Reviewerly/exHarmony huggingface dataset:
A Big Data Analysis Algorithm Designed for the Interactive Platform of the Intelligent Sensor Information System

In order to improve the interactive sharing ability of the medical information system and improve the adaptability of the medical file access and management, a big data analysis algorithm designed for the intelligent sensor information system interaction platform is proposed. Using the information collection, statistics, and summary in the archives, adopt the statistical method analysis, optimize the medical archives intelligent sensor information adaptive scheduling and access algorithm, establish the medical information interactive sharing database, design the medical information system interactive sharing platform under the B/S structure system, query and update the database management of the sharing platform, and complete the software development of the medical archives intelligent sensor information embedded interactive sharing platform. Simulation results show the recall rate of 0.82 to 1, much higher than the other two comparison methods, with better data mining performance. Therefore, the intelligent sensor medical information system interaction platform based on big data analysis can improve the science and standardization of the medical business.
Design and Implementation of AI based Kisan Se Kisan Tak (KSKT) Mobile App

AI-based technology helps increases the efficiency in all fields of the agriculture sector such as harvest, irrigation, soil content sensing, monitoring, and agricultural robots. With a jumping global population, the agricultural sector faces the crisis, but the AI has the potential to provide a very necessary solution. The proposed Kisan Se Kisan Tak (KSKT) program includes a Chatbot that can answer farmers questions and provide accurate weather forecasts so that they can plan accordingly. Agriculture is predicted to generate major part of the revenue by 2050. Agricultural monitoring is so extensive that it necessitates decreasing humans in practice. The developed application begins with the weather forecasting app. The user may input their chosen location here, and the App will use the API to give them with all weather updates. The state of the app will be saved due to shared preferences. The KSKT application’s functionalities were surveyed and a feedback of over 50 people was taken. The research work for the application is to provide ease to farmers and workers based on demographic information using API’s, and it can automatically analyze the soil status i.e., humidity, pH values.
Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.
MK-RS-PKE: Multi-Keyword Ranked Searchable Public-Key Encryption for Cloud-Assisted Lightweight Platforms

Since more and more data from lightweight platforms like IoT devices or mobile apps are being outsourced to the cloud, the need to ensure privacy while retaining data usability is essential. This paper designs a framework where lightweight platforms like IoT devices can encrypt documents and generate document indexes using the public key before uploading the document to the cloud. An admin can search and retrieve the top-k most relevant documents that match a multi-keyword query using the private key. Most existing searchable encryption that supports IoT returns all the documents matching queried keywords. However, IoT devices can produce massive data, which is not practical for such schemes. We formally name our framework Multi-keyword Ranked Searchable Public-Key Encryption (MK-RS-PKE).
FPGA Implementation of High-Performance Montgomery Modular Multiplication with Adaptive Hold Logic

This paper provides an efficient Montgomery modular multiplication technique, such as high-performance Montgomery modular multiplier, which is the most important arithmetic functional unit. The throughput of this multiplier is critical to the overall performance of these digital multiplication systems, which is measured in bits per second. The suggested work in this study proposes a Montgomery modular multiplier architecture that incorporates a unique adaptive hold logic (AHL) circuit to achieve a high level of performance. Because by the variable latency, the multiplier can deliver increased throughput while also adjusting the AHL circuit to prevent performance decline caused by the aging aware effect. Therefore, this proposed multiplier was developed using Verilog HDL and Synthesized in a Xilinx FPGA, which reduced the number of clock cycles required for operand pre-computation and conversion of format. As a result, high throughput can be achieved by hiding the additional clock cycles required for operand pre-computation and conversion of format. According to the experimental findings, our suggested design with 32-bit multipliers may provide up to a significant performance boost when compared to current 32-bit Montgomery Multipliers in terms of speed and efficiency.
Graph Convolution Word Embedding and Attention for Text Classification∗

Text classification is an important and classic task of natural language processing. Deep neural networks are becoming more and more popular in text classification due to their expressive power and low requirements for feature engineering. However, the more flexible graph convolutional neural network is rarely used for this task. This paper proposes a text classification model (GENET) based on graph convolution word embedding and attention mechanism. The model can better combine the semantic and lexical information of the text with the discontinuous global word co-occurrence information and long-distance semantic information in the corpus. It breaks the shortcomings of traditional neural networks that have limited structure and can only learn local information. Experimental results show that our proposed text classification method out performs other models on multiple datasets. At the same time, it is proved through experiments that the word global information obtained by GCN is an important supplement to the word embedding representation.
Analyze Hate Contents on Sinhala Tweets using an Ensemble Method

As social media grew in popularity among the general public, content and opinion sharing has become rapid and convenient. The majority of users rely on social media for news and trust the content shared by the network. Some individuals, both purposefully and unintentionally, disseminate hate content and instill hatred in their readers. Even in Sri Lanka, the spread of hate propaganda on social media has resulted in communal discord and a variety of concerns. Only a limited number of research studies have been conducted to analyze the hate content written in Sinhala. This work investigates a mechanism for detecting hate content typed in Sinhala language and posted on Twitter. The proposed supervised mechanism is an ensemble method that selects the most accurate result from different models. 63% of accuracy, 58% of F1 Score, 61% of Precision and 58% of Recall were achieved when predicting hate content.
Multilayer perceptron models for the estimation of the attained level of education in the Italian Permanent Census

In the Italian Permanent Census, estimates of the attained level of education are derived by the integration of administrative data, 2011 census data, and sample survey data. The result of the integration procedure is the prediction of the attained level of education (ALE) for each single resident. Due to the complexity and heterogeneity of the available information, traditional statistical methods require the construction of different imputation models for different subpopulations, with a considerable effort in terms of human intervention. We study the use of a multilayer perceptron (MLP) model to make the process more automatic, i.e., less costly in terms of human resources, and possibly more accurate in terms of estimates. The MLP model is applied to Istat data referred to an Italian administrative region (Lombardia) in 2018, and the results are compared with those obtained using the official procedure. The study shows that the MLP approach is indeed less demanding in terms of human work needed for data preparation and modeling, yet it leads to estimates characterized by the same level of accuracy as the ones provided by the official procedure.
Finding Spatial-Textual Clusters in COVID Tweets

Finding Semantic similarity in text is a vital concept in the fields of information mining, text-based profiling. There have been many approaches to improve information retrieval by mining the semantics of the text. With the pandemic situation prevailing all over the world, we come across many useful posts about the COVID infection that is being tweeted by medical practitioners and people in the health care sector. While we come across such tweets, we also have tweets related to the vaccines, medical facilities, change in economic conditions due to pandemic, etc. But there is no methodology to efficiently study the tweet data and retrieve useful information out of them. Also, we need to utilize the geographical information that comes with each tweet. Though there have been many studies conducted on sentiment analysis, statistical analysis related to twitter data, there has not been much research on finding out the geographical distribution of COVID related tweets combined with query-based textual similarity of COVID related tweets. In this paper, we try to study the semantics of geo-tagged twitter data related to COVID and segregate the tweets based on their geographical location and according to the content of tweets. We use an improved version of Density-Based Spatial Clustering for clustering the tweets according to geo-spatial information. Then, we apply cosine similarity techniques to do the textural clustering and evaluate the performance of proposed model. The proposed model is able to cluster tweets using the spatial coordinates and classify the tweets based on the textual similarity measure.
Evolving action pre-selection parameters for MCTS in real-time strategy games

Real-Time Strategy (RTS) games are well-known for their substantially large combinatorial decision and state spaces, responsible for creating significant challenges for search and machine learning techniques. Exploiting domain knowledge to assist in navigating the expansive decision and state spaces could facilitate the emergence of competitive RTS game-playing agents. Usually, domain knowledge can take the form of expert traces or expert-authored scripts. A script encodes a strategy conceived by a human expert and can be used to steer a search algorithm, such as Monte Carlo Tree Search (MCTS), towards high-value states. However, a script is coarse by nature, meaning that it could be subject to exploitation and poor low-level tactical performance. We propose to perceive scripts as a collection of heuristics that can be parameterized and combined to form a wide array of strategies. The parameterized heuristics mold and filter the decision space in favor of a strategy expressed in terms of parameters. The proposed agent, ParaMCTS, implements several common heuristics and uses NaïveMCTS to search the downsized decision space; however, it requires a preceding manual parameterization step. A genetic algorithm is proposed for use in an optimization phase that aims to replace manual tuning and find an optimal set of parameters for use by EvoPMCTS, the evolutionary counterpart of ParaMCTS. Experimentation results using the μRTS testbed show that EvoPMCTS outperforms several state-of-the-art agents across multiple maps of distinct layouts.
Learning Environments for Augmented Reality in Early Childhood

Augmented Reality (AR) is a forthcoming innovation which expands PC produced advanced information over genuine world. The client can see and effectively connect with the virtual information progressively. There has been a great deal of headways in the field of AR from innovative work to accessibility of AR/Virtual Reality (VR) equipment gadgets to the clients. Planning and creating AR applications is a troublesome undertaking. It includes UI planning, content age as indicated by the interest group so the application is straightforward, intelligent, and reliable and simplicity to utilize. Age is a huge component to be considered in creating applications for youngsters particularly with regards to collaboration strategies to be utilized in learning applications. In this paper, an Augmented Reality Learning Environment (ARLE) for kindergarten kids was created by the distinguished convenience standards considering the current ease of use issues/challenges. The outcomes showed that the children truly appreciated utilizing the created ARLE. Likewise, they additionally observed it an exceptionally helpful instructing help that can be utilized alongside customary educating as it expanded the interest and excitement of the children while learning. The utilization would work on children's memory as it gave sound and visual guides which are not difficult to recollect and comprehend.
Facial Expression Recognition Using Convolution Neural Network Fusion and Texture Descriptors Representation

Facial expression recognition is an interesting research direction of pattern recognition and computer vision. It has been increasingly used in artificial intelligence, human–computer interaction and security monitoring. In recent years, Convolution Neural Network (CNN) as a deep learning technique and multiple classifier combination method has been applied to gain accurate results in classifying face expressions. In this paper, we propose a multimodal classification approach based on a local texture descriptor representation and a combination of CNN to recognize facial expression. Initially, in order to reduce the influence of redundant information, the preprocessing stage is performed using face detection, face image cropping and texture descriptors of Local Binary Pattern (LBP), Local Gradient Code (LGC), Local Directional Pattern (LDP) and Gradient Direction Pattern (GDP) calculation. Second, we construct a cascade CNN architecture using the multimodal data of each descriptor (CNN LBP , CNN LGC , CNN GDP and CNN LDP ) to extract facial features and classify emotions. Finally, we apply aggregation techniques (sum and product rule) for each modality to combine the four multimodal outputs and thus obtain the final decision of our system. Experimental results using CK[Formula: see text] and JAFFE database show that the proposed multimodal classification system achieves superior recognition performance compared to the existing studies with classification accuracy of 97, 93% and 94, 45%, respectively.
IoT based Novel Face Detection Scheme using Machine Learning Scheme

Face detection scheme is the most significant operation of the Digital Image Processing domain, in which it is supportive to many industries to resolve the security issues and the associated time savvy functionalities. At the moment, safety concerns about the Internet of Things (IoT) are widespread. Facial recognition in the presence of selective concealment has developed into a critical issue impacting social protection. This research proposes a unique paradigm for face recognition by using the Internet-of-Things security environment, in which it is utilized to identify certain criminal actions. Our developed framework incorporates color and contour information into a learning algorithm and its advantage in detecting faces with extreme occlusion has been proved to be persistent. These services are divided into three categories: initially to introduce a novel face identification approach depending on the efficiency feature; next, employ a Support Vector Machine (SVM) model to generate feature representations for occluded faces. Finally, an unique sparse classification framework with a deep learning approach is created to determine whether the observed face is covered. Statistical analysis demonstrates that this approach outperforms the advancements in terms of effectiveness and resilience. At a frame rate of ten frames-per-second, the created face recognition method achieves 98.5% accuracy despite the presence of various forms of extreme deformations in faces and the developed occlusion verification technique achieves 96.5% accuracy.
A cross-lingual sentence pair interaction feature capture model based on pseudo-corpus and multilingual embedding

Recently, the emergence of the digital language division and the availability of cross-lingual benchmarks make researches of cross-lingual texts more popular. However, the performance of existing methods based on mapping relation are not good enough, because sometimes the structures of language spaces are not isomorphic. Besides, polysemy makes the extraction of interaction features hard. For cross-lingual word embedding, a model named Cross-lingual Word Embedding Space Based on Pseudo Corpus (CWE-PC) is proposed to obtain cross-lingual and multilingual word embedding. For cross-lingual sentence pair interaction feature capture, a Cross-language Feature Capture Based on Similarity Matrix (CFC-SM) model is built to extract cross-lingual interaction features. ELMo pretrained model and multiple layer convolution are used to alleviate polysemy and extract interaction features. These models are evaluated on multiple language pairs and results show that they outperform the state-of-the-art cross-lingual word embedding methods.
Online measurement of fill level of disc pelletizers using RGB-D camera

Vision-based technique for fill level measurement in pelletization is widely used in industries and is crucial to ensure high-quality production. However, the existing approaches are 2D-vision-based and majorly designed for rotary drums. Nevertheless, for disc pelletizers, it remains an under-study and challenging problem due to the complex structure of apparatus and granules kinetics in disc. In this work, an online fill level measurement method for disc pelletizers by an RGB-D camera is proposed. Firstly, a background depth data subtraction and automatic granular area identification method are developed to obtain the 3D pointe cloud data of the granular area by machine learning and model-based processes; thereafter, a proportion conversion algorithm is developed to real-time calculate the granular pile volume and fill level. Extensive experiments demonstrate the effectiveness of our approach with an average relative error of 5.42%, which meets the requirements in industries.
Didactic Oriented Study: Move and Transitivity Analysis of Method, Result, and Discussion Sections in Research Articles

&lt;p style="text-align: justify;"&gt;This study expounds how the analysis of move and transitivity in the method, result, and discussion sections contributes to the advancement of novice writers’ writing skills by providing a writing pattern applied when composing research articles. To this aim, a qualitative approach with descriptive technique is employed to encapsulate and explain the phenomena being studied. The data are the method, result, and discussion sections of research articles (RAs) drawn from top tier journals categorized as language and linguistics and analyzed based on macro structure (move and steps) and micro structure (transitivity). The results indicate that 11 moves and 38 steps discovered, in detail, these consist of three moves and 15 steps in method, four moves and 10 steps in result, and four moves and 13 steps in discussion. As for the successions of the move steps, few inter-move step shifts observed. This is in contrast with the outer-move step shifts that commonly occurred. Meanwhile, the outcomes of the transitivity analysis suggest that there are six processes revealed including material, relational, verbal, mental, existential, and behavioral consecutively with material dominating and behavioral the fewest. The results of the present study may supplement the teaching materials reside in English for a research purpose. &lt;/p&gt;