Reviewerly/exHarmony huggingface dataset:
Analysis the performance Medical Pharma Information System Applications

Over the years, the Indian pharmaceutical business has been the most awaited around the world. It is the world's largest supplier of medical facilities and a leader in pharmaceutical manufacture. The Internet of Things offers the pharmaceutical business a variety of new individualised market prospects, a more controlled environment for pharma manufacturing, the ability to avoid equipment maintenance, and a more varied supply chain management system. Pharmaceutical manufacturing may be unable to provide benign and safe medicinal production and circulation due to transparency issues. Adoption of the internet of things could give pharma companies using pharma IoT a new competitive advantage. This is a conceptual exploratory investigation that shows the future of Indian pharmaceuticals, which is on the point of being self-sufficient. Customers wearing internet-connected digital devices and sensors implanted in wearable's could be tracked using the internet of things, artificial intelligence, big data analytics, and tracking of customers carrying internet-connected digital devices and sensors implanted in wear ables. This would also serve as a warning to both clinical makers and consumers, allowing for the development of tailored products and a brighter future for the Indian pharmaceutical industry. Future Pharma would transform production by employing better, more reliable vast volumes of data to link paraphernalia throughout development and distribution.
Comparative Analysis of Problem Representation Learning in Math Word Problem Solving

For developing a math word problem (MWP) solver, the problem text is usually modeled as a word sequence to put into a recursive neural network to capture the quantity relationships presented by the text. Recently, more and more researchers leverage graph-based models for problem representation learning and significant improvements are claimed to have achieved. To explore the potential effectiveness of presentation learning methods on diverse characteristics of benchmark datasets, a comparative analysis of problem representation learning is conducted in this paper. The framework of typical representation learning methods are studied and comparative experiments are implemented to reveal the performance variations in solving different types of math word problems. Experimental results show that, compared to sequence-based problem learning, there is no significant performance improvement after applying graphbased learning methods.
The Effects of Contextualized Learning Content and Collaborative Behaviours in a Ubiquitous Learning Environment

Research suggests the importance of providing learners with contextualized learning content that meets the demands of learning context in the ubiquitous learning (ulearning) environment. Similarly, the positive role of collaborative learning is recognized. However, it is not clear how collaboration may benefit learners if they are provided with contextualized learning content that meets their individual needs in u-learning activities. To bridge the gap, this study explored the cross effect of contextualized learning content and collaborative behaviours on students’ learning effect in a u-learning environment. Thirty-four first-year students at a vocational college in China participated in a sixweek comparison experiment and were interviewed in focus groups. The study found that, regardless of whether contextualized learning content was provided, learners tended to collaborate by cutting the task apart in the ubiquitous environment; contextualized learning content had a positive impact on learners’ learning effect; collaboration by cutting the task apart did not benefit the learning effect; the cross effect of this collaboration and contextualized learning content on the learning effect was not significant. Implications for promoting effective u-learning in terms of learning content and collaboration are proposed.
Automatic Recognition of Speech Acts in Classroom Interaction Based on Multi-Text Classification

The traditional coding process requires mechanical observation and categorization of the various utterances produced in the classroom. Both the judgment and the professionalism of education of the coders are very challenging. With the development of Automatic Speech Recognition (ASR) and natural language processing (NLP). It is possible for researchers to automate the recognition of speech acts in the classroom. There are also many related studies, but they have not been able to complete the automatic recognition of the classroom interaction speech act(CISA). In order to solve problems, our research proposes a practical CISA coding system. And according to this system, a related CISA dataset is established. A Multi-text classification(MTC) model called Bert-TextConcat is proposed for training on the constructed dataset. The trained model performs automatic classification of CISA while referring to the above. After experiments, We demonstrate the effectiveness of the BertTextConcat model and CISA coding systems.
Smart Contract Vulnerability Detection for Educational Blockchain Based on Graph Neural Networks

With the development of blockchain technology, more and more attention has been paid to the intersection of blockchain and education, and various educational evaluation systems and E-learning systems are developed based on blockchain technology. Among them, Ethereum smart contract is favored by developers for its “event-triggered” mechanism for building education intelligent trading systems and intelligent learning platforms. However, due to the immutability of blockchain, published smart contracts cannot be modified, so problematic contracts cannot be fixed by modifying the code in the educational blockchain. In recent years, security incidents due to smart contract vulnerabilities have caused huge property losses, so the detection of smart contract vulnerabilities in educational blockchain has become a great challenge. To solve this problem, this paper proposes a graph neural network (GNN) based vulnerability detection for smart contracts in educational blockchains. Firstly, the bytecodes are decompiled to get the opcode. Secondly, the basic blocks are divided, and the edges between the basic blocks according to the opcode execution logic are added. Then, the control flow graphs (CFG) are built. Finally, we designed a GNN-based model for vulnerability detection. The experimental results show that the proposed method is effective for the vulnerability detection of smart contracts. Compared with the traditional approaches, it can get good results with fewer layers of the GCN model, which shows that the contract bytecode and GCN model are efficient in vulnerability detection.
A BERT-Based Pre-Training Model for Solving Math Application Problems

Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms.
The Development and Application of Augmented Reality Educational Game in English Learning

This study examines how augmented reality (AR) technology can be integrated into educational games to help students learn English. An AR game system was designed and developed for helping students memorize the English vocabulary by providing interesting pictures and impressive surroundings. The system framework, as well as the design and development process of the game were presented in this paper. A case study of the application and evaluation of the game was implemented. In detail, a questionnaire was designed according to the general evaluation methodology of educational games. In this study, the questions of the questionnaire were classified into five aspects: game information, multimedia, interface design, content and feedback. The evaluation results of the game show that the students can memorize the English vocabulary while playing this AR educational game happily. Obviously, the game could increase the learning interests of the students with the aid of the AR technology.
A Novel Geometry Problem Understanding Method based on Uniform Vectorized Syntax-Semantics Model

The first step in solving geometry problems is to understand problems, and automatic understanding of geometry problems by computers has always been a challenge due to the massive advanced knowledge implied in the text and diagram. This paper proposes a method for geometry problem understanding based on vectorized Syntax-Semantics (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) model. The proposed method divides the understanding of geometry problems into three parts. Firstly, we modified and optimized vectorized S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> model for understanding explicit arithmetic word problems, and applied it to the text understanding of geometry problems to extract basic geometric relations. Then, based on the idea that a diagram is an extension of problem text, we designed vectorized S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> model of diagram understanding according to the same framework as that of text understanding. All geometry diagrams are transformed into vectors for understanding in a uniform way. Finally, we designed a derived relations generation model based on the diagramet theory to extract derived geometric relations from the basic relations. Experimental results show that the proposed method is effective in understanding geometry problems with diagrams.
Sensor based Attendance System Using Feature Detection and Matching with Augmented Reality

The real world environment is interactively experienced with Augmented Reality (AR)where the objects exist in the real world are superimposed by computer-generated perceptual information. A Touch less Attendance system is implemented using AR, Feature detection and matching algorithms to avoid any physical contact. An AR Application is used to get input from the user and to compare the current data with the previous data in the database. The input is basically a palm print of a person. This image captured at present is compared with the image which is stored in the database using matching techniques. The image captured and the image present in the Database is sent to the algorithm for comparison through an API. The database consists of employee id and palm print of the employee respectively. The employee id got as an input from AR application is used to fetch the palm print of the particular employee and it is further used for comparison. The matching techniques such as SIFT, SURF, ORB, FAST and BRIEF will be used based on the efficiency accordingly. These feature detection and matching algorithms are converted into API and it is called from the AR app to do the further process. Both the images are given as an input to the API where it performs comparison and tells whether the user is valid or not with respect to the employee id.
Voice Based Motile Video Surveillance System with ViolenceDetection

The consequences of lack of surveillance are horrendous, thus the need for surveillance is at its high spot. Monitoring and surveillance systems could not be extensively used as complexity exists in fitting CCTV surveillance camera at some locus and the prevailing surveillance systems requisites human assistance for recognizing illicit activities, consequently the crime rate goes on increasing. In order to wane the crime rate, the introduced system integrated with camera and the global system for mobile communications (GSM), rove around the environment discerning the illicit activities by implementing the computer vision technology and provides warning signal over a SMS. The system comprises of a transmitter and a receiver module, the data transmission between these modules is governed by LoRa. The proposed system is also implanted with PIR sensor and the global positioning system (GPS).
Solving Word Function Problems in Line with Educational Cognition Way

Intelligent solutions are an important research field in artificial intelligence education, automatic reasoning and solutions to function problems are a key technology of intelligent tutoring services, which has become a challenging research hotspot in the field of artificial intelligence. This paper aims to provide explanatory solution guidance for intelligent tutor, and proposes a functional problem solving model based on educational cognition and applies it. Specifically, the algorithm model simulates human solution cognition, decomposes the solution process into several progressive combinations of solution states, and designs independent methods to solve subtasks. In each subtask, the cognitive solution is visualized through the mode of cognitive state guiding the closing action to reflect the transformation process between the solution steps. In order to verify the proposed solution model and apply it to the function problem solution, the effectiveness of the new solution model is proved by case analysis and experimental results.
Raspberry PI Based Automated Authentication and Monitoring System for The Examination Centers

The conduct of centralized examinations for many job postings and academic institutions demands an easy navigation and strong monitoring system. Malpractice is often referred as pretense of the person taking up the examination causing the act of deceit. Hall ticket are used for authentication along with the identity cards. The prevailing system of conduct of examination has the verification of the image manually in the process of authenticating the candidate. Malpractice in the examinations corrupts the quality of the entire education system. This work attempts to automate the face identification for identifying if any sort of deceit is being done by the candidate in venue of examination. It also detects the motion of the candidate while attempting to unauthorized work in the examination hall. The proposed work has three folds of objectives. Firstly it provides the easy navigation details for the candidates appearing for the examinations at common centers using the face recognition with Haar cascade algorithm. Secondly it provides the required navigation details only to the authenticated candidates. Third it provides the tight monitoring during the conduct of examination by detecting the motion of the candidates.
Research on Intelligent Scoring Method of Standardized Chinese Character Writing

Under the current educational background of “double subtraction in order to effectively improve students’ ability to write standardized Chinese characters and solve the problem of shortage of teachers for writing education, an intelligent scoring method for standardized Chinese character writing based on template Chinese character eigenvalue similarity is proposed. The method consists of three steps: firstly, a Chinese character evaluation classification model is established based on the eigenvalue information of handwritten Chinese character samples and expert pre-evaluation results, and the standard interval of Chinese character eigenvalue is determined based on the classification results of the model. Secondly, a multiple linear regression model is established based on the scores of individual writing rules of Chinese characters and the overall scores of experts on handwritten Chinese character samples. Through the model, the influence weight of each writing rule on the evaluation of whole character writing is determined. Thirdly, combining the similarity of eigenvalues, the difference of eigenvalues and the influence weight ofwriting rules, we can score the handwritten Chinese characters, include overall score and quantifiable details.
Machine Learning and Wireless Sensor Network based Library Automation System

Remote sensor network and radio frequency identification innovation are on the world's outskirts. In this age of data innovation, the library as a repository for information gathering and distribution is critical. Innovations such as the personal computer, the Internet, and universal identification have sped the shift to the digital age. New improvements were integrated to configure Radio Frequency Identification remote organization with Wireless sensor network comprising hubs to work on the intelligent administration of the library, relying on manual and data the board. The three modules that were basically researched were writing seek positioning framework, book full casing framework, and programmed getting and returning entry monitor warning framework. The intelligent library framework based on Wireless sensor organization and Radio Frequency Identification incorporated sophisticated RFID and ZigBee technologies for the simplification and inclusion of writing assets, administration interface, and the library's administrative procedure. The significance of library computerization and smart collaboration was recognized, and the notion of individuals situated was fully explored. According to the findings, the smart library framework, which is based on wireless sensor organization and radio frequency identification, was able to fundamentally understand the smart library executives. To summarize, the framework creates an unpredictable library administration and board framework at any time and in any location.
Automatic Object And Crack Detecting System Using IoT

The world is dependent on transportation and without transportation goods may not reach to the destination or people cannot travel from one place to another. So Transportation plays a major role in every aspect. There are different modes of transportation and the cheapest and most efficient mode of transportation is Railways. Railway trains not only used for carrying persons from one place to another but at the same time it carries the required goods to be delivered to a particular location, so it serves as multi-purpose vehicle. If this Railway trains which is used for multipurpose activities suddenly faces major issues like accidents, will be a huge impact on all sectors. In India, Train accidents posses a major challenge and it not only results in loss of lives but also indirectly makes a huge impact in economy. Train accidents not only occur due to a crack in Railway track But there are some other reasons like if we see in forest areas elephants usually crosses tracks to reach other side of forest but this leads to loss of lives of elephants and it affects our biodiversity which results in elephants life in endangered category To avoid that or to prevent accidents Occurring, this project proposes an idea of implementing an early warning system or an alert system by using software and hardware technologies like Arduino UNO integrated with IoT and GPS technology to intimate both the loco pilot and the track men about the incident by providing them the required satellite coordinates, and if suppose an objects like animals is on track it provides a buzzer sound which automatically makes the animal to move so that accidents can be prevented by moving the objects and if a crack is detected intimates the loco pilot and the track men simultaneously.
IoT - Traffic Video Surveillance System for Weapon Detection

In the previous few years, multimedia traffic has significantly increased. The Internet of Things (IoT) introduces new traffic and applications. SDNs improve network administration. When used in conjunction with SDN, artificial intelligence (AI) can offer solutions to network issues based on approaches for classification and estimation. In this study, we investigate how to enable delay-sensitive applications in IoT contexts by integrating deep learning techniques with Software-Defined Network (SDN) architecture. As a case study, real-time video surveillance applications serve as the basis for the construction and testing of a large number of deep learning-based models for weapon detection. To aid in the early detection and prevention of various crimes, as well as to reduce the bandwidth requirements by offloading the communication network from massive traffic transmission using the fuzzy technique, The best deep learning model is utilized by a proposed artificial intelligence model at the edge that is located in order to extract the first discovered video frames that contain weapons in order to ensure prompt transmission of this information to law enforcement. In other words, the best deep learning model is used to extract the first discovered video frames containing weapons for prompt transmission to law enforcement. Results indicate that the network may minimize jitter by up to 80% on average and losses by about 4% by taking certain actions.
Combining Coverage with TMPS for Reviewer Assignment

A fundamental aspect of peer review is the as-signment of reviewers. With the help of artificial intelligence, assigning reviewers can save time and effort and even achieve better results. The purpose of this paper is to explore how to assign reviewers to a paper based on matching multiple aspects of expertise. So that the assigned reviewer group covers all the aspects of a paper in a complementary manner, rather than covering the expertise only in the major research field of a paper. We extract research domain sets of the papers by prompt tuning. And calculate the research domain coverage score and TMPS score based on the review candidates and the pending papers. Then, we utilize a greedy round algorithm to establish the assigned reviewer groups for each paper. Finally, the reviewer groups will undergo a discrete check for conflicts of interest to validate the ultimate results. Experiments demonstrate that the proposed method considers the coverage of the research domain adequately. Furthermore, it arranges a proper selection order of reviewers for papers.
Design and Simulation of Autonomous Water Tank Cleaning robot in Gazebo

Designed to address the drawbacks of traditional water storage tank cleaning procedures, a new robot system is described in this paper for performing cleaning work instead of manual cleaning which is characteristic of low efficiency, low safety, long time, and water pollution problems. In this paper, we have described a robot system that can clean both the floor and walls of the water tank. The robot is designed in Fusion 360, and it is simulated in Gazebo by creating an environment of a water tank. For autonomous functionality, programs written in Robot Operating System (ROS) are employed.