Reviewerly/exHarmony huggingface dataset:
Cloud-Based Cross-Platform Collaborative AR in Flutter

Augmented Reality (AR) has progressed tremendously over the past years, enabling the creation of collaborative experiences and real-time environment tracking on smartphones. The strong tendency towards game engine-based approaches, however, has made it difficult for many businesses to utilize the potential of this technology. We present a novel collaborative AR framework aimed at lowering the entry barriers and operating expenses of AR applications. Our framework includes a cross-platform and cloud-based Flutter plugin combined with a web-based content management system allowing non-technical staff to take over operational tasks such as providing 3D models or moderating community annotations. To provide a state-of-the-art feature set, the AR Flutter plugin builds upon ARCore on Android and ARKit on iOS and unifies the two frameworks using an abstraction layer written in Dart. We show that the cross-platform AR Flutter plugin performs on the same level as native AR frameworks in terms of both application-level metrics and tracking-level qualities such as SLAM keyframes per second and area of tracked planes. Our contribution closes a gap in today&#x0027;s technological landscape by providing an AR framework seamlessly integrating with the familiar development process of cross-platform apps. With the accompanying content management system, AR can be used as a tool to achieve business objectives. The AR Flutter plugin is fully open-source, the code can be found at: https://github.com/CariusLars/ar_flutter_plugin.
A contribuição da telessaúde para as consultas de enfermagem durante a pandemia de Covid-19: uma revisão sistemática / the contribution of telehealth to nursing consultations during the Covid-19 pandemic: a systematic review

pandemic: a systematic review
Oil Family Typing Using a Hybrid Model of Self-Organizing Maps and Artificial Neural Networks

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel and not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indexes was employed on oil samples belonging to the Iranian part of the Persian Gulf oilfields. For the SOM network, at first, 10 default clusters were selected. Afterward, three effective clustering validity coefficients, namely, Calinski–Harabasz (CH), Silhouette (SH), and Davies–Bouldin (DB), were studied to find the optimum number of clusters. Accordingly, among 10 default clusters, the maximum CH (62) and SH (0.58) were acquired for 4 clusters. Similarly, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. According to the geochemical parameters, it can be deduced that the corresponding source rocks of four oil families have been deposited in a marine carbonate depositional environment under dysoxic–anoxic conditions. However, oil families show some differences based on geochemical data. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in oil family typing than those of common and overused methods of PCA and HCA.
Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees

Tree-based models underpin many modern semantic search engines and recommender systems due to their sub-linear inference times. In industrial applications, these models operate at extreme scales, where every bit of performance is critical. Memory constraints at extreme scales also require that models be sparse, hence tree-based models are often back-ended by sparse matrix algebra routines. However, there are currently no sparse matrix techniques specifically designed for the sparsity structure one encounters in tree-based models for extreme multi-label ranking/classification (XMR/XMC) problems. To address this issue, we present the masked sparse chunk multiplication (MSCM) technique, a sparse matrix technique specifically tailored to XMR trees. MSCM is easy to implement, embarrassingly parallelizable, and offers a significant performance boost to any existing tree inference pipeline at no cost. We perform a comprehensive study of MSCM applied to several different sparse inference schemes and benchmark our methods on a general purpose extreme multi-label ranking framework. We observe that MSCM gives consistently dramatic speedups across both the online and batch inference settings, single- and multi-threaded settings, and on many different tree models and datasets. To demonstrate its utility in industrial applications, we apply MSCM to an enterprise-scale semantic product search problem with 100 million products and achieve sub-millisecond latency of 0.88 ms per query on a single thread — an 8x reduction in latency over vanilla inference techniques. The MSCM technique requires absolutely no sacrifices to model accuracy as it gives exactly the same results as standard sparse matrix techniques. Therefore, we believe that MSCM will enable users of XMR trees to save a substantial amount of compute resources in their inference pipelines at very little cost. Our code is publicly available at https://github.com/amzn/pecos , as well as our complete benchmarks and code for reproduction at https://github.com/UniqueUpToPermutation/pecos/tree/benchmark.
An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people's life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales. On this basis, the change trends under different time scales are trained using an extreme learning machine to obtain the corresponding prediction values, and finally the epidemic prediction results are obtained by fitting through Adaptive Network-based Fuzzy Inference System. The experimental results show that the algorithm has good learning ability, especially in the prediction of time-series sequences can guarantee the accuracy rate while having low time complexity. Therefore, this paper not only plays a theoretical support for epidemic prevention and control, but also plays an important role in the construction of public emergency health system in the long run.
End-to-End Deep Policy Feedback-Based Reinforcement Learning Method for Quantization in DNNs

In the resource-constrained embedded systems, the designing of efficient deep neural networks is a challenging process, due to diversity in the artificial intelligence applications. The quantization in deep neural networks superiorly diminishes the storage and computational time by reducing the bit-width of networks encoding. In order to highlight the problem of accuracy loss, the quantization levels are automatically discovered using Policy Feedback-based Reinforcement Learning Method (PF-RELEQ). In this paper, the Proximal Policy Optimization with Policy Feedback (PPO-PF) technique is proposed to determine the best design decisions by choosing the optimum hyper-parameters. In order to enhance the sensitivity of the value function to the change of policy and to improve the accuracy of value estimation at the early learning stage, a policy update method is devised based on the clipped discount factor. In addition, specifically the loss functions of policy satisfy the unbiased estimation of the trust region. The proposed PF-RELEQ effectively balances quality and speed compared to other deep learning methods like ResNet-1202, ResNet-32, ResNet-110, GoogLeNet and AlexNet. The experimental analysis showed that PF-RELEQ achieved 20% computational work-load reduction compared to the existing deep learning methods on ImageNet, CIFAR-10, CIFAR-100 and tomato leaf disease datasets and achieved approximately 2% of improvisation in the validation accuracy. Additionally, the PF-RELEQ needs only 0.55 Graphics Processing Unit on an NVIDIA GTX-1080Ti to develop DNNs that delivers better accuracy improvement with fewer cycle counts for image classification.
Facemask Detection using Convolutional Neural Networks (CNN)

In last quarter of 2019, Corona Virus Disease (COVID-19), has flared up globally due to which many organizations and institutions are suffering and practically they are going to be closed if the current scenario does not change. COVID-19 is an transmissible disease causes due to Serious Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), which spreads from small liquid particles released from mouth or nose of an infected person. With this virus, anyone can get sick and become seriously ill or even die at any age. The best way to protect our self and others is by wearing a properly fitted facemask, washing hands regularly or frequently rubbing your hands by using an alcohol-based sanitizer and the way is to get vaccinated when ones turn comes. The proposed study uses Convolutional Neural Networks (CNNs) which is a technique of deep learning is used for classification by processing images. This study uses deep learning techniques for identifying if the person is with proper facemask or with no facemask from live video streams. For training the model the dataset is collected kaggle repository which contains 2000 images and attained an accuracy of 98.2% while training the model. The created system is put into action with the help of openCV, python and mobileV2 architecture v2 for recognizing the persons who are wearing and not wearing the facemasks.
An Enhanced ElGamal Cryptosystem for Image Encryption and Decryption

ElGamal cryptosystem is one of the well-known public-key algorithms for its ability to generate different ciphertexts for the same plaintext on successive runs. However, this algorithm results in a ciphertext occupying a larger memory space than its plaintext due to its encryption nature. As a result, it is pretty infeasible to use data that require their encrypted form to have the same size, such as image data. To overcome this issue, we propose an enhanced ElGamal cryptosystem that can be used for any given digital data message, including image, text, and video. The proposed approach mainly tests image data, consisting of three stages: key pair generation, image encryption, and image decryption. First, we generate as many random bytes as required for encrypting or decrypting images using the sender or receiver&#x0027;s public key information. Then, we use an XOR operation between each pixel in the image and each randomly generated byte to obtain the encrypted or decrypted image. Experimental results revealed that the proposed approach gives excellent results in various evaluation metrics tested on four different color images.
Geothermal Resources of the Asian Continent: A regional Assessment

The present work is an attempt for regional assessment of geothermal resources of the continental region of Asia. It is based on integrated analysis of the results of experimental data on heat transfer by conduction as well as hydrothermal and magmatic processes in the upper crust. The current compilation makes use of geothermal data reported at the website of IHFC but also estimates based on available information on occurrences thermal springs and volcanic events. The resource assessment has been carried out for 11260 sites distributed in 46 countries. A modified method of magmatic heat budget (MHB) has been employed in deriving estimates of geothermal resources in areas of recent volcanic activity. These datasets were reevaluated and spatially gridded using kigrid interpolation to construction of regional distribution maps of geothermal resources and interpreted on the basis of available information on tectonic setting and geological characteristics. According to results obtained the total resource base (RB) is estimated to be 47024 ± 4525 GJ. The mean resource base per unit area (RBUA) is 1765 GJ. The most prominent features in geothermal maps are the significantly high values of resource base of greater than 1000 GJ in countries such as Japan, Indonesia, China, Bhutan, Nepal and Pakistan. In addition, vertical distributions of temperatures were calculated in such areas for depths reaching down to 6 km. The results obtained indicate potential availability of high temperature resources in vast regions of the Asian continent.
Breast Cancer Detection in Mammography Image using Convolutional Neural Network

Breast cancer is one of the non-contagious diseases that tends to increase every year. This disease occurs almost entirely in women, but can also occur in men. One way to detect this disease is by observing mammography images. However, mammography images often tend to be blurry with low quality so that it is possible to detect them incorrectly. Therefore, in this study, automatic classification of breast cancer on mammographic images was carried out using the Convolutional Neural Network (CNN). This proposed system uses the VGG16 architecture with a transfer learning system. The proposed system is then optimized using Adam optimizers and RMSprop optimizers. The results of system testing for normal, benign, and malignant classifications obtained an accuracy value of 80% - 90% with the highest accuracy achieved using Adam's optimizers. With this proposed system, it is hoped that it can help in the clinical diagnosis of breast cancer.
Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks

Ductal Carcinoma In Situ (DCIS) is a non-obligatory precursor of Invasive Breast Cancer. It is the most common mammographically detected breast cancer. Predicting DCIS progression to invasive ductal carcinoma is a major clinical challenge due to the lack of a uniform classification system in the diagnosis and prognostication of this disease. To characterise the tissue microecology of DCIS, we proposed and tested the model "DCIS-Identification model" based on Generative Adversarial Networks (GAN) for detection and segmentation of DCIS ducts from multiplex immunohistochemistry (IHC) staining samples. We also trained a Spatially Constrained Convolutional Neural Network (SC-CNN) to detect and classify single cells based on their CA9 and FOXP3 expression. The DCIS-Identification model was evaluated on 8 whole slide images, resulting in an average Dice score of 0.95 for the segmentation performance. The single cell identification framework was tested on 10 randomly selected whole slide sections, achieving the average accuracy of 88.6% in a 5 fold cross validation scheme. With the proposed pipeline, we efficiently integrated deep learning, computational pathology and spatial statistics to report distinct differences in the microenvironments of DCIS and IDC/DCIS samples. The proposed pipeline provides a tool for a better understanding of the mechanism of tumours in DCIS and IDC/DCIS cases.
Researcher Wellbeing and Best Practices in Emotionally Demanding Research

HCI researchers increasingly conduct emotionally demanding research in a variety of different contexts. Though scholarship has begun to address the experiences of HCI researchers conducting this work, there is a need to develop guidelines and best practices for researcher wellbeing. In this one-day CHI workshop, we will bring together a group of HCI researchers across sectors and career levels who conduct emotionally demanding research to discuss their experiences, self-care practices, and strategies for research. Based on these discussions, we will work with workshop attendees to develop best practices and guidelines for researcher wellbeing in the context of emotionally demanding HCI research; launch a repository of community-sourced resources for researcher wellbeing; document the experiences of HCI researchers conducting emotionally demanding research; and establish a community of HCI researchers conducting this type of work.
DualVoice: A Speech Interaction Method Using Whisper-Voice as Commands

Applications based on speech recognition have become widely used, and speech input is increasingly being utilized to create documents. However, it is still difficult to correct misrecognition by speech, which makes it necessary to re-edit documents by other means such as manual input. It is also difficult to input symbols and commands because these may be misrecognized as text letters. To deal with these problems, we propose a speech interaction method called DualVoice in which commands are input in a whispered voice and letters in a normal voice. The proposed method does not require any special hardware other than a regular microphone, thus enabling a complete hands-free interaction, and it can be used in a wide range of situations where speech recognition is already available. We designed two neural networks, one for discriminating normal speech from whispered speech, and the second for recognizing whisper speech.
Analysis of Supervised Machine Learning Algorithms in the Context of Fraud Detection

In today’s era, where ‘time’ is considered as ‘money,’ people are completely depending on e-commerce and online banking for their routine purchases, shopping, and financial transactions. This increasing dependency on e-commerce are increasing fraud in online transactions, and credit card fraud is one example. Such malicious and unethical practices may cause identity theft and monitory loss to the people across the world. In this research paper, our effort is to identify the best Supervised Machine Learning algorithm that helps in classifying fraudulent and non-fraudulent transactions under credit card fraud on an imbalanced dataset. To conduct this research and compare the results, we have used five different Supervised Machine Learning Classification techniques. On implementing these machine learning techniques, it has been observed that both Supervised Vector Classifier and Logistic Regression Classifier perform better for detecting credit card fraud in an imbalanced dataset.
A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

Image manipulation can cause many ethical, economical, and political issues for everyone. It can be used to create fake information, fake ids, fake online profiles, fake news. A proper technique for image detection and verification is thus, a hot research area, which helps the affected people to overcome the forgery attacks. Due to the increased interaction among people through social networking sites, image forgery is prevalent and it is an essential activity to detect any forged image. This paper’s main objective is to identify fraud or tampered images effectively. By using the methods of copy move forgery detection, the obtained results are more accurate in a minimum time as compared to previous algorithms.
Multiclass Intent Analysis: Beyond the Conventional Polarities

The sentiment polarity for an element determines the orientation of the conveyed feeling, namely, whether the text communicates the user's positive, negative, or neutral sentiment toward the object in question. Sentiment analysis is a crucial task in natural language processing, with numerous real-world applications. Most sentiment analysis attempts to predict whether a given text is positive or negative. One of the most efficient and robust methods applied in the domain of sentiment analysis is deep learning. The recent advancements in social networking sites and their growing impact on the behavior of individuals have led to many pieces of research in the field of study of such networking platforms. Multiclass sentiment analysis identifies the exact sentiment delivered by the tweets instead of merely generalizing those sentiments as positive, negative, and neutral. In the case of online buying, most clients resort to the reviews about the same product they want to buy that are posted on the website. This work incorporates understanding the real-time situations of different customers' reviews about several products purchased through online shopping platforms thereafter determining whether their review is an Appreciation, Complaint, Enquiry, or Non-Intent. This paper includes Max-Vote Ensemble Technique, comprising three deep learning models: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a combined model of CNN and LSTM. These models predict the review’s sentiment and strive to classify multiple polarities breaking through the three conventional polarity categories.
Rapid Prototyping for XR

This course teaches principles of rapid prototyping for augmented, virtual, and mixed reality (XR). Participants will learn about low-fidelity prototyping with paper and other physical materials, and digital prototyping including immersive authoring. After an overview of the XR prototyping process and tools, participants will complete a hands-on session using easy-to-use digital authoring tools to create working interactive prototypes that can be run on XR devices. The course is targeted at non-technical audiences including HCI practitioners, user experience researchers, and interaction design professionals and students interested in XR design.
MoodTurner: A Self-Tracking Smart Jewellery to Support Awareness and Reflection in Sensory Processing Sensitivity Self-Care

This paper introduces MoodTurner, a wearable and mobile system that supports individuals in the self-care of high sensory processing sensitivity. We discuss how different aspects of personal informatics and embodied perception can be combined to help individuals in the tracking and reflecting on episodes of high sensory processing sensitivity, and overcome stigmatisation associated with the use of tracking tools. We present the design of a smart jewellery that tracks where episodes take place as well as their severity and a complementary mobile application that allows users to review and document episodes of high sensory processing sensitivity.
Identification of Fake Link Using Blockchain Technology

During the lockdown period, many people faced financial problems because of the jobless situation. Many of the people lost their jobs and starved for money and some more worked at their home and gained money by staying at home. There are also some people who earn online by using some gaming applications, apps and many more useful platforms to earn money. Some of the people don’t go to the side of online income because of the doubt of insecurity. But there are some really good websites which afford money when we play games or when we do online trading. This idea is for the people to identify the true earning website. In this model, the true one is identified using a fake link identifier (cloud security) and is entered through the smart contracts and distributed all over. We used the Design science analysis methodology (DSRM) method model, and developed a blockchain linked website so that the people can identify the true link and get benefit through it.