Reviewerly/exHarmony huggingface dataset:
A Hybrid Deep Learning GRU based Approach for Text Classification using Word Embedding

Text categorization has become an increasingly important issue for businesses that handle massive volumes of data generated online, and it has found substantial use in the field of NLP. The capacity to group texts into separate categories is crucial for users to effectively retain and utilize important information. Our goal is to improve upon existing recurrent neural network (RNN) techniques for text classification by creating a deep learning strategy through our study. Raising the quality of the classifications made is the main difficulty in text classification, nevertheless, as the overall efficacy of text classification is often hampered by the data semantics' inadequate context sensitivity. Our study presents a unified approach to examine the effects of word embedding and the GRU on text classification to address this difficulty. In this study, we use the TREC standard dataset. RCNN has four convolution layers, four LSTM levels, and two GRU layers. RNN, on the other hand, has four GRU layers and four LSTM levels. One kind of recurrent neural network (RNN) that is well-known for its comprehension of sequential data is the gated recurrent unit (GRU). We found in our tests that words with comparable meanings are typically found near each other in embedding spaces. The trials' findings demonstrate that our hybrid GRU model is capable of efficiently picking up word usage patterns from the provided training set. Remember that the depth and breadth of the training data greatly influence the model's effectiveness. Our suggested method performs remarkably well when compared to other well-known recurrent algorithms such as RNN, MV-RNN, and LSTM on a single benchmark dataset. In comparison to the hybrid GRU's F-measure 0.952, the proposed model's F-measure is 0.982%. We compared the performance of the proposed method to that of the three most popular recurrent neural network designs at the moment RNNs, MV-RNNs, and LSTMs, and found that the new method achieved better results on two benchmark datasets, both in terms of accuracy and error rate.
Research on Feature Fusion Methods for Multimodal Medical Data

With the rapid development of artificial intelligence, knowledge graph, image processing, etc. have been widely used, and smart medical care, as a major application scenario of artificial intelligence, has received a lot of attention. Traditional diagnostic methods have problems such as low accuracy and low efficiency, and the research and application of knowledge graph and image classification in the field of dermatology are also in the initial stage, but text-based knowledge graph technology and image-based image classification technology have developed very maturely. Considering that various current image classification algorithms extract features, feature calculation, and model matching from images, they do not consider obtaining information such as features or relationships that are not in images from text data to participate in image classification tasks. In this paper, the optimized hierarchical perception model H-HAKE based on hierarchical perception model KGE-HAKE calculates selector parameters by improving the hierarchical perception model to add category dimension to the TransE coordinate system, divide more image features and entities with the same attribute into the same level, increase the number of links between image and map entities, and produce better data coverage effect. Aiming at the image classification task, this paper proposes a game tree model to optimize the classification results, including calculating the confidence degree based on the map, the aggregation degree of the classification results, the inference value of the entities in the domain, etc., and comprehensively designing the fusion mode of knowledge graph and image classification algorithm KG-based CNN in scenarios such as multi-map input and feature pre-extraction. The mode is effective enough to enable the image classification task to utilize multimodal data, and the effectiveness is verified by multi-scenario and data ablation experiments on the public data collection.
Design of a Blind Guidance System Based on RealSense and the Improved YOLOv5 Algorithm

To assist blind people in travelling and solve the problems of high hardware costs, insufficient portability of blind assistance devices, and vulnerability to environmental impacts, a modified YOLOv5 algorithm (YOLOv5-CM) based on ground plane segmentation and Euclidean clustering algorithms is proposed and applied to a blind guidance system based on RealSense D435. This proposed algorithm adds coordinate attention and uses MobileNetv3 as the backbone network for YOLOv5s to extract the main features. Compared with those of the YOLOv5 model, the mAP was improved by 0.7%, the model size was decreased by 79.1%, and the number of parameters was reduced by 80.3%. In this study, the YOLOv5-CM algorithm was applied to design a blind guidance system. When the system detects a traffic light, the RealSense D435 is used to obtain depth images to detect and measure the distance from the pedestrian traffic light. The system provides warnings based on the measured distance. When the system does not detect a traffic light, it utilizes the ground plane segmentation and Euclidean clustering algorithms to obtain the location and distance of obstacles. This information is conveyed to the blind through voice guidance, which aids navigation.
A BMRC Algorithm Based on Knowledge Enhancement and Case Regularization for Aspect Emotion Triplet Extraction

Aspect Emotion Triplet Extraction (ASTE) is a vital branch in the field of NLP that strives to identify triads consisting of aspect words, opinion words, and emotional polarity from sentences. Each triplet contains three elements, namely, aspect words, opinion words and their emotional polarity. How to effectively extract attribute and opinion words and detect the connection between them is extremely important. Early work often focused on only one of these tasks and could not extract triples simultaneously in the same framework. Recently, researchers proposed using the bidirectional machine reading comprehension model (BMRC) to obtain triples of aspects, opinions, and emotions in the same framework. However, the existing BMRC model ignored professional domain knowledge, and the equal treatment training ignored the special contribution of individual instances in sentences to sentences. To this end, this paper proposes a BMRC with knowledge enhancement, which integrates the knowledge of specialized fields into the model, strengthens the connection between specific fields and open fields, increases instance regularization, and pays attention to the contribution of individual instances to sentences. Our experiments on several benchmark data sets have shown that our model has reached the most advanced performance.
Research on Clarification Question Recognition and Generation in Intelligent Q&amp;A

Intelligent question answering systems often encounter ambiguous questions that require the generation of clarification questions to understand users’ true intentions. Without clarification questions, syestems may be confused by ambiguous questions. In this paper, the generation of clarification questions is separated into three subtasks. This study focuses on the three subtasks of the clarification question identification and generation process. We propose the DeBERTA v3 + FC model for clarification question detection and entity prediction, and an improved ByT5-based model for generating diverse and comprehensible clarification questions. On the MSParS dataset, our method outperforms traditional DMN models by 11.2% and 15.17% in accuracy for clarification question detection and entity prediction tasks respectively, while the BLEU score is 4.9% higher than the traditional Seq2Seq models. The efficacy of our proposed methods is verified by their superior performance to traditional methods on all three subtasks. The results on the MSParS dataset demonstrate the effectiveness of our framework for generating clarification questions and entity predictions.
Dictionary-Assisted Chinese Nested Named Entity Recognition

Chinese Named Entity Recognition is a challenging task, made even more difficult by the presence of nested entity structures. Previous work on Nested Named Entity Recognition focused only on exploiting internal contextual information, while ignoring the use of external information. In this paper, we propose a dictionary-assisted Chinese Nested Named Entity Recognition model, called KBCNER. Our model uses the dictionary to obtain matching words, combines characters and phrases into character-phrase pairs, and integrates them into BERT. By doing so, we can extract richer semantic information from Chinese phrases than from a single character. The use of external information from the dictionary enhances the features of our model and obtains richer semantics. To avoid constraints from specific-length enumerations, we use bi-affine structures to obtain a global view of spans. We also model local interactions between spans using a Convolutional Neural Network (CNN), taking advantage of the spatial correlation between adjacent spans. Finally, we adopt the idea of contrastive learning based on R-drop to enhance the model's robustness. Experimental results demonstrate that our model achieves excellent performance on multiple datasets. By introducing external information, we improve the performance of the model, highlighting the significance of external information for Chinese Nested Named Entity Recognition.
Linguistic annotation of Byzantine book epigrams

In this paper, we explore the feasibility of developing a part-of-speech tagger for not-normalised, Byzantine Greek epigrams. Hence, we compared three different transformer-based models with embedding representations, which are then fine-tuned on a fine-grained part-of-speech tagging task. To train the language models, we compiled two data sets: the first consisting of Ancient and Byzantine Greek texts, the second of Ancient, Byzantine and Modern Greek. This allowed us to ascertain whether Modern Greek contributes to the modelling of Byzantine Greek. For the supervised task of part-of-speech tagging, we collected a training set of existing, annotated (Ancient) Greek texts. For evaluation, a gold standard containing 10,000 tokens of unedited Byzantine Greek poems was manually annotated and validated through an inter-annotator agreement study. The experimental results look very promising, with the BERT model trained on all Greek data achieving the best performance for fine-grained part-of-speech tagging.
Democratizing neural machine translation with OPUS-MT

Abstract This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our ongoing mission of increasing language coverage and translation quality, and also describe work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.
How to Define a Multi-modal Knowledge Graph?

As a form of structured human knowledge, knowledge graphs (KG) have attracted great attention from both the academic and industrial communities since their emergence. It is widely used in the field of artificial intelligence for applications such as information retrieval, data analysis, intelligent question-answering and recommendation systems. In recent years, various types of information on the internet have exploded in growth. In response, multimodal knowledge graphs (MMKGs) have emerged to serve the management and applications of different types of data. However, since the proposal of KG in 2012, there has not been a unified and standardized definition to describe KG, let alone MMKG. Based on previous research and experience, this paper has summarized the definition of KG through extensive investigation and explores the concept of MMKG. To provide a better illustration, this paper constructed a sample MMKG in the medical field based on an ontology and resource description framework (RDF). We use Neo4j for visualization and design a UI to extract node information. Finally, the shortcomings of the work were summarized, and future research directions were proposed.
AHM: A Novel Model for Mining Academic Hot Spots Based on a Scientific Knowledge Graph

Academic hot spots refer to a group of words that are widely considered in a specific period. Academic hot-spot mining is an essential task in the bibliometrics field, and the purpose is to mine academic hot-spots in a large number of studies. The common methods at present are bibliometrics, based analysis tools and machine learning methods, and these methods cannot fully apply deep semantic features. With the increase in academic papers, extracting hot spots is difficult through bibliometrics and machine learning methods. Combining deep learning technology to extract deep features and mine hot spots more efficiently and accurately is a challenge in the field of academic hot spot research. This paper proposed a novel model called AHM to compensate for the current shortcomings, which improves the feature representation of k-means++, obtains deep semantic and contextual features by applying deep learning technology, and fuses the two features as feature inputs for k-means++. The experimental results of a comparison of AHM with four baselines (i.e., k-means++, TF-IDF+k-means++, Word2vector+k-means++, and Node2vector+k-means++) on the literature datasets in the artificial intelligence field show that the AHM model has a better effect in academic hot-spot mining tasks. In addition, this paper presents an overall framework for evolution analysis and analyses the evolution path of academic hot spots during 2010−2020.
Cell Phone Use in American Civics and History Classrooms

The use of digital devices in K-12 classrooms has become increasingly contested with their ubiquity. This study examines policies governing cell phone use in elementary and secondary school civics, social studies, American government, and history classes in the United States. Cell phone policies implemented by teachers differ vastly across and even within schools. Data from an original national survey of civics and history teachers conducted by the Civic Education Research Lab at Georgetown University were used to examine cell phone policies nationwide. While there was near universal agreement among teachers about the importance of digital tools for learning, the propriety of using cell phones for instruction was hotly contested. Grade level and school characteristics, such as school size, Title I status, and one-to-one school programs, were better predictors of cell phone policies than teacher characteristics. Teachers with more years of experience were the most likely to institute strict cell phone policies. These findings indicate that there is no one-size-fits-all policy for cell phones and provide insights for teachers and schools for adapting workable strategies given the school context.
Partially Observable Markov Chains

The controlled Partly Observable Markov Decision Process (POMDP) architecture has shown to be effective in a variety of fields where one is required to disclose only partial knowledge about the problem’s structure and parameters. Since some state variables are difficult to track and measure correctly, it may be more beneficial to base judgments on less accurate information. This chapter focuses on the design of an observer for a class of partially observable ergodic homogeneous finite Markov chains. The major objective of the suggested approach is to derive the formulas for computing an observer and, as a consequence, the best control strategy. We create a new variable that combines the policy, the observation kernel, and the distribution vector in order to solve the issue. To retrieve the important variables, we derive the formulas. The POMDP model’s parameters are being learned in a dynamic context in this work. The development of the adaptive policies is based on an identification method, in which we count the number of unobserved events to estimate the components of the utility and transition matrices. The practical applications of the theoretical concerns addressed to a portfolio optimization problem are demonstrated through the use of a numerical example.
On a quantum inspired approach to train machine learning models

Abstract In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems. This represents a drastic departure from the standard approach of quantum machine learning which, to this day, is based on the use of actual physical quantum systems. To provide a clear context, the field of quantum inspired machine learning is first provided. Then, we proceed with a detailed description of our proposed method. To conclude, some preliminary, yet compelling, results are presented and discussed. Although at a seminal stage, the author firmly believes that this approach could represent a valid and robust alternative to the way machine learning models are trained today.
Towards bespoke optimizations of energy efficiency in <scp>HPC</scp> environments

Abstract We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent‐based decision‐making framework for delivering control decisions to middle‐ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment.
Optical essential secret image sharing using unequal modulus decomposition and gyrator transform

Abstract Essential Secret Image Sharing (ESIS) decomposes a secret image into a set of shares that are distributed among categorized participants, and ensures that only authorized subsets of these participants can restore the image. All ESIS schemes to date have been based merely on computational techniques. In this paper, an optical ESIS system is introduced which uses unequal modulus decomposition (UMD) and optical gyrator transform (GT), offering high-speed parallel processing and dispensing with any pre-processing stages. The presented (1, 2, n ) ESIS system generates n shares, including one essential share, such that any two shares that include the essential one, can reconstruct the initial secret image with no distortion. Any other unauthorized subset will not gain any information about the image. The scheme generates essential and nonessential shares that are of equal size, eliminating the need to concatenate sub-shares during the reconstruction of the secret image. The results verify that the secret image was completely retrieved in cases of authorized access, while full distortion occurred in cases of unauthorized access. The GT rotation angle serves as an additional authentication factor to validate the essential share and bolster the security. The optical ESIS system exhibits a high level of sensitivity to the changes in the GT rotation angle - that a variation of just 0.001 radians can cause the correlation coefficient to drop below 0.05.
Fuel Distribution Controller for ARFF Trainer with BACAK BAE: Enhancing Practical Learning in Aircraft Firefighting Operations

ARFF services are vital for aviation safety, but traditional trainers struggle with fuel efficiency and environmental regulations. This study aims to develop a Fuel Distribution Controller for ARFF trainers to enhance fuel efficiency and compliance. The goal is to improve training while reducing operational costs. Borg and Gall's Research and Development (R&amp;amp;D) model is the foundational framework in this study. This R&amp;amp;D model typically comprises ten essential steps, but for this research, these steps are streamlined into three central stages: preliminary study, research development, and Validation. These stages collectively guide a methodical and structured research process, commencing with comprehensive preliminary investigations, progressing to developing a research solution, and concluding with a validation phase to ascertain the innovation's effectiveness. Professional insights from Politeknik Penerbangan Palembang inform the development of the Fuel Distribution Controller for the ARFF Trainer, aiming to improve practical aircraft firefighting training. This innovation enhances fuel efficiency, reduces environmental impact, and minimizes operational costs. The revamped system simplifies operations and provides a cost-effective, user-friendly learning experience. This achievement, verified by experts and users, holds significant promise for aircraft firefighting training and education
The Effect of Promotion and Tuition Costs on The Determination of Higher Options (Case Study of New Students For The 2020/2021 Information Management Study Program Banjarmasin State Polytechnic

The decision-making process for higher education options is influenced by various factors, including promotions and tuition costs. This study aims to examine and determine whether promotion and tuition fees partially and simultaneously have a significant effect on the choice of college for new students of the 2020/2021 class of the Informatics Management study program, at the State Polytechnic of Banjarmasin. This type of research is explanatory with a quantitative approach. The variables in this study consist of promotion variables, tuition fees variables, and choice selection variables. Sampling using a non-probability sampling technique in which carried out using a convenience sampling technique of 60 respondents with analysis multiple linear. Based on the results of the data analysis carried out, it shows that there is a significant effect of the promotion and tuition fees have an effect on the choice of college for new students.
Optimizing Parallelization Strategies for the Big-Means Clustering Algorithm

This study focuses on the optimization of the Big-means algorithm for clustering large-scale datasets, exploring three distinct parallelization strategies. We conducted extensive experiments to assess the computational efficiency, scalability, and clustering performance of each approach, revealing their benefits and limitations. The paper also delves into the trade-offs between computational efficiency and clustering quality, examining the impacts of various factors. Our insights provide practical guidance on selecting the best parallelization strategy based on available resources and dataset characteristics, contributing to a deeper understanding of parallelization techniques for the Big-means algorithm.