Reviewerly/exHarmony huggingface dataset:
Ensemble of Support Vector Machine and Ontological Structures to Generate Abstractive Text Summarization

Automatic summarization systems are much needed to lessen the information overload which is being faced by people due to exponential growth of data on World Wide Web. These systems choose the most significant part of the text from a single document or multiple documents and present the compressed surrogate form of the complete information which was intended to be conveyed. In this research paper, we propose an approach to generate summary from a given text first by extracting the most relevant sentences and then making further concise by creating ontological structures of these sentences and then generating the abstractive summary from these structures. Our proposed system is evaluated with DUC 2002 data set and it is found that the performance of this system as evaluated using ROUGE-1 is 58.175 which is better than other state of the art systems. The values reported in the experimental process of the research report the significant contribution of this innovative method.
Similarity-based Form Visualization for Supporting Sports Instructions

We propose a support system for instructing sports forms by comparing the forms of a user and those of a model using their similarity and visualizing differences. Captured images of the model performance are scaled to fit to those of the user and overlaid to visualize differences between them. Our system has 2 modes, A) practice mode and B) test mode. In mode A), the user repeats motions to receive real time feedback using similarity based on body joint positions. Mode B) visualizes motion trajectories of the model and user to differentiate their motions. Our system also outputs advice for user&#x2019;s improvements such as adjusting motion timing. We conducted a series of experiments with an implemented prototype of the proposed method. Although our method&#x2019;s superiority was not clearly confirmed, we have found several advantages in our method.
A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images

In this paper, we propose a novel content-based image-retrieval scheme that allows us to use a mixture of plain images and compressible encrypted ones called "encryption-then-compression (EtC) images." In the proposed scheme, extended SIMPLE descriptors are extracted from EtC images as well as from plain ones, so the mixed use of plain and encrypted images is available for image retrieval. In an experiment, the proposed scheme was demonstrated to have almost the same retrieval performance as that for plain images, even with a mixture of plain and encrypted images.
Secure Data Transmission through Steganography with ECC

Development of innovation and Internet make data to move all around the world so effectively and financially. This is something what made individuals to stress over their privacy and works. The steganography gives techniques that clients can hide their data inside other data that make them hard to perceive by attackers. A safe exchange of data can be accomplished by the combination of both steganography and cryptography. The sender encrypts the secret message using Elliptic Curve Cryptography. To give double security the scrambled message got from various encryption techniques is stowed away in a image dependent on DCT steganography. From opponent side the encoded message is removed from the image and is then decrypted utilizing decoding techniques to get the secret message. The data to be transferred can be encrypted and then use an image to hide the encrypted data before transmitting. This makes the transmitted data very secured and the chances of it falling in wrong hands becomes almost impossible. The receiving side can decrypt the cypher text and can easily obtain the original data. The strong encryption techniques don't allow unauthorized persons to view the content. Further hiding it through steganography gives the data a much more security and data integrity is maintained.
Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs

Negative samples in health and medical insurance domain refer to fraudulent or erroneous insurance claims that may include inconsistent diagnosis-procedure relations with respect to a medical coding system. Unfortunately, only a few datasets are publicly available for research in health insurance domain, yet none reports any negative claims. However, negative claims are essential not only to develop new machine learning approaches but also to test and validate automated artificial intelligence systems deployed by insurance providers. In this study, we introduce a synthetic negative claim generation procedure based on the bipartite graph representations of positive claims. Our empirical results demonstrate promising outcomes that will improve the development and evaluation processes of machine learning approaches in healthcare, where negative samples are required, but not available. Moreover, the proposed scheme can be applied to other domains, where bipartite graph representations are meaningful and negative samples are lacking.
Smart Plant Monitoring System using NodeMCU

Farming is the cultivation of plants and livestock. Plant monitoring is one of the most important tasks in farming. The goal of this paper is to use IoT in the NodeMCU system platform for plant monitoring and smart gardening. The primary goal of this paper is to reduce direct interaction and provide comfort to the farmer by improving the system's overall performance. Humidity, sunlight, and soil moisture are important factors to consider when monitoring plant productivity. Plant growth and health information must be provided to the user on a continuous basis by monitoring and recording these parameters. The NodeMCU interfaces with all of the sensors used in this project. Farmers can use IoT to directly monitor and control plant information via their smart phones. By sensing and controlling the parameters of the plants without their physical presence, this smart gardening system will provide the user with convenience and comfort. The smart gardening application can be installed on any Android-enabled device. The software used is the Arduino IDE and the IoT platform. The Arduino IDE is used to compile and upload the programme to the NodeMCU, and the IoT platform is used to display temperature, humidity, atmospheric pressure, and soil moisture from a distance. This will assist the farmer in understanding the relationship between plant growth and mentioned plant parameters.
Classification of vehicle types using fused deep convolutional neural networks

Classification of vehicle types using surveillance images is a challenging task in Intelligent Transportation Systems (ITS). In this paper, Convolutional Neural Networks for Vehicle types classification are comparatively studied. Firstly, GoogLeNet, ResNet50 and InceptionV4 are exploited as baselines for comparison. Secondly, we proposed a new network architecture based on GoogLeNet, ResNet50 and InceptionV4, named Fused Deep Convolutional Neural Networks (FDCNN), to take advantage of the ‘Inception’ module on parameter optimization and ‘Residual’ module on avoiding gradient vanishing, and applied the model to vehicle types classification. Thirdly, we created a vehicle dataset under the conditions of complicated and varied weather and lighting conditions, and conducted comparative experiments using the SEU vehicle dataset. Experimental results show much better performance of the proposed FDCNN with RMSprop optimizer on recognizing vehicle types. Specifically, the average classification accuracies of six vehicle types, such as truck, coach, sedan, minivan, pickup and SUV, are over 96.8%. Among the six classes of vehicle types, sedan is the most difficult to classify and the proposed FDCNN achieved over 93.81% accuracy in comparative experiments.
Recognition of Hand Gestures and signals from Depth silhouettes by Dynamic Time Warping

Hand gestures aid the user in unspoken communication and help interpret sign language. Modern nonverbal communication detection employs gesture recognition systems and allows us to recognize shapes and signals made by hand movements using numerous machine learning techniques. Gestures have a very high value in human-computer interaction and sign language detection systems. To implement the gesture recognition and detection the existing methodologies rely on the application of bodily sensors. Due to this, the effectiveness in communication becomes challenging and cumbersome. Hence targeting on the major fallacies of the existing approaches, the primary aspiration of this research work is to recognize hand gestures in real-time using depth sensors that support natural communication avoiding on body sensor dependency. A novel approach is presented to detect hand gestures using minimalistic training and the efficiency of other algorithms concerning this proposed technique has been compared. Experimental investigations on the proposed methodology have successfully confirmed that, the research exertion has obtained an overall gesture detection accuracy of 90.86% and a high precision of 98.25%.
Role of Artificial Intelligence and Machine Learning in Product Design and Manufacturing

Manufacturing companies must adapt and innovate in order to stay competitive. With this latest breakthrough, sustainable manufacturing will be made possible utilizing new technology. To encourage sustainable practices, it is essential that production practices be accompanied with wise use of production technologies. Due to this, AI-based methods, such as machine learning, have already been used in the industry for product design and production processes. The main goal of the study in this invited paper was to examine the literature on how artificial intelligence and machine learning (ML) are used in industry. Artificial intelligence and machine learning are now regarded as the key components in the smart industrial revolution. The significance of artificial intelligence and its usefulness in context to product design and manufacturing has been discussed carefully in this paper looking in to the satisfaction of future customer needs, and how to create a healthy and harmonious lifestyle also.
Lexicon-Enhanced Multi-Task Convolutional Neural Network for Emotion Distribution Learning

Emotion distribution learning (EDL) handles emotion fuzziness by means of the emotion distribution, which is an emotion vector that quantitatively represents a set of emotion categories with their intensity of a given instance. Despite successful applications of EDL in many practical emotion analysis tasks, existing EDL methods have seldom considered the linguistic prior knowledge of affective words specific to the text mining task. To address the problem, this paper proposes a text emotion distribution learning model based on a lexicon-enhanced multi-task convolutional neural network (LMT-CNN) to jointly solve the tasks of text emotion distribution prediction and emotion label classification. The LMT-CNN model designs an end-to-end multi-module deep neural network to utilize both semantic information and linguistic knowledge. Specifically, the architecture of the LMT-CNN model consists of a semantic information module, an emotion knowledge module based on affective words, and a multi-task prediction module to predict emotion distributions and labels. Extensive comparative experiments on nine commonly used emotional text datasets showed that the proposed LMT-CNN model is superior to the compared EDL methods for both emotion distribution prediction and emotion recognition tasks.
Semi-supervised Cell Classification Based on Deep Learning

Pathological examination is an important diagnostic means for cancer, including clinical cytological examination and histopathological examination. In pathological examination, it is often necessary to judge the type of cells. According to identifying the cells type or the number of different cell, doctors can determine whether to have cancer or the stage of cancer. However, pathological images contain a large number of different types of cells, which often need to be labeled with the professional knowledge of pathologists. In order to reduce the burden of pathologists, there are more and more methods to use computer aided cell classification. In recent years, with the rise of deep learning, it has become common to apply it to the segmentation and classification of pathological images. And the semi-supervised learning method can make good use of the image information of a large number of unlabeled samples to improve the performance of the model. But the existing methods of semi-supervised cell classification are not simple enough, and the sample selection mechanism cannot make full use of the characteristics of semi-supervised learning. Therefore, we propose a semi-supervised cell classification framework based on reliable sample selection mechanism, which can flexibly train different classifiers according to different data sets. The framework makes full use of semi-supervised learning, which makes the classification accuracy of model improved steadily.
An exploratory study of 21<sup>st</sup> century skills of undergraduate education students: A comparison between freshman, second, and graduation years

This study is an exploratory study aiming to add to the scarce research exploring the enhancement of 21st century skills across the undergraduate years of Education student. Data were collected from 329 undergraduates (96% females), who learned at Ashkelon Academic College, Israel. 103 students (31%) were at the beginning of the freshman year, 102 students (31%) were at the end of the second school year, and 123 students (38%) were at the end of the graduation and final school year. The 21st century skills were assessed using a 32 Likert-type item questionnaire about 21st century skills. Results indicated of positive relations between all 21st century skills, and between undergraduates` matriculation examination grades and all these skills, except for ICT. Significant differences in 21st century skills were found between freshman year and advanced years. 21st century skills mastery was higher in advanced years compared to freshman year. Educational implications are discussed.
Real-Time Heuristic Search with LTLf Goals

In Real-Time Heuristic Search (RTHS) we are given a search graph G, a heuristic, and the objective is to find a path from a given start node to a given goal node in G. As such, one does not impose any trajectory constraints on the path, besides reaching the goal. In this paper we consider a version of RTHS in which temporally extended goals can be defined on the form of the path. Such goals are specified in Linear Temporal Logic over Finite Traces (LTLf), an expressive language that has been considered in many other frameworks, such as Automated Planning, Synthesis, and Reinforcement Learning, but has not yet been studied in the context of RTHS. We propose a general automata-theoretic approach for RTHS, whereby LTLf goals are supported as the result of searching over the cross product of the search graph and the automaton for the LTLf goal; specifically, we describe LTL-LRTA*, a version of LSS-LRTA*. Second, we propose an approach to produce heuristics for LTLf goals, based on existing goal-dependent heuristics. Finally, we propose a greedy strategy for RTHS with LTLf goals, which focuses search to make progress over the structure of the automaton; this yields LTL-LRTA*+A. In our experimental evaluation over standard benchmarks we show LTL-LRTA*+A may outperform LTL-LRTA* substantially for a variety of LTLf goals.
Text-based Conversation Analysis Techniques on Social Media using Statistical methods

When two or more people exchange their opinions, we call it a conversation. When people sharing their perspectives attain a value of contributing and exchanging ideas, the dynamic of a discussion works best. It is believed that conversation, or “dialogue,” is the ultimate form of learning since it provides for rapid and clear answers to inquiries as well as the sending and receiving of messages. Good talks bring individuals together around shared information, values, and emotions. People's interactions have transitioned to social media platforms such as Twitter, Facebook, and Messenger in recent years. Social media was created to allow individuals from all over the world to communicate by text, audio, video, and other forms of media. In most cases, communication can be formed solely with a plain text document. There is a lack of prediction and information extraction during human interaction. Some conversation filtering processes are constructed based on content relevance and social metrics. This review adds to the general understanding of Conversation Analysis' methodological development, providing important insights for anyone interested in applying it to analyze social interaction as it occurs online.
Asymmetric encryption by optical Kerr nonlinearities exhibited by electrochromic NiO thin films

Herein is analyzed how an electric field can induce a band gap shift in NiO films to generate an enhancement in their third-order optical nonlinearities. An electrochromic effect seems to be responsible for changes in absorbance and modification in off-resonance nonlinear refractive index. The optical Kerr effect was determined as the dominant physical mechanism emerging from the third-order optical susceptibility processes present in a nanosecond two-wave mixing configuration at 532 nm wavelength. Absence of any important multi-photonic absorption was validated by the constant trace of high-irradiance optical transmittance in single-beam mode. The inspection of nonlinear optical signals allowed us to propose an exclusive disjunctive logic gate assisted by an electrochromic effect in an optical Kerr gate. Asymmetric encryption by our XOR system with the influence of a switchable probe beam transmittance and electrical signals in the sample was studied. Immediate applications for developing multifunctional quantum systems driven by dynamic parameters in electrochromic and nonlinear optical materials were highlighted.
Analysis the impact of digital trends and IoT procedural scheme on traditional banking system

The next stage of the digital revolution is here, and it's going to have a significant impact on consumers' daily lives: the Internet of things. Things and locations may be linked to the Internet, even though the Internet is often confined to electronic devices. Financial institutions are increasingly using IoT in a wide range of ways. Still, this essay aims to show how these technologies affect conventional banking procedures and how they may be used in the future. It would be fascinating to compile all the digital advancements that have a comparable influence on bank procedures. We will use these new approaches to carry out the digital transition as we have done before.
Sentiment Analysis using Machine Learning and NLP for Digital Education

In this present days, a higher level of education about various subjects can be acquired through various applications and websites which are known as online educational platforms. That is why digital education has its platform to spread knowledge among people. Each of these application contains individual content along with the user point of view which is usually expressed in the comment section, and those reviews or comments can be processed through the natural language processing. On the other hand, there are numerous machine learning algorithms for evaluating these reviews to find out the best and most user-friendly online educational platform. This paper has considered 6000 reviews which have been collected manually from education-related six applications and classified them by analyzing the feelings of the reviewers. The use of 4-gram with Logistical Regression produced higher accuracy (88%) among five algorithms to classify the reviewer&#x2019;s comment.
Long-Term Trend Analysis for Social Media Content Published During COVID-19 Pandemic

Social media platforms are open for society to distribute numerous vectors of information upon personal interest. A substantive component of social media platforms is mining long-term trends. COVID-19 is an indispensable keyword captivating from last year in multiplex social media platforms providing opportunities for societal to express opinions in multiple languages. Existing literature is scarce in multiple aspects, such as the lack of resources that provide high accuracy long-term trend detection in social media platforms and missing the substantive factor for long-term trend detection, and time series forecasting over Natural Language Processing. This research presents a novel methodology to extract insights into low resource language and the responses are validated using the built crowdsourcing platform. Additionally, research employees with several classical time series analysis techniques and different LSTM RNN to identify the best-performing long-term trend analysis method. Novel concept deployed by extracting public data available in YouTube and Twitter which has at least one year of life expressing content related to COVID-19. Preponderance textual engagements were felt under the low resource language category. Crowdsourcing supports mitigating the risk of the intimidating issue. Finally, the data set is employed with time series forecasting. Mean Square Error and Huber loss were employed for testing. Authors argue there is no long-term trend in Twitter posts and YouTube videos with comments more petite than a hundred. YouTube videos with more than a hundred comments were further analyzed and the authors revealed that Stacked LSTM outperforms the LSTM used for trend analysis for more than one year by achieving 84.96% accuracy.
UI/UX Design Methodology of Portable Customizable Simulated Pet System Considering Human Mental Health

The economic depression accompanied by COVID-19 pandemic has also had some adverse effects on people's mental health. 4 in 10 adults have symptoms of anxiety and depression, and these symptoms may continue to increase. In particular, the reported rate of mental health disorders among adolescents is as high as 56.2%. Recently, pet ownership has become widely accepted as a means of improving mental health. However, due to various reasons, the separation of owners and pets has also increased significantly. Therefore, we have developed a portable simulated pet "KEDAMA", which can be used in place of real pets, and can be customized with fur and sound. The price is relatively cheap and can be ordered easily. We conducted a statistical analysis on 20 users, and the results show that the "KEDAMA" has a positive effect on users' mental health.
Cross Domain Answering FAQ Chatbot

The world is leaping into a future where everything will be automated. Most of the tasks could be completed without the intervention of human beings. Heading in the right direction, chatbots have taken the world by storm. A chatbot is an automated query response system which deals with end users to answer their queries and eliminates the need for customer services. There wouldn&#x0027;t be any delay in providing services if a chatbot handles all the queries in a systematic way. The University website is the place where students, teachers, aspiring teenagers and parents tend to visit to know about the university. By designing a chatbot for the university website which can handle the frequently asked questions we could reduce the workload on the services team. Chatbots fail to answer queries outside their domain of interest, which causes user inconvenience. We have implemented dynamic querying by integrating the chatbot with the SERP API which parses through the internet and generates answers in the form of snippets. This way we have ensured that the chatbot will answer cross domain questions and enhance user experience. Also, the implementation of Speech recognition which records users&#x0027; query as a voice input helps users to have a quality experience. Using these methods to implement a FAQ chatbot will increase the number of users who visit the website.