Reviewerly/exHarmony huggingface dataset:
Incremental Encoding Transformer Incorporating Common-sense Awareness for Conversational Sentiment Recognition

Conversational sentiment recognition has been widely used in people's lives and work. However, machines do not understand emotions through common-sense cognition. We propose an Incremental Encoding Transformer Incorporating Common-sense Awareness (IETCA) model. The model helps the machines use common-sense knowledge to better understand emotions in conversation. The model uses a context-aware graph attention mechanism to obtain knowledge-rich utterance representations and uses an incremental encoding Transformer to get rich contextual representations. We do some experiments on five datasets. The results show that the model has some improvement in conversational sentiment recognition.
Energy Consumption Prediction in Ceramic Production Process Based on a PSO-ELM Coupling Model

The uncertainty and non-linearity of coal consumption in the ceramic production process are difficult to predict due to the weak generalization ability of the current single method. In order to improve the ability to match the supply and demand of coal consumption in the ceramic production process, this paper proposes a coupling model of Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) to predict coal consumption. First, the network structure is determined, and then the input layer weights and hidden layer thresholds of the ELM are optimized using PSO to effectively improve the prediction effect and accuracy. Finally, the method was verified by the monthly coal consumption data of a sanitary ware enterprise in Tangshan. The results show that the proposed PSO-ELM coupling model has higher prediction accuracy and generalization ability than Random Forest, Support Vector Machine, XGBoost and ELM.
High capacity reversible information hiding algorithm based on asymmetric prediction error histogram

The embedding capacity of the traditional reversible information hiding algorithm based on the asymmetric prediction error histogram is limited by the number of pixels at the peak point, the available pixels in the image are not fully utilized, resulting in the increase of invalid shift points, which makes the visual quality of camouflage image poor. In this paper, a new multi-bit translation reversible information hiding algorithm based on asymmetric prediction error histogram is proposed, which aims to greatly improve the embedding capacity of carrier image with minimal impact on image quality. The algorithm makes more use of the correlation between adjacent pixels, so that the error value of pixels is more concentrated near the peak point, and the difference between the peak point and the zero point is reduced, so as to obtain a more concentrated asymmetric prediction error histogram, which makes the embedding capacity larger. Meanwhile, the algorithm is not limited to the pixels of the peak point, and the error pixels that meet the conditions around the peak point are also embedded with secret information. The experimental results show that the algorithm can effectively reduce the invalid shifted pixels and reduce the distortion of the camouflage image while maintaining the large embedding capacity.
Radar micro moving gesture recognition method based on multi-scale fusion deep network

In order to solve the problem that the micro moving gesture features are not obvious and difficult to be identified, a micro moving gesture recognition method based on multi-scale fusion deep network for millimeter wave radar is proposed in this paper. The method is mainly composed of 2D convolution module, multi-scale fusion module and attention mechanism module. The multi-scale fusion module is composed of three residual blocks of different scales, which can obtain receptive fields of different sizes and obtain multi-scale features. Meanwhile, residual blocks of different scales are fused to increase the diversity of the network and better extract the deep features of the data. The Squeeze-and-congestion (SE) attention mechanism module is added to suppress the channel characteristics with little information. This improves the network identification accuracy and reduces the number of parameters and computation. The experimental results show that this method is simple to implement, doesn't need to do complex data preprocessing. The convergence speed of the network is fast, which can realize the effective recognition of the micro moving gesture.
Express-related Counterfeit Cigarette Crime Prediction with Imbalanced Data-based Machine Learning Techniques

The number of cigarette-related packages and normal packages is often extremely unbalanced in practice, this characteristic must be considered in the express-related counterfeit cigarette crime prediction, otherwise the performance of prediction model on unknown packages will be affected badly. In this paper, an approach of using SMOTE-ENN technology to deal with imbalanced express data and establish the express-related counterfeit cigarette crime prediction model is proposed. In the preprocessing stage, original data sets are structured by Chinese word segmentation method. In feature calculation and analysis stage, the spatio-temporal features of express data are calculated and analyzed, and the optimal features are further obtained by feature reduction algorithm named PCA. Next, based on optimal features, balanced data are obtained by mixed sampling. Finally, the classifier model was trained and optimized to predict the probability of a package containing counterfeit cigarettes in express data including 2851 normal samples and 285 cigarette-related samples randomly selected from 3077 cigarette-related samples. The performance of all classifiers is improved after using the SMOTE_ENN to balance datasets, and the indicators are above 98%. Experimental results demonstrate feasibility, effectiveness of the proposed approach.
Feature Weighted Cutset-type Possibilistic Fuzzy C-Means Clustering Algorithm

Possibilistic fuzzy c-means clustering (PFCM) algorithm improves the noise sensitivity of the fuzzy c-means clustering (FCM) algorithm and the serious coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm. However, the Euclidean distance of the PFCM treats all features equally in the clustering process and doesn't consider the imbalance between sample features, resulting in low clustering accuracy. Furthermore, as the number of clusters increases, the PFCM still has a partially coincident clustering problem due to the lack of between-class relationships of possibilistic memberships. Therefore, a feature weighted cutset-type possibilistic fuzzy c-means clustering (FW-C-PFCM) algorithm is proposed by introducing the cutset theory and feature weights into the PFCM in this paper. Firstly, the proposed FW-C-PFCM introduces a feature weighted parameter in the objective function, and different features should take different weight values according to the distribution of samples. Secondly, the cutset theory is utilized to divide the data into inner core and outer core regions, and some possibilistic memberships inside each cluster core are modified in the iterative process. Finally, the experiments on synthetic multi-class datasets and the Iris dataset show that the FW-C-PFCM improves the partially coincident clustering phenomenon and overcomes the imbalance between sample features. The comparative experiments on a color image also indicate that the FW-C-PFCM enhances the segmentation accuracy and generates better clustering results than PFCM.
Nominalization in Priyanka Chopra’s Selected Speeches

This study aims to find out types of nominalization used in Priyanka Chopra’s selected speeches and the realizations. The selected speeches were taken from written sources and videos on the internet consisting of three speeches. This research used a descriptive qualitative method by applying theory of nominalization in Systemic Functional Linguistics by Quirk et al. The data collection techniques were conducted by applying a documentation method. The documents used to support this study were in the form of videos and transcriptions of the speeches on the internet. The results showed that not all the three types of nominalization were used in the three selected speeches. The nominalization with the gerundive noun type was used in all the three selected speeches. There were 6 gerundive nouns (14.6%) in the first speech, 7 gerundive nouns (25%) in the second speech, and 4 gerundive nouns (21%) in the third speech. The nominalization with verbal noun type was only used 1 (2.4%) among the three selected speeches which was in the first speech. Meanwhile, the deverbal noun type was used most in all the three selected speeches. There were 34 deverbal nouns (83%) in the first speech, 21 deverbal nouns (75%) in the second speech, and 15 deverbal nouns (79%) in the third speech. The nominalizations were realized by suffixations and conversions. The nominalizations were formed in the presence of the suffixes –dom, -er, –ing, -ion, -ity, -ment, and -or. Also, they were formed by conversion, such as the nouns care, cause, experience, and waste.
Few-shot Object Detection Model based on Transfer Learning and Convolutional Neural Network

Aiming at the problem that Faster R-CNN fails to meet the current situation of few-shot object detection, an improved few-shot object detection model is proposed based on transfer learning and Faster R-CNN. Transfer learning algorithm is applied by transferring trained weights which from similar source dataset initially. The attention mechanism is added to the feature extraction module of the Faster R-CNN algorithm model and the idea of feature pyramid is introduced to fuse feature maps to build an effective feature extraction model. At the same time, a regularization term of background suppression is added to loss function of Faster R-CNN to improve the accuracy of object detection. The experimental results show that not only the feature extraction ability of the object detection network is strengthened, but also the improved loss function can effectively suppress the background interference and make the model training more adequate as a result. Experimental results show that the proposed network can converge faster and the detection accuracy is improved compared with other methods.
A Study on Learner Awareness Survey for Adult Learners Customized Curriculum - Focusing on the Case of D University

This study is a study on the results of educational satisfaction surveyed on adult learners at universities that operate customized curricula for adult learners. The demand survey conducted on adult learners at D University analyzed the qualitative survey FGI and the quantitative survey questionnaire education satisfaction survey. The research results of qualitative data analysis were obtained as follows. The research results of quantitative data analysis were obtained as follows.&#x0D; First, in the case of the lower grades of adult learners who have entered the regular four-year regular course, it is necessary to focus on supporting them to adapt to school life at the adult learning center. Senior students need community management. In particular, it is necessary to ensure that adult learners do not give up and study is sustainable with social support, material support, information support, and emotional support. Second, the results of the change through the educational satisfaction survey of adult learners are as follows. The results of the three year satisfaction level were continuously reflected. High positive results were found in all questions.&#x0D; Through continuous system improvement and communication, it can be seen from the results of satisfaction with students that the university dedicated to adult learners is also gradually improving its constitution as a venue for adult learner education. Third, the results of changes in the satisfaction of comparative programs are as follows. High average results were found because it helped improve content benefits and learning ability. Finally, in the question of the educational effects felt by each adult learning, the changes in their competencies were the highest in the order of self-management, development, and organizational understanding in response to satisfaction with the effects of the curriculum and experience of the department to which adult
Improving Intelligence Metrics Using Frequency Domain Convolutions for Improving Bug Prediction

Background: The novelty of the work lies in the formulation of these frequency-based generators, which reflects the lowest level of information loss in the intermediate calculations. The core idea behind the approach presented in this work is that a module with complex logic involved may have more probability of bugs. Software defect prediction is the area of research that enables the development and operations team to have the probability of bug proneness of the software. Many researchers have deployed multiple variations of machine learning and deep learning algorithms to achieve better accuracy and more insights into predictions. Objective: To prevent this fractional data loss from different derived metrics generations, a few optimal transformational engines capable of carrying forward formulations based on lossless computations have been deployed. Methods: A model Sodprhym has been developed to model refined metrics. Then, using some classical machine learning algorithms, accuracy measures have been observed and compared with the recently published results, which used the same datasets and prediction techniques. Results: The teams could establish watchdogs thanks to the automated detection, but it also gave them time to reflect on any potentially troublesome modules. For quality assurance teams, it has therefore become a crucial step. Software defect prediction looks forward to evaluating error-prone modules likely to contain bugs. Conclusion: Prior information can definitely align the teams with deploying more and more quality assurance checks on predicted modules. Software metrics are the most important component for defect prediction if we consider the different underlying aspects that define the defective module. Later we deployed our refined approach in which we targeted the metrics to be considered.
Security Attacks and Share Valuations

Abstract Dr Adrian Ford MBCS explores how markets react to information security incident announcements about publicly listed companies. Three types of events are investigated: data breaches, GDPR infringement fines and CISO appointments.
Component Training of Turbo Autoencoders

Isolated training with Gaussian priors (TGP) of the component autoencoders of turbo-autoencoder architectures enables faster, more consistent training and better generalization to arbitrary decoding iterations than training based on deep unfolding. We propose fitting the components via extrinsic information transfer (EXIT) charts to a desired behavior which enables scaling to larger message lengths ($k \approx 1000$) while retaining competitive performance. To the best of our knowledge, this is the first autoencoder that performs close to classical codes in this regime. Although the binary cross-entropy (BCE) loss function optimizes the bit error rate (BER) of the components, the design via EXIT charts enables to focus on the block error rate (BLER). In serially concatenated systems the component-wise TGP approach is well known for inner components with a fixed outer binary interface, e.g., a learned inner code or equalizer, with an outer binary error correcting code. In this paper we extend the component training to structures with an inner and outer autoencoder, where we propose a new 1-bit quantization strategy for the encoder outputs based on the underlying communication problem. Finally, we discuss the model complexity of the learned components during design time (training) and inference and show that the number of weights in the encoder can be reduced by 99.96 %.
METHOD OF BUILDING ENSEMBLES OF MODELS FOR DATA CLASSIFICATION BASED ON DECISION CORRELATIONS

The scientific work highlights the problem of increasing the accuracy of binary classification predictions using machine learning algorithms. Over the past few decades, systems that consist of many machine learning algorithms, also called ensemble models, have received increasing attention in the computational intelligence and machine learning community. This attention is well deserved, as ensemble systems have proven to be very effective and extremely versatile in a wide range of problem domains and real-world applications. One algorithm may not make a perfect prediction for a particular data set. Machine learning algorithms have their limitations, so creating a model with high accuracy is a difficult task. If you create and combine several models by combining and aggregating the results of each model, there is a chance to improve the overall accuracy, this problem is dealt with by ensembling. The basis of the information system of binary classification is the ensemble model. This model, in turn, contains a set of unique combinations of basic classifiers – a kind of algorithmic primitives. An ensemble model can be considered as some kind of meta-algorithm, which consists of unique sets of machine learning (ML) classification algorithms. The task of the ensemble model is to find such a combination of basic classification algorithms that would give the highest performance. The performance is evaluated according to the main ML metrics in classification tasks. Another aspect of scientific work is the creation of an aggregation mechanism for combining the results of basic classification algorithms. That is, each unique combination within the ensemble consists of a set of basic models (harbingers), the results of which must be aggregated. In this work, a non-hierarchical clustering method is used to aggregate (average) the predictions of the base models. A feature of this study is to find the correlation coefficients of the base models in each combination. With the help of the magnitude of correlations, the relationship between the prediction of the classifier (base model) and the true value is established, as a result of which space is opened for further research on improving the ensemble model (meta-algorithm)
New Quantum Color Codes Based on Hyperbolic Geometry

In this paper, hyperbolic geometry is used to constructing new quantum color codes. We use hyperbolic tessellations and hyperbolic polygons to obtain them by pairing the edges on compact surfaces. These codes have minimum distance of at least and the encoding rate near to which are not mentioned in other literature. Finally, a comparison table with quantum codes recently proposed by the authors is provided.
State Space Explosion Mitigation for Large-Scale Attack and Compliance Graphs Using Synchronous Exploit Firing

Attack and compliance graphs are useful tools for cybersecurity and regulatory or compliance analysis. Thgraphs represent the state of a system or a set of systems, and can be used to identify all current or future ways the systems are compromised or at risk of violating regulatory or compliance mandates. However, due to their exhaustiveness and thorough permutation checking, these graphs suffer from state space explosion - the graphs rapidly increase in the total number of states, and likewise, their generation time also rapidly increases. This state space explosion in turn also slows the analysis process. This work introduces a mitigation technique called synchronous firing, where graph users and designers can prevent the generation of infeasible states by firing exploits simultaneously through joining inseparable features like time. This feature does not invalidate the integrity of the resulting attack or compliance graph by altering the exhaustiveness or permutation checking of the generation process, but rather jointly fires exploits through their defined inseparable features.
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs

This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The study adapts generic augmentation techniques such as shuffling, back translation, and paraphrasing and proposes new data augmentation techniques such as Noun-Verb Substitution, target-lemma replacement and Actor-Action Substitution for software requirement texts. A comprehensive empirical analysis is conducted on six software text datasets to identify conflicts and duplicates among sentence pairs. The results demonstrate that data augmentation techniques have a significant impact on the performance of all software pair text datasets. On the other hand, in cases where the datasets are relatively balanced, the use of augmentation techniques may result in a negative effect on the classification performance.
HQA-Data: A historical question answer generation dataset from previous multi perspective conversation

This data article contains a quality assurance dataset for training the chatbot and chat analysis model. This dataset focuses on NLP tasks, as a model that serves and delivers a satisfactory response to a user's query. We obtained data from a well- known dataset known as "The Ubuntu Dialogue Corpus" for the purpose of constructing our dataset. Which consists of about one million multi-turn conversations containing around seven million utterances and one hundred million words. We derived a context for each dialogueID from these lengthy Ubuntu Dialogue Corpus conversations. We have generated a number of questions and answers based on these contexts. All of these questions and answers are contained within the context. This dataset includes 9364 contexts, 36,438 question-answer pairs. In addition to academic research, the dataset may be used for activities such as constructing this QA for another language, deep learning, language interpretation, reading comprehension, and open-domain question answering. We present the data in raw format; it has been open sourced and publicly available at https://data.mendeley.com/datasets/p85z3v45xk.
A novel word-graph-based query rewriting method for question answering

Purpose Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs. Design/methodology/approach This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences. Findings The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem. Originality/value The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.