Reviewerly/exHarmony huggingface dataset:
Health Care Chatbot Using NLP and Flask

Abstract: Health care is very important to start a good life. However, if you have a health problem, it is very difficult to talk to your doctor. Chatbots can be used to communicate with text or voice interfaces and receive responses via artificial intelligence. Chatbots are programs designed to automatically interact with incoming messages. Chatbots can be programmed to respond the same each time and respond differently to messages containing specific keywords. In addition, you can use machine learning to adapt your response to your situation. More and more hospitals, nursing homes, and even private centers are now using online human service chatbots on their websites. These bots connect with potential patients accessing the site, find specialists, make appointments, and ensure appropriate treatment. In any case, the use of artificial intelligence in industries where people's lives can be a problem still raises personal concerns. The question arises whether the above tasks should be left to human staff. This healthcare chatbot system helps hospitals provide 24/7 online healthcare support. It answers both deep and general questions. It also helps generate leads and automatically delivers lead information to sales. Asking in turn helps the patient by guiding them on exactly what they are looking for.
GRAPH NEURAL NETWORK BASED OPEN-SET DOMAIN ADAPTATION

Abstract. Owing to the presence of many sensors and geographic/seasonal variations, domain adaptation is an important topic in remote sensing. However, most domain adaptation methods focus on close-set adaptation, i.e., they assume that the source and target domains share the same label space. This assumption often does not hold in practice, as there can be previously unseen classes in the target domain. To circumnavigate this issue, we propose a method for open set domain adaptation, where the target domain contains additional unknown classes that are not present in the source domain. To improve the model’s generalization ability, we propose a Progressive Weighted Graph Learning (PWGL) method. The proposed method exploits graph neural networks in aggregating similar samples across source and target domains. The progressive strategy gradually separates the unknown samples apart from known samples and upgrades the source domain by incorporating the pseudolabeled known target samples. The weighted adversarial learning promotes the alignment of known classes across different domains and rejects the unknown class. The experiments performed on a multi-city dataset show the effectiveness of the proposed approach.
Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) "breast tissue", (2) "benign lymph nodes", and (3) "suspicious lymph nodes". Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a "real-world" dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the "real-world" dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93-0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.
A Rule-Based Grammar and Spell Checking

Rule-Based Grammar and spell checking is the task of checking the content of any specific language using a particular set of rules for their grammar and a set of spellings for spell checking. In this work, libraries and functions of Python useful for Natural Language Processing are used to perform grammar and Spell checking. Python Libraries NLTK (for Sentence Boundary Detection and POS tagging), pyEnchant (for Spell checking by comparing with a standard dictionary), and Inflect (for indefinite article). The article includes information about these libraries, techniques, and steps used for Grammar and Spell Check.
A CLOSER LOOK AT SEGMENTATION UNCERTAINTY OF SCANNED HISTORICAL MAPS

Abstract. Before modern earth observation techniques came into being, historical maps are almost the exclusive source to retrieve geo-spatial information on Earth. In recent years, the use of deep learning for historical map processing has gained popularity to replace tedious manual labor. However, neural networks, often referred to as “black boxes”, usually generate predictions not well calibrated for indicating if the predictions are trustworthy. Considering the diversity in designs and the graphic defects of scanned historical maps, uncertainty estimates can benefit us in deciding when and how to trust the extracted information. In this paper, we compare the effectiveness of different uncertainty indicators for segmenting hydrological features from scanned historical maps. Those uncertainty indicators can be categorized into two major types, namely aleatoric uncertainty (uncertainty in the observations) and epistemic uncertainty (uncertainty in the model). Specifically, we compare their effectiveness in indicating erroneous predictions, detecting noisy and out-of-distribution designs, and refining segmentation in a two-stage architecture.
A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis

To improve the accuracy of the current vision-based linear displacement measurement in a large range, a new type of linear displacement sensing system, namely, image grating, is proposed in this paper. The proposed system included a patterned glass plate attached to the moving object and an ultra-low distortion lens for high-accuracy image matching. A DFT local up-sampling phase correlation method was adopted to obtain the sub-pixel translation of the patterns onto the target plate. Multiple sets of stripe patterns with different designs were located on the glass plate to expand the measurement range, based on the principle of phase correlation. In order to improve the measurement accuracy, the main errors of the image grating system were analyzed, and the nonlinear error compensation was completed based on the dynamic calibration of the pixel equivalent. The measurement results, after the error compensation, showed that the total error of the proposed system was less than 2.5 μm in the range of 60 mm, and the repeatability was within 0.16 μm, as quantified by standard deviation.
Hierarchical Novelty Detection for Traffic Sign Recognition

Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy.
Sentiment Analysis Pedulilindungi Tweet Using Support Vector Machine Method

Pedulilindungi application has many benefits but many controversies arise in the community. Various opinions in the form of tweets were expressed by the public, both positive and negative opinions. In this study, the objective is to make a classification model to classify tweets into two types of sentiment, namely positive and negative. The model is made in several stages, namely data retrieval, data filtering, data labeling, data preprocessing, splitting data train and data test, feature selection using Information Gain and Genetic Algorithm, and then classification using the SVM method. The model using two-stage feature selection and SVM method, obtained an accuracy value of 64.08% with 841 features and processing time of 0.033 seconds with 9.6% CPU usage. The model with two-stage feature selection is more efficient and effective than the one-stage feature selection model whose accuracy value is only 60.56% with 1800 features and a processing time of 0.044 seconds with 15.4% CPU usage.
Human Body Posture Recognition Approaches

Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential ‎ hardware technologies are ‎used in posture recognition systems‎. These systems capture and collect datasets through ‎accelerometer sensors or computer vision. In addition, this paper presents a comparison ‎study with state-of-the-art in terms of accuracy. We also present the advantages and ‎limitations of each system and suggest promising future ideas that can increase the ‎efficiency of the existing posture recognition system. Finally, the most common datasets ‎applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 2020
Chatbot Language - crowdsource perceptions and reactions to dialogue systems to inform dialogue design decisions

Abstract Conversational User Interfaces (CUI) are widely used, with about 1.8 billion users worldwide in 2020. For designing and building CUI, dialogue designers have to decide on how the CUI communicates with users and what dialogue strategies to pursue (e.g. reactive vs. proactive). Dialogue strategies can be evaluated in user tests by comparing user perceptions and reactions to different dialogue strategies. Simulating CUI and running them online, for example on crowdsourcing websites, is an attractive avenue to collecting user perceptions and reactions, as they can be gathered time- and cost-effectively. However, developing and deploying a CUI on a crowd sourcing platform can be laborious and requires technical proficiency from researchers. We present Chatbot Language (CBL) as a framework to quickly develop and deploy CUI on crowd sourcing platforms, without requiring a technical background. CBL is a library with specialized CUI functionality, which is based on the high-level language JavaScript. In addition, CBL provides scripts that use the API of the crowd sourcing platform Mechanical Turk (MT) in order to (a) create MT Human Intelligence Tasks (HITs) and (b) retrieve the results of those HITs. We used CBL to run experiments on MT and present a sample workflow as well as an example experiment. CBL is freely available and we discuss how CBL can be used now and may be further developed in the future.
IMPLEMENTATION OF STEM EDUCATION SYSTEM IN UKRAINE

The article deals with the contemporary situation in education sphere in Ukraine, describing the main aspects of STEM implementation. The article analyzes the main aspects of a learning strategy STEM which provides students mainly with an interdisciplinary approach to learning. Both the advantageous and disadvantageous issues of this learning approach are revealed in the article. The peculiarities of STEAM learning method in Ukraine have also been emphasized. In the course of the study, it has been discovered that modern educational system faces lots of challenges and needs to be improved by implementing an interdisciplinary approach to learning such as STEM education that promote not only skills such as critical thinking, problem solving, higher-order thinking, but also behavioral competencies such as adaptability, cooperation, organization, and responsibility. Examples of the implementation of STEM-education in the scientific and pedagogical project "Scientific and methodological foundations for creating an innovative model of STEM education" are presented. Despite the fact that Ukrainian schools are just beginning to prepare for work on the principles of STEM, some typical features of the introduction of STEM education, which is new to us, are already obvious. Significant shortcomings are, among other reasons, an insufficient number of teachers willing to work in a new way, and an insufficient material and technical base. These two aspects partially hinder the introduction of STEM-direction, based on the integration of knowledge, attraction of resources, cooperation of skills.
Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.
Optimizing rat and human blood cells sampling for in silico morphometric analysis

Measurements of Morphometric Parameters of the Blood Cells (MPBC) are key for the diagnosis of both mental and metabolic diseases. Several manual approaches or computational methodologies are useful to provide reliable clinical diagnosis. The sample processing and data analysis is relevant, however the sample handling on the pre-analytical phase remains scarcely evaluated. The main goal of this study was to favor the preservation of blood smear using a histological resin. This strategy lead us two practical approaches, give a detailed morphometric description of white blood cells and establish reference intervals in male Wistar rats, which are scarcely reported. Blood smears from male Wistar rats (n = 120) and adult men were collected at room temperature. The integrity of Wright-stained cells was evaluated by an in silico image analysis from rat and human blood smear preserved with a toluene-based synthetic resin mounting medium. A single sample of human blood was used as a control of procedure. The reference intervals was established by cell counting. Based on the results of segmentation algorithm followed by an automatic thresholding analysis, the incorporation of resin favor the conservation of cell blood populations, and lead to identify morphologic features such as nucleus/cytoplasmic shape, granules presence and DNA appearance in nucleus of white blood cells. The use of a histological resin could favor a fast and efficient sample handling in silico MPBC measurements both in the species studied as in wild animals.
Designing appropriate things: An experiential perspective on the effectiveness of artefacts in contributing to behaviour change

Behavioural design is an emergent discipline that aims to harness design's influence on behaviour in an intentional way.However, there is limited knowledge on how to translate knowledge on behaviour and its determinants to specific design properties in ways that can maintain such change.We adopt a user experiential view to discuss the role that artefacts and their materiality play in effectively changing behaviour by introducing the notion of appropriateness, a quality of user-artefact interaction that describes the fitness of an artefact to the user and context that may play a moderating role in effectiveness of a design intervention in contributing to behaviour change.Based on an in-situ exploratory study with two conceptual artefacts we show that this appropriateness could help to investigate the long-term effectiveness of artefacts.
Modeling Second Language Acquisition with pre-trained neural language models

Prediction of language mistakes is a task introduced by Duolingo as part of the Second Language Acquisition Modeling topic that aims to learn from the history of mistakes to improve the experience of language learners.Using transfer learning by means of pre-trained language models, we propose a framework that can learn the actual mistakes distribution according to which faraway words of a sentence have a higher chance to produce errors.To adapt the information provided by the pre-trained language models, more approaches based on feature extraction or fine-tuning were tried.However, according to our experiments, integrating these two options in a stack-and-finetune approach seems to be more appropriate for our task.Regarding the comparison of language models in terms of model distillation, we notice that distillation does not affect the effectiveness while significantly reducing the training time.We conclude that the model complexity should be adjusted to the specifics of the analysed problem and the distillation is an efficient option for low complexity corpora without considerably affecting the overall performance.
Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data

The proliferation of big data for web-enabled technologies allows users to publish their views, suggestions, sentiments, emotions, and opinionative content about several real-world entities. These available opinionative texts have greater importance to those who are inquisitive about their desired entities, but it becomes an arduous task to capture such a massive volume of user-generated content. Emotions are an inseparable part of communication, which is articulated in multiple ways and can be used for making better decisions to reshape business strategies. Emotion detection is a subdiscipline at the crossroads of text mining and information retrieval. Context is a common phenomenon in emotions and is inherently hard to capture not only for the machine but even for a human. This study proposes a decision-making framework for efficient emotion detection of mobile reviews. An unsupervised lexicon-based algorithm has been developed to tackle the problem of emotion prediction. Dictionaries and corpora are used as backend resources in the semantic orientation of emotion words, whereas the major contribution is to cope with contextualized emotion detection. The proposed framework outperformed the existing emotion detection systems by achieving 86% accuracy over mobile reviews.
Circuit Bending and Environmental Sustainability: Current Situation and Steps Forward

In this paper, we propose a set of reflections to actively incorporate environmental sustainability instances in the practice of circuit bending.This proposal combines circuit bending-related concepts with literature from the domain of sustainable Human-Computer Interaction (HCI).We commence by presenting an overview of the critical discourse within the New Interfaces for Musical Expression (NIME) community, and of circuit bending itself-exposing the linkages this practice has with themes directly related to this research, such as environmental sustainability and philosophy.Afterwards, we look at how the topic of environmental sustainability has been discussed, concerning circuit bending, within the NIME literature.We conclude by developing a list of recommendations for a sustainable circuit bending practice.
Multiple color image encryption based on cascaded quaternion gyrator transforms

In this paper, we propose a novel encryption algorithm using cascaded quaternion gyrator transforms that allows protecting multiple color images with efficiency at once. The originality is reflected in the integration of quaternion algebra with a cascaded encryption structure. First, image RGB channels are constituted into three imaginary parts of a quaternion matrix. A cascaded encryption structure is constructed to cooperate with the vortex phase mask, which is easy to transmit and saves storage. Then, a quaternion gyrator transform is conducted, after which the phase is extracted to serve as the decryption key while the amplitude is used as the real part of the following quaternion matrix. Finally, the amplitude of the last gyrator transform is scrambled to obtain the ciphertext. All phase matrices are further modulated by a secondary image phase mask for additional security. Numerical simulations have been conducted to verify the validity of the proposed algorithm. Experiments on several data test set demonstrate the robustness of the proposed method to various attacks.