Reviewerly/exHarmony huggingface dataset:
Aspect-based sentiment analysis of online peer reviews and prediction of paper acceptance results

Peer reviews of academic articles contain reviewers' overall impressions and specific comments on the contributed articles, which have a lot of sentimental information.By exploring the fine-grained sentiments in peer reviews, we can discover critical aspects of interest to the reviewers.The results can also assist editors and chairmen in making final decisions.However, current research on the aspects of peer reviews is coarse-grained, and mostly focuses on the overall evaluation of the review objects.Therefore, this paper constructs a multi-level fine-grained aspect set of peer reviews for further study.First, this paper uses the multi-level aspect extraction method to extract the aspects from peer reviews of ICLR conference papers.Comparative experiments confirm the validity of the method.Secondly, various Deep Learning models are used to classify aspects' sentiments automatically, with LCFS-BERT performing best.By calculating the correlation between sentimental scores of the review aspects and the acceptance result of papers, we can find the important aspects affecting acceptance.Finally, this paper predicts acceptance results of papers (accepted/rejected) according to the peer reviews.The optimal acceptance prediction model is XGboost, achieving a Macro_F1 score of 87.43%.
Quantity Processing in Object Counting Task Based on Event-Related Potentials

In the preliminary event-dependent potentials study, our team found that the neural mechanism of Chinese singular and plural picture naming is different, and explained that the differences might lie in the different numbers of quantitative processing.However, previous studies have lacked separate neural processing of objects in singular-plural pictures.This study intends to explore the neural differences in quantity processing in singular and plural pictures by using an object counting task and combining Event-related potential (ERP).It is expected to prove that the neural differences of naming Chinese single and plural pictures found in our previous ERP research are neural processing differences of different quantities, further demonstrating that the quantity processing in Chinese singular and plural picture naming is automatic.The experimental results showed no significant differences in object counting accuracy (P=0.139) and reaction time (P=0.231) between the singular and plural groups.However, there was a noticeable difference in ERPs between the two groups (P<0.05) and statistical analysis showed that the P1 effect of the parieto-occipital lobe was greater in plural pictures than in singular pictures (140-180ms) and the P2 effect of the parieto-occipital lobe was more significant in singular pictures than in plural pictures (200-320ms).There are differences in the quantity processing of healthy adults in object counting tasks.These differences are similar to the neural differences in Chinese singular-plural picture naming, indicating that the quantity is automatically processed in this process.
Taxonomy of AR to Visualize Laparoscopy During Abdominal Surgery

The augmented reality (AR) is the latest technology in laparoscopy and minimally invasive surgery (MIS). This technology decreases post-operative pain, recovery time, difficulty rate, and infections. The main limitations of AR systems are system accuracy, the depth perception of organs, and real-time laparoscopy view. The aim of this work is to define the required components to implement an efficient AR visualization system. This work introduces Data, Visualization techniques, and View (DVV) classification system. The components of DVV should be considered and used as validation criteria for introducing any AR visualization system into the operating room. Well-designed DVV system can help the end user and surgeons with a clear view of anatomical details during abdominal surgery. This study validates the DVV taxonomy and considers system comparison, completeness, and acceptance as the primary criteria upon which the proposed DVV classification is based upon. This work also introduces a framework in which AR systems can be discussed, analyzed, validated and evaluated. State_of_the_art solutions are classified, evaluated, validated, and verified to describe how they worked in the domain of AR visualizing laparoscopy during abdominal surgery in the operating room. Finally, this paper states how the proposed system improves the system limitations.
A collaborative filtering recommendation algorithm based on DeepWalk and self-attention

Graph embedding is one of the vital technologies in solving the problem of information overload in recommendation systems. It can simplify the vector representations of items and accelerates the calculation process. Unfortunately, the recommendation system using graph embedding technology does not consider the deep relationships between items when it learns embedding vectors. In order to solve this problem, we propose a collaborative filtering recommendation algorithm based on DeepWalk and self-attention in this paper. This algorithm can enhance the accuracy in measuring the similarity between items and obtain more accurate embedding vectors. Chronological order and mutual information are used to construct a weighted directed relationship graph. Self-attention and DeepWalk are utilised to generate embedding vectors. Then item-based collaborative filtering is utilised to obtain recommended lists. The result of the relative experiments and evaluations on three public datasets shows that our algorithm is better than the existing ones.
INVENTORY DATA MINING SYSTEM USING FEDERATED MACHINE LEARNING STRATEGY

Inventory management is a particularly important process for keeping track of levels, orders, and transactions in the inventory retailing industry.A significant quantity of data about the stocked items in inventory is generated and gathered daily in the current retailing market.It is often a job to manage stocked goods effectively due to the growing volume of transaction data and their related relationships, but it must be conducted, especially when these inventory databases are scattered in various places.To collect all the inventory data from different distributed databases, it is time consuming, insecure, and inaccurate to implement intelligent management systems; it is essential to investigate the underlying dependencies of the body inventory items.However, current inventory management systems only have a limited ability for intelligent management because they depend on the statistical analysis of historical inventory data.There has not been much progress made in putting in place intelligent inventory management systems to use combined data-driven analysis to uncover hidden relationships.In this paper, we use distributed computing resources to process an enormous amount of inventory data and integrate federated learning, one of the most recent developments in data mining techniques.Federated Learning offers thorough aid for conducting various inventory management duties, including showing anomalous items and examining the aging of the inventory.
A citation iteration method for publications and scientists evaluation

In the last decades many methods have been developed for the evaluation of the quality and impact of both the scientific research papers and scientists.Effectively identifying, discovering, and evaluating high-impact papers using scientometric methods, and adopting reasonable evaluation procedures and methods are vital to stimulating scientists' creative vitality.Examples of methods used for evaluating impact are: h-index and the cited frequency of articles and the number of highly cited papers.Here we propose a new method to assess the scientist impact based on citation iteration.The method was inspired in the PageRank algorithm.In the present study, both the number of citations and the citing publications after each citation were considered.According to the obtained results, the proposal allows a more accurate measurement of the impact of scientific papers.Also, the application of this method, it can greatly improve the judgment efficiency of high-impact scientists.We have also conducted an empirical study at three levels in the discipline of mathematics, namely the comparisons of two publications, two scientists and eight scientists.Results show that indexes proposed in this dissertation designed for the publications' impacts evaluation and scientists' impact evaluation can be used to find the cause behind the number of cited frequencies resulting in the impact difference.The Q-index for publications' impacts evaluation and F-index for scientists' impacts evaluation proposed in this article can be used more accurately to check and evaluate the impact of scientists.Additionally, these new indexes can be used in the research management of departments at all levels, and can be useful by the states to find leading scientists in several fields.
A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study

Background The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors’ writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning–based institution name normalization model can increase the accuracy of institution name normalization at the semantic level. Objective This study aimed to train a deep learning–based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization. Methods In this study, an institution name normalization–oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus. Results It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names. Conclusions The deep learning–based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value.
Pengaruh Metode Demonstrasi Menggunakan Media Nyata Terhadap Hasil Belajar Siswa Pada Pembelajaran IPA Kelas IV SD N 88 Palembang

The purpose of this study was conducted to determine the effect of the demonstration method using real media on the results of learning science in class V SDN 88 Palembang. The method used is this research research using experimental research methods (experimental research). In this study, researchers used a quasi-experimental design, namely the posttest only control design. The subjects in this study were class IV.1 and class IV.2 which totaled 54 students. Data collection is done by testing and documentation techniques. The data analysis technique uses test questions which are tested for validity and reliability. The results of the data analysis showed that the results of the hypothesis test showed that the t value was 2.942 &gt; 1.675 t table and the significance value was &lt;0.05 (0.005 &lt;0.05). So it can be concluded that there is an influence of the demonstration method using real media on student learning outcomes in Class IV Science learning at SDN 88 Palembang.
PENGEMBANGAN E-MODUL BERBASIS APLIKASI FLIPBOOK KELAS IV SUBTEMA INDAHNYA KEBERAGAMAN BUDAYA

This study aims to determine the feasibility of an electronic module developed based on a flipbook application as teaching material for elementary school students. This research was conducted using the Research and Development (R&amp;D) method with the ADDIE model (Analyze, Design, Development, Implementation, Evaluation). This study aims to produce an e-module product based on a flipbook application, theme 7 sub-theme 2 learning 2 The Beauty of Cultural Diversity in class IV Sekolah Dasar Regency with a total of 30 students as respondents. To determine the feasibility test, validation sheets were used by e-module experts and material experts with the results obtained in the appropriate category. The results of the validation of the media, language, and material aspects show that the flipbook e-module is very feasible to use with an average percentage gain of 90.45%. While the results of the study on student responses showed that the flipbook e-module was feasible to use with an acquisition of 82,31%. Based on the results of the study showed that the e-module based on the flipbook application can be categorized as feasible, effective, and practical for students to use in learning.
An adaptive combination algorithm based on deep learning and genetic algorithm for anomalous events detection

One of the most widely used human behavior detection methods is anomaly detection, which this article covers. Ensuring a person's safety is a crucial task in every community today due to the ever-increasing actions that can be dangerous, from planned crime to harm from an accident. Classic closed-circuit television is insufficient since a person must always be awake and available to monitor the cameras, which is costly. Also, someone's attention tends to decrease after a certain period of time. Due to these reasons, a surveillance system that is automated and able to detect unusual activities in real-time and give sufferers prompt aid is necessary. It should be noted that the identification process must be completed swiftly and correctly. In this paper, we employ a model based on mixes the machine learning (ML) model, namely genetic algorithms with deep learning (DL). In this study's experimentation, the UCF-Crime dataset was employed. The detection accuracy on the testing sample dataset was equal to 89.90%, while the area under the curve (AUC) was equal to 94.58%. The developed models have demonstrated reliability and the ability to achieve the greatest accuracy when compared to models that have already been designed.
Weather Logger Using Weather Cloud

In this project, we have proposed an IOT and cloud based Weather Monitoring System.The aim of weather monitoring system is to detect, record and display various weather parameters such as temperature ,humidity.This system makes use of sensors for detecting and monitoring weather parameters and then this collected information is sent to the cloud which can be accessed using the internet.The data displayed as an output can be observed .The system engages an Arduino UNO board, sensors, WIFI Module which sends data to cloud computing services.A web page is also created which exhibits the data and displays it to users.
Technical university educational process individualization

The problem of the implementation of individual educational trajectories of students developing basic educational programs of higher education in a technical university, whose feature is a holistic education in a certain area of professional activity, is considered. The authors define an individual educational trajectory as an individual plan for professional and personal development, which helps the student to discover his interest in a particular direction and develop his strongest qualities. The article proposes an approach to the implementation of individualization in the development of basic educational programs through the introduction of the model of higher education ‘2 + 2 + 2’, which allows the student to choose an enlarged group of areas and specialties, the direction of training and the educational program during training. The authors consider the problems of ensuring the choice of an educational program after the second year of undergraduate studies, which are regulated by the procedure for transferring a student to another educational program of higher education. The problems of choosing elective disciplines by students of different courses of study and areas of training are also considered. The authors propose an approach for obtaining additional microqualifications for students within the framework of one educational program without violating the systemic nature of education, which in turn allows the graduate to perform his professional functions in several jobs and increases his competitiveness in the labor market.
Elasticity for computable (re)insurance

Elasticity offers contractual parties the linguistic space to manage the unknown. While this has historically operated as a powerful tool in negotiation, elasticity has also behaved as a double-edged sword. That is, elastic language facilitates the present, but does not account for the future. In this article, we consider that by understanding the role of vagueness in contracts, and in effect, making the implicit explicit, it may be more rewarding strategically for contractual parties in the long term. Specific to the context of reinsurance, we notice that elasticity may be regarded as a mirror to negotiation dynamics, traceable by the linguistic complexity of contractual clauses. Therefore, a determination of elasticity, through a diagnostic tool, can offer insights to enable for more intentional drafting and certainty at the contract performance stage.
Copyright Protection and Risk Prevention of Short Video Platform

In the new media era, the short video industry is booming, but it has also become the hardest hit area for copyright infringement. In practice, there are problems such as low cost of copyright infringement, high concealment, difficulty in judicial rights protection, and huge differences in the realization of original authors' income. Given the significant challenges involved in managing copyright infringement in the short video industry, it is imperative to identify and implement an appropriate regulatory and legal framework that can effectively address these issues. This will require close collaboration between industry stakeholders, including content creators, platform providers and legal authorities. In this context, it is urgent to standardize and manage short video copyright infringement issues, and effectively protect the copyright of short videos, so as to regulate the proliferation of infringements, encourage short video creators to carry out cultural creation, and promote the healthy and orderly development of the short video industry.
Image Processing Based Intelligent Traffic Lighting System

Due to India’s rising automobile population, traffic congestion has become a severe problem. The flow of traffic at road intersections is frequently managed by traffic signal control systems. To govern traffic flow, the majority of traffic signal systems are using pre-time and countdown timers. Because of the system’s fixed-time configuration, traffic jams frequently result from the system’s inability to handle unforeseen significant traffic flows. As a result, adaptive traffic lights that can perform monitoring in the real-time are necessary for adjusting traffic-light signals depending on the density of traffic. The present study has accorded a reconciling traffic signal control system which takes pictures of every lane at a junction and controls the traffic in an efficient way using image processing and image matching techniques. At each junction, the traffic density in the photos is compared. As opposed to other less packed roads, the results demonstrate that the vehicles on the densest road are given more time to pass. The traffic level is decreased by utilizing a camera that is rotated by a servo motor to take pictures of the intersection’s whole length. These pictures are then transferred to the Geany software, where a programme counts the amount of pictures and displays traffic light signals.
Impact Of Effective Corporate Culture on Organizational Performance: An Empirical Study

The effectiveness of corporate culture and organizational performance are examined in this study.Corporate culture is incredibly important in determining how people behave, value, and think inside an organization.It includes accepted values, customs, and procedures that direct employee behavior and affect productivity.Employee engagement, satisfaction, and dedication are fostered by an effective and positive business culture, which enhances organizational success.The core components of great business culture-clear ideals, capable leadership, transparent communication, and employee empowerment-are examined in this essay.Additionally, it looks into the several ways that improved corporate cultures can improve performance outcomes such as boosted productivity, innovation, customer happiness, and financial performance.The report also emphasizes the possible difficulties in creating and keeping a favorable business culture.Leaders and managers can benefit greatly from understanding the link between corporate culture and organizational performance in order to improve performance and establish a long-lasting competitive advantage.The researcher had considered 187 people from different organizations to know the impact of effective corporate culture on organizational performance and concludes that there is significant Impact of effective corporate culture on organizational performance.
SPEECH EMOTION RECOGNITION USING LSTM WITH NATURAL AND SIMULATED DATASET

Emotion-Recognition (ER) can be challenging to identify emotions from speech signals as input, especially when speech signals are the main source of data.Despite these difficulties, researchers have produced impressive research on speech-based emotion recognition.The main challenges in emotion identification are choosing appropriate dataset, locating relevant speech characteristics, and picking-up the appropriate classification model.The accuracy and efficiency of emotion detection systems, which have many uses in the fields of healthcare, education, and entertainment, are being actively worked on by researchers to overcome these issues.It is still an exciting and developing field of study to identify emotions from speech signals.In this paper, we are using simulated TESS dataset through LSTM approach and attaining the accuracy of 62%.