruction. Specifically, we design a novel residual feature supplement structure(RFSS), which is composed of some residual feature supplement groups (RFSGs) and layer feature attention module (LFAM). Each RFSG contains several heterogeneous residual channel attention blocks (HRCAB) with different levels of skip connections. In RFSS, the stacked RFSG is able to extract features efficiently with limited parameters. Meanwhile, LFAM further improves the feature representation capability of the network because it is capable of capturing more discriminative features from the original image by considering the correlation between the extracted features of different depths. Experiments of various models conducted on benchmark datasets show that our proposed method not only can achieve comparable results compared to the state-of-the-art methods, but also effectively recover high-quality images, even when using limited parameters than the existing method for image super-resolution. The codes of our proposed structure-FSN are accessible on https://github.com/ReckonerInheritor/RFSN.
Bayesian Modeling of Crowd Dynamics by Aggregating Multiresolution Observations From UAVs and UGVs

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) can be jointly deployed to form a collaborative surveillance system, where UAVs collect low-resolution images at a high altitude to obtain a global perception and UGVs observe high-resolution images within a focused detection range. Such multiresolution heterogeneous observations create opportunities yet pose challenges to model the dynamics of the targeted crowds. Existing approaches that integrate multiresolution observations rely on intensive computation of a large volume of historical data, and thus, result in a lack of computational efficiency, accuracy, and robustness. To address these limitations, this article proposes a new crowd dynamics modeling approach to aggregating the multiresolution information under a Bayesian inference framework. Beta-binomial and Normal–Wishart conjugate distributions were adopted to model crowd dynamics from low-resolution UAV observations and high-resolution UGV observations, respectively. Based on the proposed approach, a real-time model updating mechanism is developed to implement information aggregation for onboard crowd dynamics inference, where high efficiency, accuracy, and robustness are critical. Numerical simulations and onboard experiments were conducted to demonstrate the effectiveness and efficiency of the proposed modeling approach.
Face mask detection services of Covid19 monitoring system to maintain a safe environment using deep learning method

Abstract COVID19 has become one of the pandemic diseases that hit the world, including in Indonesia. Efforts to prevent the spread of this virus have been carried out, including by implementing health protocols, provide information services for the spread of the virus and emergency response, detection services for people suspected and infected with the virus and programs for preventing the spread of the virus and vaccination for all elements of society in Indonesia. This research focuses on the development of face mask detection system to detect the use of mask by people using deep learning method that focuses more on object detection. Face masked detection systems can be used to assist in monitoring compliance with the use of masks in public areas that have the potential to cause crowds so as to create a safe environment. The use of the Deep Learning method through the MobileNets V2 architecture was quite effective and gives an accuracy result in testing the classification training data of 0.99 at 10 epochs and has also been implemented in real-time direct testing.
Fine-Grained Entity Typing with a Type Taxonomy: a Systematic Review

Fine-grained entity typing (FGET) is an important natural language processing task. It is to assign fine-grained semantic types of a type taxonomy (e.g., Person/artist/actor) to entity mentions. Fine-grained entity semantic types have been successfully applied in many natural language processing (NLP) applications, such as relation extraction, entity linking and question answering. The key challenge for FGET is how to deal with label noises that disperse in the corpora since the corpora are normally automatically annotated. Various type taxonomies, typing methods and representation learning approaches for FGET have been proposed and developed in the past two decades. This paper systematically categorizes and reviews these various typing methods and representation learning approaches to provide a reference for future studies on FGET. We identify the current trends in FGET research: (i) Learning embedded feature representations to address the challenges posed by label noises, tail types and new entities; (ii) Tackling FGET jointly with other entity analysis sub-tasks (e.g., entity linking and coreference resolution) is also a promising direction. We also present a comprehensive review of type taxonomies, resources, applications for FGET and methods for automatically generating FGET training corpora.
Detecting breast cancer using novel mask R‐CNN techniques

Abstract Breast cancer is more common in women and the mortality rate also increases in recent days. Early detection of breast cancer is reduces the severity of the disease to some extent. Various image processing and classification techniques imposed on a particular image to detect and diagnose the breast cancer clearly. For early detection, an efficient methodology is needed. To reduce death rate, accurate discovery of the disease should be efficiently implemented. The mask R‐CNN is used for segmentation to identify abnormalities and ensemble CNN is used to classify the benign and malignant tumour from the given mammographic breast cancer image. The input is fed into the enhanced fuzzy based median filter which is used to remove speckle noises which in turn increases the clarity of images. The noise‐free images are fed into the proposed mask R‐CNN for segmentation. It is an effective framework for segmentation of medical images. The classification process is then given into ensemble based CNN classifier for prediction of mammogram images. The ensemble based CNN is proposed for the classification of mammograms as benign and malignant. The proposed method focus on the optimization which is performed to minimize the memory requirements and running time while maintaining the high performance of the classifier. The performance metrics such as accuracy, precision, f‐measure, and recall is evaluated to measure the efficacy of the proposed design. Using MIAS and DDSM dataset the performance metrics has been evaluated and compared with existing approaches. The results shows that the performance of the proposed method shows higher efficiency.
Prediction of Depression Using Machine Learning and NLP Approach

Today, for Internet users, micro-blogging has become a popular networking forum. Millions of people exchange views on different aspects of their lives. Thus, micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Because of the recent advent of micro blogging, there are a few research papers dedicated to this subject. In our paper, we concentrate on Twitter, one of the leading micro blogging sites, to explore the opinion of the public. We will demonstrate how to collect real-time Twitter data and use algorithms such as Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes (MNB) for sentiment analysis or opinion mining purposes. We are able to assess positive and negative feelings using the algorithms selected above for the real-time twitter info. The following experimental evaluations show that the algorithms used are accurate and can be used as an application for diagnosing the depression of individuals. We worked with English in this post, but it can be used with any other language. English in this document, but it can be used for any other language.
Learning Regularized Noise Contrastive Estimation for Robust Network Embedding

Skip-gram models are popular in large-scale network embedding for their cost-effectiveness. The objectives of many skip-gram based methods relate to the word2vec model which closely relates to Noise Contrastive Estimation (NCE). Among existing embedding methods, the differences mostly lie in how the node neighborhood is modeled e.g. by different ways of random walk, which leads to different learning strategies. Orthogonal to these efforts, we take a unified view that the NCE based methods commonly involve two basic NCE components in the learning objective. This perspective allows a natural generalization of the objectives by taking different forms of scoring function in the NCE components. We theoretically analyze how the vanilla NCE-based objectives suffer from the slow convergence speed and challenge in first-/second-order proximity preservation. We also prove the fundamental difficulty for NCE methods to capture non-linearity of complex networks. To mitigate such issues, we devise a general distance-based term added to the used NCE term, inspired by its physical meaning. The distance functions include Wasserstein-k distance and Laplacian/Gaussian kernel functions, with relatively little additional time overhead. The effectiveness of our approach is verified both by prototype examples as well as real-world datasets, for the task of node classification and network reconstruction.
Bridging values: Finding a balance between privacy and control. The case of Corona apps in Belgium and the Netherlands

Abstract This paper focuses on two examples of the introduction and use of COVID‐19 contact tracing apps in The Netherlands (CoronaMelder) and Belgium (Coronalert). It aims to offer a critical, sociotechnical perspective on tracing apps to understand how social, technical, and institutional dimensions form the ingredients for increasing surveillance. While it is still too early to gauge the implications of surveillance‐related initiatives in the fight against COVID‐19, the “technology theatre” put in place worldwide has already shown that very little can be done to prevent the deployment of technologies, even if their effectiveness is yet to be determined. The context‐specific perspective outlined here offers insights into the interests of many different actors involved in the technology theatre, for instance, the corporate interest in sociotechnical frameworks (both apps rely on the Google/Apple exposure notifications application programming interface). At the same time, our approach seeks to go beyond dystopian narratives that do not consider important sociocultural dimensions, such as choices made during app development and implementation to mitigate potential negative impacts on privacy.
Implicit bias and negative stereotyping in global software development and why it is time to move on!

Abstract Prior research documents how the use of national cultural differences when used as an argument for failed collaboration is problematic and makes information technology (IT) companies blind to the challenges in global software development (GSD). Nevertheless, we still witness how issues in GSD work are kept explained, applied, and predicted through generic descriptions of national cultural behavior. Based on two ethnographic studies conducted within two large Danish IT companies, we extend prior work on implicit bias . The paper presents empirical examples on the widespread practice of using racist and stereotypical rhetoric in GSD, which initially motivated us to look for alternative strategies for analyzing the actual and locally situated collaboration‐related problems within organizations involved in GSD. Our contributions are threefold: (1) We show how the widespread practice of using negative stereotypical rhetoric is weaved into the fabric of GSD engagements; (2) we present the empirical results of attending to implicit bias as an approach to explore and combat pervasive practices that deploy static cultural narratives and stereotypes in GSD; and (3) we propose three areas in GSD that software organizations should investigate to identify and address the implicit biases that potentially challenge or shatter their distributed collaborative work.
UIT-DroneFog: Toward High-performance Object Detection Via High-quality Aerial Foggy Dataset

In recent years, although various research has been performed on object detection with clear weather images, little attention has been paid to object detection with foggy aerial images. In this paper, we address the problem of detecting objects in foggy aerial images. Firstly, we create the UIT-DroneFog dataset by implementing a fog simulator (taken from the imgaug library) on 15,370 aerial images collected from the UIT-Drone21 dataset. This dataset has its distinguishing characteristic of having dense motorbike density in Vietnam with 4 objects: Pedestrian, Motor, Car, and Bus. Secondly, we further leverage two state-of-the-art object methods: Guided Anchoring, and Double Heads. The experiment results show that Double Heads achieve a higher mAP score, with 33.20%. Additionally, we propose a method called CasDou, which is the combination of Cascade RCNN, Double Heads, and Focal Loss. CasDou remarkably improves the mAP score up to 34.70%. The comprehensive evaluation points out the advantages and limitations of each method, which is the fundamental basement for further work.
A Novel Medical Image Encryption Using R鰏sler System

The technological advances made possible by the Internet, coupled with the unforeseen critical circumstances set in motion by the Covid-19 pandemic, have greatly increased the generation and transmission of medical images every day.Medical image transmission over an unsecured public network threatens the privacy of sensitive patient information.We have, in this paper, designed a new secure color medical image encryption algorithm based on binary plane decomposition, DNA (deoxyribonucleic acid) computing, and the chaotic Rössler dynamical system.At first, a bit-by-bit swap is performed on twenty four binary planes of the input image and encoded using DNA encoding rules.Thereafter, the Rössler system is used to modify the pixel values of the encoded image, which is subsequently decoded.Finally, the ciphered image is obtained by pixel-by-pixel permutation using position sequences.An innovative approach is used to compute keys from the color components of the input image.Extensive performance experiments of the proposed technique is conducted with metrics such as key sensitivity, key space, correlation coefficients (horizontal, diagonal and vertical directions), histograms, information entropy, number of pixel changes rate (NPCR), information entropy, unified average changing intensity (UACI), and encryption time.Comparative analyses have demonstrated that the proposed algorithm is fast, robust and competitive.
Movie Recommender System using critic consensus

Recommendation systems are perhaps one of the most important agents for industry growth through the modern Internet world. Previous approaches on recommendation systems include collaborative filtering and content based filtering recommendation systems. These 2 methods are disjointed in nature and require the continuous storage of user preferences for a better recommendation. To provide better integration of the two processes, we propose a hybrid recommendation system based on the integration of collaborative and content-based content, taking into account the top critic consensus and movie rating score. We would like to present a novel model that recommends movies based on the combination of user preferences and critical consensus scores.
Breast Cancer Classification with Random Forest Classifier with Feature Decomposition Using Principal Component Analysis

Cancer is one of the most deadly diseases in the world. It has no proven cure. The only hope is to detect it at an early stage and undergo treatment. Today, machine learning is helping health workers diagnose various deadly diseases at an early stage. We have developed a system that can help in diagnosing breast cancer at an early stage. We have used a Random Forest classifier with selecting the top 12 features from the dataset containing 32 attributes using Gini score criteria and applying principal component analysis. We have achieved an accuracy of 97.37%, precision of 0.9762, recall of 0.9535 and F1-score of 0.9647.
Safe transductive support vector machine

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Implementation and Analysis of the Results of the Application of the Methodology for Hybrid Multi-cloud Replication Systems

The main problem of local storage systems are the physical threats that can damage the integrity of the hardware and thus lose all the stored information. In this paper we present a methodology to perform data replication in PostgreSQL databases using OpenNebula cloud management software as a prototype of a private cloud and Amazon Web Services as a public cloud. The results obtained made it possible to determine the authenticity of the replicated information and to test the dynamic replication using multiple clouds.
An investigation into the accuracy of follow-on GPRS/mobile data CDRs

This study investigated the accuracy of 3G and 4G follow-on GPRS (General Packet Radio Service)/mobile data CDRs (Call Detail Records) from three UK mobile network operators (EE, Vodafone and Three). Follow-on GPRS/mobile data CDRs are currently considered to be more open to misinterpretation than voice/SMS CDRs as uncertainties exist regarding the correspondence between the timestamp and the Cell ID presented within the CDRs. Consequently, follow-on GPRS/mobile CDRs may be disregarded during criminal investigations, potentially losing valuable intelligence and evidence. To assess the accuracy of follow-on GPRS/mobile data CDRs, connected mode RF (Radio Frequency) surveys were conducted while simultaneously producing follow-on GPRS/mobile data CDRs in a travelling vehicle. This allowed a comparison of the start Cell ID presented in the CDR and the Cell ID that provided coverage to the device at the start time of the CDR to assess the correspondence between the timestamp and the Cell ID presented within the CDRs, and to consider the validity of the terminology used by experts. It was found that individual follow-on GPRS/mobile data CDRs cannot consistently place a device within the coverage area of the start Cell ID at the start time of the CDR. Instead, the results indicate that a terminology which places the device within the coverage area of the start Cell ID 'at or before' the start time of the CDR is appropriate. It is crucial that follow-on GPRS/mobile data CDRs are analysed with this consideration in mind so to interpret the evidence correctly.
Live 360 Degree Video Delivery Based on User Collaboration in a Streaming Flock

Streaming of live 360-degree video allows users to follow a live event from any view point and has already been deployed on some commercial platforms. However, the current systems can only stream the video at relatively low-quality because the entire 360-degree video is delivered to the users under limited bandwidth. Streaming video falling into user field of view (FoV) can improve bandwidth efficiency of 360-degree video delivery. In this paper, we propose to use the idea of “flocking” to simultaneously improve the accuracy of user FoV prediction and video delivery efficiency for live 360-degree video streaming. By assigning variable playback latencies to users in a streaming session based on their network conditions, a “streaming flock” is formed and led by “strong” users with low playback latencies in the front of the flock. We propose a long short-term memory (LSTM) based collaborative FoV prediction scheme where the FoV traces of users in the front of the flock are utilized to predict the FoV of users behind them. Given a predicted FoV, we develop an optimal rate allocation strategy to maximize the perceptual quality. By conducting experiments using real-world user FoV traces and LTE/5 G network bandwidth traces, we evaluate the gains of the proposed strategies over several benchmarks. Our experimental results demonstrate that the proposed streaming system can increase the overall quality dramatically by about 10 dB compared with heuristic FoV prediction strategy. In addition, the network-aware flocking formation can further reduce the video freeze without influencing video quality.