Reviewerly/exHarmony huggingface dataset:
Sentimental Analysis of Various TV Serials in Society

Sentiment analysis which involves identifying emotions in the text is a widely studied topic of natural language processing. Many researchers focus on social media content like posts, tweets and reviews for their studies. In this paper, a large number of data from comments about serials on social media and internet forums in Banglish is collected and analyzed. We use NLP techniques to do the sentiment analysis of the comments. This study explores how different kinds of serials have impacted society and concentrates on how they have influenced the creation of Banglish comments. This study aims to conduct a sentiment analysis of Banglish comments generated in response to different genres of serials and to analyze their potential societal implications. Along with Long Short-Term Memory (LSTM), we use several machine-learning algorithms such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes, Decision Tree (DT), AdaBoost, Random Forest (RF) and Support Vector Machine (SVM) to create this model. The SVM gives the highest accuracy among all of the machine learning algorithms.
Geochemical Assessment of Some Trace Elements in Selected Locations at Thi-Qar Governorate Soil, Southeast Iraq

Trace elements (Co, Cr, Mn, Mo, Ni, Rb, Th, U, and V) in surface soil of Al-Aslah (NH1), Nasiriyah refinery (NH2), Gharraf (NH3), and Tall Al-Laham (NH4) at Thi-Qar governorate, southeast Iraq, were studied. The study aims to detect their distribution and pollution levels to reduce the subtracted from industrial sources and avoid using contaminated areas for agricultural purposes before the soil treatment process. Inductivity Coupled Plasma Mass Spectrometry (ICP-MS) technique was used to determine the concentrations of these elements, and the results showed that there is a high concentration of Cr, Ni, Mo, and Mn in the soil of the study area, which exceeded the international levels compared with other elements (Co, Th, U, and V). The Geo accumulation index (I geo) values showed that the soil area was classified as low contamination in all sites and for all trace elements except for Mo, which is polluted in Al-Gharraf and strongly polluted in Tall Al-Laham. The values of the enrichment factor (EF) were indicated to be minimal enrichment of (Mn and V) in all sites and moderate enrichment of (Co) in all sites except in the Gharraf soil sample, which is considered as minimal enrichment. At the same time, it suffers from significant enrichment of (Cr) in all sites. The values of contamination factor (CF) showed extreme contamination of Cr and Ni in all sites due to the oil industry (Nasiriyah refinery), except for Ni in the Gharraf soil sample, which is considered as high contamination and moderately contamination with Co in all sites, and low contamination with Mn and V. The main cause of the increasing trace element concentrations in the study area that the samples located in industrial sites where trace elements are one of the waste products from fuel combustion from the oil fields and the waste of the brick factories near the study area, also the nature of the study area soil which is clay soil that absorb the trace elements on its surfaces.
THE MODEL OF PEDAGOGICAL COOPERATION IN THE CONDITIONS OF INCLUSIVE EDUCATION AS AN IMPORTANT FACTOR OF THE FORMATION OF SPEAKING COMPETENCE

The article deals with the issue of the model of cooperation between a teacher and a teacher’s assistant in elementary school in classes with inclusive education for the purpose of forming speech competence. The relevance of the problem, as well as its goal and task, was formulated. The purpose of the article was to find out the model of pedagogical cooperation between a teacher and a teacher’s assistant, aimed at the formation of speech competence of younger schoolchildren in the conditions of inclusive education. The tasks were: determination of models of cooperation between a teacher’s assistant and a teacher in classes with inclusive education and a presentation of one’s own vision of the formation of speech competence of children with intellectual disabilities. The formation of speech competence consists of the ability to listen, perceive and reproduce information, read and understand the text, conduct dialogue, participate in discussions, convince and defend one’s point of view, express one’s own thoughts, considerations, views in oral and written forms. Modern scientific searches of scientists regarding models of cooperation were analysed. It was found out that these researches were aimed at determining the specifics of working with children with special needs, clarifying the models, forms, methods of cooperation of the support team, including the teacher and the teacher’s assistant. Different positions on cooperation are indicated: pedagogical interaction between a teacher and a student, team cooperation between a teacher and a teacher’s assistant in classes with inclusive education. The author’s vision of a model of pedagogical cooperation between a teacher and a teacher’s assistant for the purpose of forming speaking competence in younger schoolchildren in the conditions of inclusive education was formulated. It includes innovative methods and means of learning, a system of training exercises of reproductive and creative-productive direction, corrective tasks to ensure the development of four types of speech activity. The author believes that this can be realized in the process of creating a joint methodical portfolio by teachers. The system of tasks should be developed at the following levels: lexical and orthoepic, phrase level, sentence, text in two directions – dialogic and monologic.
GEOCHEMISTRY AND ORIGIN OF BANDED ORE OF MANGANESE BELT, EASTERN CARPATHIANS, ROMANIA.

The Manganese Belt has a great mineralogical diversity and rarity contining more than 350 minerals and mineral varieties, of which 182 are new for Romania, being one of the most complex deposit in the world. The manganese ores are of carbonate-silicate type with a few oxides and sulfides. The chemical composition of many manganese carbonates, silicates, and oxides have high Mn/Fe ratios. The all manganese ores/manganiferous rocks have well-developed laminations/bandings, that appeared in hand samples and thin sections, representing distinct lithologies/petrographic types. The folowing types were established: metachert, quartzite with alkali pyroxenes and amphiboles, tephroite, Mn-humites, spessartine, pyroxmangite, rhodonite, mangancummingtonite, nambulite and natronambulite, pyrosmalte, and vein type (bannisterite, ganophyllite, and stilplomelane), all host by Tulgheş Series 1 (TG1) rocks. Each type was geochemical analysed. All lithology types show low concentrations of Ni, Co, Cu, Zn, U, Th, and REE trace elements, being similar to the trace element concentrations reported from sediments which are forming in hydrothermally active regions on the present sea floor. The metachert shows similar, but in low concentrations of trace elements, as the type ore. The metachert and manganese ore have high Li concentrations as trace element. The association of the manganiferous lithologies with metachert and meta-igneous rocks of TG1 is consistent with deposition of the original sediments in a rifting envronment, such as a back-arc basin. The current tectonic and structural setting of Mn ore correspond to their development and evolution in a subduction/rift zone, a narrow and long active paleotrench area, reflected in their line development as belt.
Research on Automatic Generation of Social Short Text Based on Backtracking Pattern

Aiming at the problems of cyclic repetition and incoherence in text generated by the pre-trained language model based on Transformer, the backtracking pattern of phrase joint sentences is proposed to improve the generation performance of the model, by imitating the deletion and modification in the process of human writing. In the text generation process, phrase level and sentence level backtracking processes are added to control generation quality globally. The phrase level captures generation errors and tracks them through the 3–4 sliding window algorithm, controls word root selection at the word or phrase level, and improves text quality at the word level, the quality evaluator based on BERT is introduced into sentence level, which comprehensively judges the quality from fluency, consistency, redundancy and semantic correlation between sentences, and the evaluator score is used as the condition for backtracking resampling, so errors in the generation process can be detected and corrected. This method focuses on generation and adjustment, does not affect the training amount and generation mechanism of the model, and has certain universality. Based on the typical open language model GPT2 and the conditional language model PPLM, this paper realizes the high-quality generation of microblog text. Experiments show that after introducing backtracking, the model can towards expected quality direction without damaging the attribute consistency of the conditional model, and the text is more coherent and readable, which is close to human writing style. It provides a solution to improve the quality of social text generation.
Group Regularization Learning with Dynamic Regularization Factor Based on BN Layer Scaling Factor

Convolutional neural network (CNN) is increasingly known as computation-intensive and memory-intensive models, which hinder their deployment on embedded systems. Attempts have been made to compress CNNs using the pruning method, especially structured pruning, which can achieve a compact network without affecting the original accuracy. Group regularization methods are usually used to achieve structured pruning. However, existing group regularization methods usually use a fixed regularization factor to compress weights towards zero, which has an unreasonable presupposition that all weights are equally important. To solve this issue, we propose a group regularization with dynamic regularization factor by combining regularization-based structured pruning and importance-based structured pruning, named DRFGR. It assigns a different and updatable regularization factor for each filter based on the scaling factors of the Batch Normalization (BN) layer, which represents the importance of the filters. The efficiency of DRFGR has been verified by empirical analysis on the CIFAR10 dataset with popular CNNs. The results show that DRFGR can compress the network more adequately than the method with a fixed regularization factor, while also improving accuracy.
Zero-Shot Construction of Chinese Medical Knowledge Graph with ChatGPT

Knowledge graphs have revolutionized the organization and retrieval of real-world knowledge, prompting inter-est in automatic NLP-based approaches for extracting medical knowledge from texts. However, the availability of high-quality Chinese medical knowledge remains limited, posing challenges for constructing Chinese medical knowledge graphs. As LLMs like ChatGPT show promise in zero-shot learning for many NLP downstream tasks, their potential on constructing Chinese medical knowledge graphs is still uncertain. In this study, we create a Chinese medical knowledge graph by manually annotating textual data and using ChatGPT to automatically generate the graph. We refine the results using filtering and mapping rules to align with our schema. The manually generated graph serves as the ground truth for evaluation, and we explore different methods to enhance its accuracy through knowledge graph completion techniques. As a result, we emphasize the potential of employing ChatGPT for automated knowledge graph construction within the Chinese medical domain. While ChatGPT successfully identifies a larger number of entities, further en-hancements are required to improve its performance in extracting more qualified relations.
An Enhanced Water Flow Optimizer Incorporating Adaptive Weights and Search Step Sizes

Meta-heuristic algorithms are highly regarded for their exceptional global search capability. In recent years, a novel natural inspired algorithm known as the Water Flow Optimizer (WFO) has emerged. This algorithm mimics the laminar and turbulent flow phenomena observed in natural water systems and showcases remarkable optimization performance. However, similar to other meta-heuristic algorithms, WFO still suffers from the drawback of getting trapped in local optima. In this paper, we propose an enhanced water flow optimizer (AWS-WFO) based on adaptive weights and search step sizes to prevent the algorithm from getting stuck in local optima. AWS-WFO considers the weights and adaptive exploration performance mechanism, draws inspiration from the advantageous features of LDIWPSO (Linearly Decreasing Inertia Weight Particle Swarm Optimization) and effectively enhances the optimization performance of WFO. In order to evaluate the performance of the proposed AWS-WFO algorithm, comparative experiments were conducted using seven widely recognized test functions as benchmarks. The experimental results indicate that the proposed algorithm exhibits a certain level of competitiveness.
Research on Distance Teaching Course Interactive System Based on Computer Algorithm Research Data

In this paper, an interactive distance teaching system is designed based on data denoising algorithm. Firstly, by eliminating the interference of data, the interactive teaching and research section is established. Secondly, Spring MVC is used as the framework to implement an interactive system of distance education based on database. Wavelet analysis technology is used to realize the clarity of dynamic images in online teaching. Then a digital signal processing method based on wavelet decomposition is proposed and verified by interactive reaction time. The practice proves that the system has a fast response speed. It can reduce the interaction delay phenomenon in the network and achieve the goal of efficient interaction in the network.
Enhancing Pathology Insights: Deep Learning for Histopathological Image Analysis in Colorectal Cancer

Histopathological image analysis has emerged as a pivotal tool in the field of colorectal cancer diagnosis and prognosis. As the incidence of colorectal cancer continues to rise globally, the need for accurate, efficient, and scalable diagnostic methods becomes increasingly paramount. Deep Neural Networks (DNNs) have shown remarkable potential in various medical imaging tasks, including histopathological image analysis. In this paper, we introduce DNNI, a novel framework comprises of Deep Neural Networks and Inception, tailored specifically for the comprehensive analysis of colorectal cancer histopathological images. The DNNI framework leverages state-of-the-art deep learning techniques, drawing inspiration from the inception architecture, to address the unique challenges posed by colorectal cancer histopathology. Moreover, we present a large-scale dataset of colorectal cancer histopathological images, carefully curated and annotated, to facilitate training and evaluation of the DNNI model.This dataset incorporates diverse tissue samples, capturing various stages and subtypes of colorectal cancer, thus enabling robust and generalizable model development. Experimental results demonstrate the efficacy of DNNI as 98.25% in accurately identifying cancerous regions, grading tumor malignancy, and predicting patient outcomes. We compare our model's performance with existing approaches, showcasing superior accuracy and efficiency.
The Evaluation Benchmark for SP ARQL Federated Query

In SP ARQL federated query system, cardinality estimation is one of the most critical steps that affect query performance. In recent years, a large number of federated query cardinality estimation methods have been produced, which is particularly important for evaluating the performance of different methods. However, the existing research shows that there is no unified test benchmark when evaluating cardinality estimation methods, and the commonly used precision index Q-error has also been proved to be unable to generate the optimal query plan. In order to solve the above problems, this paper proposes a test benchmark (FQCEB) for SP ARQL federated query cardinality estimation. FQCEB consists of three components: benchmark data set, query generator and evaluation index, including 32 templates created manually, 1600 unique queries and more than 200K query subplans. The experimental results show that when the performance of radix estimation method is evaluated by using the test benchmark in this paper, it shows the applicability of the benchmark to various methods, and the P-error evaluation index in the benchmark shows the correlation with the query running time, and the correlation coefficient is as high as 85%, which proves that P-error is more suitable for evaluating the actual performance of radix estimation method than Q-error.
A Framework for Virtual Training in Crisis Context and a Focus on the Animation Component: The Gamemaster Workshop

Critical infrastructures, historical places, and sensitive buildings are vulnerable to crises, from natural to man-made disasters. Public institutions and practitioners have to train, before the occurrence of a major event, to be prepared to respond to the crisis. These sites are difficult to vacate just to perform exercises. To propose training adapted to the needs the trainer have to create an exercise dedicated to the aimed skills. Stakeholders responding to high-risk situations are asking for new ways to train and compensate for the weaknesses of existing training approaches. The research work presented in this paper concerns the design of immersive environments that can be used to create, implement and simulate crisis scenarios to improve collaborative training.
Text Classification Algorithm for Medical Adverse Events Based on Deep Learning

The management of medical adverse events is an important topic in the field of healthcare. The classification of medical adverse events is the primary link in adverse event management. However, the research on medical adverse event classification technology is still in its early stages, mainly using rules or machine learning methods, and the prediction accuracy is not high enough. To address the above issues, this paper explores the application of deep learning in medical adverse event text classification tasks and proposes a hybrid model that combines deep pyramid convolution and cyclic convolution network structures to achieve category classification of adverse events. This model has two feature extraction modules: deep pyramid convolution and cyclic convolution. These two modules extract features in parallel using BERT word vectors as inputs, and then concatenate the features. The experiment shows that the model has good performance in adverse event classification. In order to further improve the effectiveness of adverse event text classification, this paper proposes two improved models, BRDCN and BRDHW, based on the hybrid model. The BRDCN model abstracts the concatenated features by adding a convolutional network after the feature extraction module to further extract features from the concatenated tensor. The experiment shows that the multi classification performance of adverse event cate-gories in the BRDCN model has improved compared to before improvement. The BRDHW model refers to the gate control structure of the highway network and sets a gate control matrix. This matrix is used to fuse the features extracted by the two modules. The experiment shows that the BRDHW model's multi classification performance of adverse event categories has been further improved.
Investigation of Cognitive Preference in Augmented Reality Node-Link Diagrams

Node-link diagrams are commonly used for visualizing entity-relationship data, but as the number of nodes and links increases, it can become challenging due to occlusion issues. One potential solution is representing these diagrams in 3D. In a recent study, we conducted a multi-factor experiment in an AR (Augmented Reality) environment to investigate how the complexity and spatial arrangement of node-link diagrams, as well as user interaction preferences, are related. We varied factors like viewing angle, rendering distance, and the number of nodes. Three viewing angles (24, 34, and 42 degrees) were tested, with three distance levels (23cm, 36cm, and 60cm) and three node count levels (8, 16, and 24). The results indicated that the time it took to perform tasks was mainly influenced by the viewing angle in the diagram's immediate vicinity. Surprisingly, participants preferred to rotate the node-link diagram for complex tasks, even though it took longer than simply walking and viewing. Additionally, participants tended to rotate the diagram when the target was closer during detailed viewing tasks. In these detailed tasks, people typically walked clockwise around the diagram and got closer to it. The research results have reference value for the human-computer interaction design of data visualization in the virtual space.
Jewelry Recognition via Encoder-Decoder Models

Jewelry recognition is a complex task due to the different styles and designs of accessories. Precise descriptions of the various accessories is something that today can only be achieved by experts in the field of jewelry. In this work, we propose an approach for jewelry recognition using computer vision techniques and image captioning, trying to simulate this expert human behavior of analyzing accessories. The proposed methodology consist on using different image captioning models to detect the jewels from an image and generate a natural language description of the accessory. Then, this description is also utilized to classify the accessories at different levels of detail. The generated caption includes details such as the type of jewel, color, material, and design. To demonstrate the effectiveness of the proposed method in accurately recognizing different types of jewels, a dataset consisting of images of accessories belonging to jewelry stores in Córdoba (Spain) has been created. After testing the different image captioning architectures designed, the final model achieves a captioning accuracy of 95%. The proposed methodology has the potential to be used in various applications such as jewelry e-commerce, inventory management or automatic jewels recognition to analyze people's tastes and social status.
Contact Tracing Applications in the Context of COVID-19 Pandemic: Proposals for Acceptance of its Use

COVID-19 has been the biggest threat humanity has faced since World War II. The rapidity of its spread generated a general feeling of uncertainty and fear. National governments had to adopt measures in a context of total ignorance of the threat. The development of contact tracing applications has emerged as the fastest option to contain the spread of the virus. However, its acceptance has been reduced. This study intends to understand the reasons for the resistance in the use of those applications and to identify proposals so that in future situations the use of applications of contact tracing can be generalized. This is a qualitative study using a literature review in accordance with the procedures. The literature review allowed us to identify, on the one hand, three dimensions in the approach to the theme: technical, legal, and political. In the particular case of COVID-19, the political dimension stood out compared to the others.