Reviewerly/exHarmony huggingface dataset:
Research on Chinese Medical Named Entity Recognition Based on ALBERT and IDCNN

BERT (Bidirectional Encoder Representations from Transformers) as a pre-training model has been widely used in the field of natural language processing, of course, it also covers the field of Chinese medical text.In the process of actually dealing with Chinese tasks, BERT also has its own shortcomings, including the lack of Chinese word segmentation.This is because BERT is segmented based on the granularity of words.In addition, the amount of pre-training parameters of the BERT model is too large, which will also cause some problem of poor model performance caused by excessive computing power requirements, long training time, and excessive parameters.To solve the above problems, this paper proposes a Chinese medical named entity recognition model based on ALBERT and IDCNN.Experiments show that the ALBERT-IDCNN-CRF model constructed in this paper has a good performance on the Chinese electronic medical record named entity recognition task, and effectively solves the problems of polysemy and word recognition completion in Chinese electronic medical record named entity recognition.On the CCKS 2017 dataset the model effect F1 value reached 94.51%, and on the CCKS 2019 dataset, the model effect F1 value reached 88.61%.
The Application of Modern Portfolio Theory in US Stock Market with the Use of Python Programming

The purpose of our study is to investigate the application of Modern portfolio Theory in the stock markets and design an investment strategy for riskaverse investors.The principle of the Modern portfolio theory is mean-variance analysis, that is we use mean and variance to measure the expected return and risk of portfolios respectively, and select an optimal portfolio based on it.The core idea is diversification of investments, which means to divide the investment into a combination of different financial assets or shares of different companies, such as stocks and securities.We investigate the adjusted closing stock price of five companies: Facebook, Amazon, Apple, Netflix and Google, from 2011 to 2021.Our results show that the minimum risk (annualized volatility at 23%) can be obtained when the expected annualized return is at level of 28%.A higher expected annualized return (at the level of 33%) might be achieved if the investors are willing to take more risk (annualized volatility at 25%).
CLOUD-BASED WEB PORTAL FOR COACHING ACADEMY

A coaching class web-portal system is a complete solution for managing an administration.In other words, an enhanced tool that assists in organizing basic academic activities of the coaching institutes.The use of the webbased system has become popular in recent years.The web-based system represents an excellent example of representing a business & managing the online transactional process to get better achievement of the working process.Coaching Class System is one of the systems that are going to be developed using the web-based system.It offers a faster process and reduces the cost and increases sales.The cost includes manpower use, facilities, and time issue.By using the computerized management system, there is much more flexibility in handling the data.The coaching class web portal is developed after collecting requirements from many organizations in various fields.The application is very easy to use.A website guarantees to save money and time for the owners besides organizing the information of the institution and growing the business by representing the academy in the digital world through a website which also helps in digital marketing.In the current system, we have to physically visit the academy location to inquire about different batches and do the manual process for admission and we have to store all the records manually which requires huge manpower and record storage space.Due to manual maintenance, many mistakes are made so the existing system needs to be replaced with this web application."Web-Portal for coaching academy" is a product to automate various academy activities and provide instant information required for the effective management of any coaching class.The software's admin model allows the administrator to dynamically manage all of the website's data as well as all student, faculty, and course-related data.so that the administrator can oversee the entire academy.The visitor module will serve as a representation of the digital image of the academy, and the academy and student modules will be useful for effective cooperation between them.
Efficient Path and Action Planning Method for Multi-Agent Pickup and Delivery Tasks under Environmental Constraints

Abstract We propose a method called path and action planning with orientation (PAPO) that efficiently generates collision-free paths to satisfy environmental constraints, such as restricted path width and node size, for the multi-agent pickup and delivery in non-uniform environment (N-MAPD) problem. The MAPD problem, wherein multiple agents repeatedly pick up and carry materials without collisions, has attracted considerable attention; however, conventional MAPD algorithms assume a specially designed environment and thus use simple, uniform models with few environmental constraints. Such conventional algorithms cannot be applied to realistic applications where agents need to move in more complex and restricted environments. For example, the actions and orientations of agents are strictly restricted by the sizes of agents and carrying materials and the width of the passages at a construction site and a disaster area. In our N-MAPD formulation, which is an extension of the MAPD problem to apply to non-uniform environments with constraints, PAPO considers not only the path to the destination but also the agents’ direction, orientation, and timing of rotation. It is costly to consider all these factors, especially when the number of nodes is large. Our method can efficiently generate acceptable plans by exploring the search space via path planning, action planning, and conflict resolution in a phased manner. We experimentally evaluated the performance of PAPO by comparing it with our previous method, which is the preliminary version of PAPO, the baseline method in a centralized approach, and fundamental meta-heuristic algorithms. Finally, we demonstrate that PAPO can efficiently generate sub-optimal paths for N-MAPD instances.
A three-tiered semi supervised MTL mechanism and its application in dating apps

Abstract A thorough understanding of the purpose of dating applications is crucial for service providers in order to optimize the design and user experience of the application. Despite the fact that many APPs prompt users to provide their usage purpose, many do not reveal this attribute. In this study, a three-module framework with semi-supervised and multitask learning mechanisms is proposed (T-SSMTL). Using the T-SSMTL mechanism, the purpose of the dating APP usage can be automatically inferred from the publicly available heterogeneous data of the user. The heterogeneous feature extraction module employs a number of techniques to extract semantic representations, maximizing the use of heterogeneous dating APP data. The multi-task module extracts task-specific knowledge for learning and solves the classification problem involving multiple labels. To alleviate the problem of label insufficiency, the semi-supervised module utilizes a large quantity of unlabeled data generated by users who do not report their usage purpose. A large-scale dataset containing 34,364 active dating APP users with their self-reported usage purpose, portrait image, profile, and posts was collected to evaluate the T-SSMTL framework. In the context of this dataset, simulation experiments have confirmed the efficacy of all three modules of the T-SSMTL framework, demonstrating its substantial theoretical significance as well as its excellent application value.
INVESTIGATING DISTANCE-DEPENDENT DISTORTION IN MULTIMEDIA PHOTOGRAMMETRY FOR FLAT REFRACTIVE INTERFACES

Abstract. Multimedia camera calibration is a challenging, yet increasingly important task. Due to the lack of software with appropriate models, implicit calibrations with standard single-media models are often performed, despite the well-known fact that distance-dependent errors arise from neglecting the specific multimedia influences. This becomes particularly relevant when test field calibrations are employed. We present a thorough simulation approach that shows the influence of distance-dependent errors when performing implicit calibration. Results show major accuracy decreases for performing measurements far from the calibration distance while closer distances may compensate the errors quite well. Furthermore, we investigate on the behavior of different test field geometries and angles between interface and optical axis which particularly affects the achievable accuracy.
Features and Supervised Machine Learning-Based Method for Singleton Design Pattern Variants Detection

Abstract Design patterns codify standard solutions to common problems in software design and architecture. Given their importance in improving software quality and facilitating code reuse, many types of research are proposed on their automatic detection. In this paper, we focus on singleton pattern recovery by proposing a method that can identify orthodox implementations and non-standard variants. The recovery process is based on specific data created using a set of relevant features. These features are specific information defining each variant which is extracted from the java program by structural and semantic analysis. We are based on the singleton analysis and different proposed features presented by Nacef et al. in [1] to create structured data. This data contains a combination of feature values defining each singleton variant to train a supervised Machine Learning (ML) algorithm. The goal is not limited to detecting the singleton pattern but also the specification of the implemented variant as so as the incorrect structure that is incoherent with the pattern intent. We use different ML algorithms to create the Singleton Detector ( SD ) to compare their performance. The empirical results demonstrate that our method for automatically training the ( SD ) based on features and supervised ML, can identify any singleton implementation with the specific variant’s name achieving 99% of precision, and recall. We have compared the proposed approach to similar studies namely DPDf and GEML . The results show that the SD outperforms the state-of-the-art approaches by more than 20% on evaluated data constructed from different repositories; PMART , DPB and DPDf corpus in terms of precision.
Handwritten Character Recognisation Using Machine Learning

This electronic document is a "live" template and already define the components of your paper [title, text, heads, etc.] in its style sheet.For people, it is common and easy to recognize a character, but it is challenging to build a machine that can do the same.One of those abilities that some individuals just naturally have is the ability to recognize characters.Computers and other contemporary technology fall short of human brain power.The three types of algorithms used in character recognition are feature extraction, classification, and image pre-processing.They are frequently employed because feature extraction is easier with image pre-processing, and good classification depends on feature extraction.identifying handwritten letters and the English alphabet from A to Z. Making a neural network that needs to be trained on a dataset of letter images will be used to do this.First the input image is converted into a gray-scale image and normalized in such a way that it represents the same resolution (28 x 28) as that of EMNIST dataset.CNN is trained using EMNIST dataset and use it as a classifier for better results.The feature vectors are extracted from the input image and provided to the trained model Convolution Neural Network is a type of neural network that recognizes and produces the required output.
Assessment Strategy to Rectify EFL Students’ Performance: A Need Analysis

The objective of this research is to investigates the assessment strategies that required to be implemented, and are favorable for the learners in order to rectify EFL students’ performance regards to their language performance. This study conducted based on the presumption that teachers merely consider students’ preference on how they want to be assessed. A need analysis is administered to meet the objective of the research. The result indicates that learners mostly prefer to have corrective feedback from the teacher during the teaching and learning activities, meanwhile performance task and quiz assessment are preferred by learners as their assessment strategies instead of summative assessment. Nevertheless in spite of that, KWL assessment strategy is also recommended for the teaching and learning of English.
Students’ Understanding on Teacher’s Speech Acts in the Classroom Instructions

This study analyzes the teacher's speech acts in the classroom. This study covers two points of discussion: speech acts in the teacher’s instructions and the students’ understanding to the instructions as speech acts. To describe two problems of this study, a qualitative descriptive research was applied in which the transcription of the conversation between the teacher and students can describe how the speech acts uttered by the teacher can be understood by the students. The results of this study indicated that explicit and implicit speech acts that were frequently uttered by the teacher in giving instructions in the learning process in which those instructions can be found in opening, core, and closing. Explicit and implicit speech acts can be categorized as directive form, namely command and/or request. Those utterances are used by the teacher to tell students to do something. Besides that, the students could understand the teacher's instructions in the classroom in various ways such as taking action, and giving statements, but there were some instructions that were not given a response by students.
CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates

Abstract Frequent itemset mining (FIM) is the crucial task in mining association rules that finds all frequent k-itemsets in the transaction dataset from which all association rules are extracted. In the big-data era, the datasets are huge and rapidly expanding, so adding new transactions as time advances results in periodic changes in correlations and frequent itemsets present in the dataset. Re-mining the updated dataset is impractical and costly. This problem is solved via incremental frequent itemset mining. Numerous researchers view the new transactions as a distinct dataset (partition) that may be mined to obtain all of its frequent item sets. The extracted local frequent itemsets are then combined to create a collection of global candidates, where it is possible to estimate the support count of the combined candidates to avoid re-scanning the dataset. However, these works are hampered by the growth of a huge number of candidates, and the support count estimation is still imprecise. In this paper, the Closed Candidates-based Incremental Frequent Itemset Mining approach, or CC-IFIM, has been proposed to decrease candidate generation and improve the accuracy of the global frequent itemsets that are retrieved. The proposed approach is able to prune several produced candidates in earlier steps before performing any further computations. To improve the accuracy of the computation of the support count of the produced candidates, the similarity between partitions has been evaluated using just the local closed candidates rather than all candidates. The experimental findings demonstrated that the CC-IFIM approach is superior to its competitors in terms of efficiency and accuracy.
The Usefulness of Augmented Reality in Enhancing English as a Second Language (ESL) Learners’ Vocabulary Acquisition: A Systematic Review (2018-2022)

Despite technological advancements, some educators continue to use traditional ways to teach vocabulary to youngsters.As a result, pupils feel unmotivated and struggle to achieve vocabulary mastery.This is well beyond time for instructors to adapt their instructional techniques and raise knowledge of technology-based ways to boost students' vocabulary acquisition.Consequently, this research aims to present a comprehensive assessment of previous research on the usefulness of Augmented Reality (AR) in enhancing the vocabulary acquisition of primary English as a Second Language (ESL) learners.A total of ten publications from various credible databases published between 2018 and 2022 were selected and reviewed in correlation with the inclusion criteria.The findings of review portrayed AR as a potential mean of improvising young learners' vocabulary learning.Overall, this review furnishes readers with the benefits of incorporating AR in the instructional practice of vocabulary to ESL learners.
International Capital Flows, Dynamic Changes in Cryptocurrency and Noble Metal Markets

In 2022, with the implementation of tightening monetary policies by FOMC, US dollar is experiencing a dramatic appreciation in a very short period. Though numerous studies have demonstrated the connection between the traditional currency market, cryptocurrency market, and precious metal market, rare studies are exploring the relationships between the three markets under a special political environment. This paper selects USDCNY exchange rate, gold and silver, and bitcoin as the representatives of three markets and then tests the volatility response of return on gold &amp; silver and return on bitcoin to the change of return on USDCNY exchange rate. By employing impulse response function and ARMA-GARCHX model, the paper verifies the change of exchange rate will exacerbate the volatility of returns on gold &amp; silver and bitcoin significantly, which suggests high risk and uncertainty of the cryptocurrency market and precious metal market in a complex and extreme political environment. Investors and speculators should take prudent investment strategies in such environment.
Information Technology Personnels’ Understanding, Knowledge, and Attitude towards Modern Application Development

The objective of this research is to investigate Information Technology Personnels' understanding/confidence, knowledge and attitude of Modern Application Development.A mixed method approach is chosen for their strength in acquiring objective and subjective findings.For the questionnaire, a descriptive interpretive analysis is used while for the qualitative data, a thematic analysis is carried out.The findings show that almost all the participants cited that they could see the improvement in their understanding, knowledge and attitude to the training topic.It is concluded that ,modern application development training attributed positively to the skills development of the participants.Thus, the recommendation is that further training following Bloom's Taxonomy be carried out for the participants to strengthen their core skill which is then applied at the workplace.
Penerapan Algoritma Welch-Powell Pada Penyusunan Jadwal Perkuliahan di Program Studi Pendidikan Matematika

Graph theory can be applied in course scheduling. The problem discussed in this study is scheduling courses in the UNUGIRI Mathematics Education Study Program using graph coloring. The purpose of this study is to prevent the occurrence of overlap (cross-over) in the preparation of courses. Good scheduling is a schedule that can be carried out by all related parties in teaching and learning activities. The algorithm used in this study is the Welch-Powell algorithm. The research was conducted using an axiomatic deductive method by deriving existing axioms or theorems, then applied to vertex coloring on the graph representation of the lecture scheduling of the UNUGIRI mathematics education study program. The readiness matrix to teach odd-semester courses is obtained based on the Welch-Powell algorithm. 2022/2023 with six different colors. The six colors represent the scheduling of 26 UNUGIRI Mathematics Education Study Program courses into six days without any cross-over.
Subjects of counteraction to the Internet influence on juvenile delinquency

The relevance of the study is determined by the fact that negative processes in any sphere of public life most painfully affect minors, requiring special attention in the direction of eliminating destructive phenomena. The situation is aggravated by general globalization, the development of the digital environment, the growth of mobile communications, the escalation of organized crime beyond national boundaries, etc. of the research is determined by the fact that negative processes in any sphere of public life most painfully affect minors, requiring special attention in the direction of eliminating destructive phenomena.&#x0D; The situation is aggravated by general globalization, the development of the digital environment, the growth of mobile communications, the escalation of organized crime beyond national boundaries, etc.&#x0D; Under the current conditions, countering juvenile delinquency mediated by the destructive influence of the Internet, in which special powers are defined for internal affairs bodies, is of particular importance.&#x0D; The purpose of the article is to reflect the features and problems of legal regulation of the powers of the internal affairs bodies in the field of combating crime associated with the destructive impact on the personality of a minor of the global information network.&#x0D; The object of research is aimed at studying of measures to combat juvenile delinquency, mediated by the destructive influence of the Internet.&#x0D; Problems of legal regulation and ways to overcome them are considered as a subject,&#x0D; Research methods: analysis, modeling, generalization, classification, analogy.&#x0D; The article reflects issues related to the powers of the internal affairs bodies to combat juvenile delinquency, mediated by the negative influence of the network, the system of counteraction subjects with which the internal affairs bodies interact.&#x0D; Novelty: due to the dynamism of the impact of the Internet on the personality of a minor, in combination with circumstances considered as criminogenic, some adjustments are proposed in the work to combat crime produced by such influence and implemented by the internal affairs bodies. It is proposed to include in the Instruction on the organization of the activities of juvenile divisions of the internal affairs bodies of the Russian Federation, approved by order of the Ministry of Internal Affairs of Russia dated October 15, 2013 No. 845, a new section «Organization of work on the prevention of juvenile crimes caused by the influence of the information and telecommunication network «Internet»» with an indication of the main directions and forms of prevention, allowing to expand the content of the juvenile divisions activities of the internal affairs bodies of the Russian Federation.