Reviewerly/exHarmony huggingface dataset:
Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership

Due to the development of medical data, a large amount of clinical data has been generated. These unstructured data contain substantial information. Extracting useful knowledge from this data and making scientific decisions for diagnosing and treating diseases have become increasingly necessary. Unstructured data, such as in the Marketplace for Medical Information in Intensive Care III (MIMIC-III) data set, contain several ambiguous words that demonstrate the subjectivity of doctors, such as descriptions of patient symptoms. These data could be used to further improve the accuracy of medical diagnostic system assessments. To the best of our knowledge, there is currently no method for extracting subjective words that express the extent of these symptoms (hereinafter, "degree words").Therefore, we propose using the fuzzy c-means (FCM) method and Gaussian membership to quantify the degree words in the clinical medical data set MIMIC-III.First, we preprocessed the 381,091 radiology reports collected in MIMIC-III, and then we used the FCM method to extract degree words from unstructured text. Thereafter, we used the Gaussian membership method to quantify the extracted degree words, which transform the fuzzy words extracted from the medical text into computer-recognizable numbers.The results showed that the digitization of ambiguous words in medical texts is feasible. The words representing each degree of each disease had a range of corresponding values. Examples of membership medians were 2.971 (atelectasis), 3.121 (pneumonia), 2.899 (pneumothorax), 3.051 (pulmonary edema), and 2.435 (pulmonary embolus). Additionally, all extracted words contained the same subjective words (low, high, etc), which allows for an objective evaluation method. Furthermore, we will verify the specific impact of the quantification results of ambiguous words such as symptom words and degree words on the use of medical texts in subsequent studies. These same ambiguous words may be used as a new set of feature values to represent the disorders.This study proposes an innovative method for handling subjective words. We used the FCM method to extract the subjective degree words in the English-interpreted report of the MIMIC-III and then used the Gaussian functions to quantify the subjective degree words. In this method, words containing subjectivity in unstructured texts can be automatically processed and transformed into numerical ranges by digital processing. It was concluded that the digitization of ambiguous words in medical texts is feasible.
Machine Learning Implementation for Refactoring Prediction

Refactorings improve the internal organization of object-oriented software project without altering the functionality to address the problem of architectural degradation. The application of refactoring leads to increased software quality and maintainability. However, finding refactoring chances is a complex topic that affects both developers and researchers. In a recent study, machine learning methods demonstrated significant promise for resolving this issue. Model refactoring prevents erosion of the program architecture at an early stage of the model-driven engineering paradigm-compliant software development project. However, difficulties such as variable data set distribution and the availability of duplicate and irrelevant variables hamper the efficacy of refactoring prediction models. We aim to develop a model for refactoring prediction using several machine learning classifiers, data sampling techniques, and feature selection techniques.
Scenario-based requirements analysis for Chinese new train control system

In order to meet the needs of railway operations in western China, the development of Chinese New Train Control System (CTCS-N) has been launched. To obtain complete and correct system requirements for CTCS-N early in system development, this paper applies the scenario-based requirements analysis method in software engineering to the requirements analysis of CTCS-N. This method takes the system operation scenario as input, analyzes the behaviors of the scenario based on ontology theory, and extracts the system requirements contained in the scenario based on SWRL (Semantic Web Rule Language) reasoning rules. Furthermore, the timed automata (TA) network model is constructed for requirement verification. Based on the design of the CTCS-N architecture, the above method is used to analyze the requirements based on the train integrity monitoring scenario as an example. The result shows that the method can effectively obtain the system requirements and provides a reference for the requirements development of CTCS-N.
Long-memory parameter estimation based on fractional spline wavelets

Fractional splines extend Schoenberg's B-splines to fractional orders, which were proven to fulfill all requirements to form wavelet bases. Moreover, some of these fractional splines wavelets act approximately as fractional difference operators for signals that are essentially lowpass and concentrated around the origin, which makes them helpful in analyzing series with fractal behavior. In this article, we propose a novel estimator for the long memory parameter of a time series based on the fractional spline discrete wavelet transform (FrDWT), using the fact that it works approximately as a fractional differentiator. We perform simulations and examples to illustrate the proposed method. The proposed estimator outperforms traditional and widely used estimators in simulated data. We also show that, as the sample size increases, the bias in the simulation tends to decrease, as well as the variance, which might indicate that the estimator is consistent.
Multilingual Natural Language Understanding for the FPT.AI Conversational Platform

This paper presents a multilingual natural language understanding model which is based on BERT and ELECTRA neural networks. The model is pre-trained and fine-tuned on large datasets of four languages: Indonesian, Malaysian, Japanese and Vietnamese. Our fine-tuning method uses an attentional recurrent neural network instead of the common fine-tuning with linear layers. The proposed model is evaluated on several standard benchmark datasets, including intent classification, named entity recognition and sentiment analysis. For Indonesian and Malaysian, our model achieves the same or higher results compared to the existing state-of-the-art IndoNLU and Bahasa ELECTRA models for these languages. For Japanese, our model achieves promising results on sentiment analysis and two-layer named entity recognition. For Vietnamese, our model improves the performance of two sequence labeling tasks including part-of-speech tagging and named entity recognition compared to the state-of-the-art results. The model has been deployed as a core component of the commercial FPT.AI conversational platform, effectively serving many clients in the Indonesian, Malaysian, Japanese and Vietnamese markets–the platform has served 62 million API requests in the first five months of 2022 for chatbot services. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> including requests deployed for on-premise contracts.
Losing a Head in Grammar Extraction

The treebank corpus is a collection of the tree that represents a sentence constituency and dependency relation. We are motivated to extract grammar rules from the treebank, that is to decompose the tree data structure and to find grammar rules. After the extraction, we need to validate the adequacy of the grammar so that we inspect the generative power of the obtained grammar. In this phase, the syntactic head is a significant feature, however, in the obtained grammar the head information is missing. Hence, we propose to supplement the lost head information with the type-raising rule of categorial grammar (CG). We extend the same issue to combinatory categorial grammar (CCG) and solve it using the generalized type-raising. Furthermore, we verify our grammar by the formal proof written in the proof assistant system, Isabelle/ HOL.
Controlling Weight Update Probability of Sparse Features in Machine Learning

In machine learning, the feature frequency in learning data can be used for a value of the feature, and in this case, sparse feature is likely to create overfitting problems in the weight optimization process. This is called sparse data problem, and this paper proposes a method that reduce the probability of weight update as the feature is sparse. We experimented with this method in four Natural Language Processing tasks, and the experiment results showed that this method had positive effects on all tasks. On average, this method had the effect of reducing 8 per 100 errors. Also it reduced the number of weight updates, therefore the learning time was reduced to 81% in Named Entity Recognition task.
A multi-population coevolutionary approach for Software defect prediction with imbalanced data

This paper proposes a cooperative coevolutionary approach namely COESDP to the software defect prediction (SDP) problem. The proposed method consists of three main phases. The first one conducts data preprocessing including data sampling and cleaning. The second phase utilizes a multi-population coevolutionary approach (MPCA) to find out optimal instance selection solutions. These first two phases help to deal with the imbalanced data challenge of the SDP problem. While the data sampling method aids in the creation of a more balanced data set, MPCA supports in the elimination of unnecessary data samples (or instances) and the selection of crucial instances. The output of phase 2 is a set of different optimal solutions. Each solution is a way of selecting instances from which to create a classifier (or weak learners). Phase 3 utilizes an ensemble learning method to combine these weak learners and produce the final result. The proposed algorithm is compared with conventional machine learning algorithms, ensemble learning algorithms, computational intelligence algorithms and an other multi-population algorithm on 6 standard SDP datasets. Experimental results show that the proposed method gives better and more stable results in comparison with other methods and it can tackle the challenge of imbalance in the SDP data.
Encapsulated and Anonymized Network Video Traffic Classification With Generative Models

In recent years, anonymization and encrypted web applications have become increasingly popular among the public. Although the Internet communication process already has complete privacy protection, network security technologies continue to evolve, and technologies for exploring the commercial behavioral characteristics of network users' fingerprints are also accelerating. In this paper, we picked benchmark public datasets, including anonymously viewing video streaming traffic using The Onion Router (Tor) network technology, establishing tunnel encapsulated video streaming packets using Virtual Private Network (VPN) technology, and viewing video streaming traffic scenarios in normal mode. We combined video streaming application types and names to construct a new dataset for multi-class classification problem. Unlike the frequently used classification discrimination models, we used generative models, including Restricted Boltzmann Machines (RBM) and Deep Belief Networks (DBN), having an advantage of features extraction learning to construct models for probabilistic judgments, and better performance, that has been verified and supported in several works. The experimental results show the classification accuracy is as high as 0.94 for eight categories and 0.97 for three categories. Its overall classification performance is good, and different classification performance metrics are balanced.
Health Performance Interval Prediction of Pumped Storage Unit Based on Multi-objective Optimization

The health performance of pumped storage unit (PSU) affects the stable and safe operation of pumped storage station and even the power system. Thus, it is essential to predict the health tendency accurately and helpful to achieve predictive maintenance as well. An interval prediction model based on multi-objective optimization is proposed in this paper. To effectively obtain the operating characteristic of PSU, the health state model is developed based on Gaussian process regression (GPR) and the monitoring data. Then, to comprehensively quantify the health degree, an integrate health index (IHI) is developed with entropy weight to fuse the healthy information of different objects. Taken the prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW) as objective constraints, the global optimization strategy is proposed based on kernel extreme learning machine (KELM) and multi-objective particle swarm optimization (MOPSO). In the final, the comparative experiments are conducted with the data collected from a PSU in China. The results show that the proposed model can deduce the health interval with reliability and accuracy.
Reconstructing quantum states via unambiguous state discrimination

Abstract In this paper, we introduce an analytical framework for the reconstruction of quantum states. The reconstruction of an unknown quantum state requires the information of a complete set of observables, obtained through experimental measurements of Hermitian operators usually defined as positive-operator-valued measures (POVMs). The scheme involves a single-qubit unambiguous state discrimination POVM, which can be generalized to perform n-qubit measurements. We also use maximum likelihood estimation as a method in the reconstruction of the density matrix from experimental data and show that the expected value of the cleaner is independent of the parameter of the density operator.
ATPM-REAP: A Simple and Efficient Address Tracking and Parsing for Vietnamese Real Estate Advertisement Posts

Real estate is an enormous and essential field in many countries. Taking advantage of helpful information from real estate advertisement posts can help better understand the market condition and explore other vital insights, especially for the Vietnamese market. It is worth noting that in the representative information of real estate, the address or the location is required information. However, there are different ways to write down the address information in Vietnam. For this reason, detecting the relevant text representing the address information from real estate advertisement posts becomes an essential and challenging task. This paper investigates the address detecting and parsing task for the Vietnamese language. First, we create a dataset of real estate advertisements having 16 different attributes (entities) of each real estate and assign the correct label for each entity detected during the data annotation process. Then, we propose a practical approach for detecting locations of possible addresses inside one specific real estate advertisement post and then extract the localized address text into four different levels of the address information: City/Province, District/Town, Ward, and Street. The experiment results indicate that the ${\mathrm {PhoBERT}}_{bas\mathrm{e}}$ model achieves the best performance with an F1-score of 0.8195. Finally, we compare our proposed method with other approaches and achieve the highest accuracy results for all levels as follows: City/Province (0.952), District/Town (0.9482), Ward (0.9225), Street (0.8994), and the combined accuracy of correctly detecting all four levels is 0.8367.
Preliminary hydro-geochemistry characterization of the complex geological framework of the Coreca area (Calabria, South Italy).

Hydrogeochemical characterization combined with statistical methods has been used to investigate groundwater quality and related geochemical processes in the complex geological framework of Coreca (Calabria, South Italy). Coreca is characterized by a peculiar geological setting that affects the groundwater quality mainly exploited for irrigation use. Two groups of waters were identified: Ca-HCO3 waters strongly controlled by the interaction with Ca-rich phases; Mg-HCO3 waters related to the interaction of meteoric water with the metamorphic Units. In Mg-HCO3 group was identified a good correlation between Cr and Ni (not observed in Ca waters) and a negative correlation between Cr, Ca and Al in agreement with direct interaction with ultramafic rocks characterized by low concentration in CaO and Al2O3. The concentration of major and trace elements has been compared with the Italian law limit values and the drinking water guidelines provided by the World Health Organization (WHO). Only samples S20, S25 and S29 showed Mn and Ni concentration higher than the Italian law threshold. The study allows defining how a multidisciplinary approach represents a useful tool to trace the factors controlling the groundwater evolution and quality especially in areas with an articulate geological and hydrogeological asset.
Systems improvement and energy savings program for the existing conventional extended aerobic treatment of domestic wastewater in compliance of DAO 2016-08 new general effluent regulations

This is a system improvement and energy savings program for a Sewage Treatment Plant treating wastewater coming from a shopping mall operation. Wastewater from shopping mall is considered domestic wastewater, which needs a big volume of water, and produced big amount of wastewater. Generators of wastewater such as in this case, from shopping mall, is mandated to be responsible for the collection, treatment of wastewater, and the ultimate disposal of the treated wastewater, as well as the separated solids, in a manner that is safe, and within the new effluent regulations as provided under DENR Administrative Order No. 2016-08. Under this new effluent regulations which only takes effect on June 2016, shopping mall is now mandated to meet stringent parameters to comply, particularly on Nitrate, Phosphate, Ammonia and Surfactants. To comply the new general effluent regulations, there were innovative solutions and energy programs implemented, resulting of savings in water and energy.
A Low-Cost Device for the Monitoring and Detection of Falls in Older Adults

According to the World Health Organization (WHO), falls are the second cause of death due to accidental injuries, and older adults are the ones who suffer the most from them. In Ecuador, there are about 1,300,000 older adults, and falls are a major problem for their quality of life. For this reason, in this article, we present a low-cost prototype system for the monitoring and detection of falls, with the aim of providing support for the care of older adults. This tool is based on a module that applies computer vision and image processing techniques, convolutional neural networks (CNNs) and Web and mobile applications. They allow the monitoring and control of falls. To test the operation of the system, tests were carried out with fifteen volunteers. It was determined that the system managed to correctly detect 80% of fall-related events.
The Implementation of New Public Governance Through Blockchain: A Delphi-based analysis

Collaborative public service delivery and blockchain are perceived as important themes for developing public services. Thus far, each topic has generally been discussed separately. Additionally, most research on blockchain is conceptual and empirical work seems scarce to date. This paper seeks to theoretically and empirically substantiate the potentials and challenges of using blockchain to enable the paradigm of New Public Governance. Therefore, a theoretical analysis and a subsequent Delphi-based evaluation are conducted to map the theoretical ideals of New Public Governance against expert perceptions and experiences towards blockchain. Experts from public administrations, IT service providers, and science, who are all specialized in blockchain, contributed to this study. As this qualitative study demonstrates, blockchain can support public and nonpublic stakeholders in their efforts to create public services in co-producing networks. Furthermore, according to the findings of this research, stronger consideration of the mutual alignment between governance, process, and technology level requires more attention.
Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks

The prevalence of brain tumor is quite high. Brain tumor causes critical diseases. Also, brain tumor causes a variety of symptoms in most people. This study aims to segmentation of the tumor in the brain. For this purpose, state-of-art architectures, such as UNet, Attention UNet, Residual UNet, Attention Residual UNet, Residual UNet++, Inception UNet, LinkNet, and SegNet were used for segmentation. 592 magnetic resonance (MR) images were utilized in the training and testing of segmentation architectures. In the comparative analysis, Attention UNet achieved the best predictive performance with a 0.886 dice score, 0.795 IoU score, 0.881 sensitivity, 0.993 specificity, 0.891 precision, and 0.986 accuracy.
RMCL: A deep learning based recursive malicious context learner in social networks

Abstract Propagation of malicious content and its promotion is a recurrent problem prominent in social networks. Detection and interactive labeling of malicious context promotion on‐the‐fly is a very challenging and difficult task due to the lack of complete knowledge about the underlying context. Modern research in this field use neural networks and text encoders to analyze the information dynamically which is inefficient, in terms of time‐space consumed in the overall process. This article proposes an online active learning system to label the data streams on‐the‐fly by sampling them in the form of tweet‐retweet‐follow (trf) sequences in social networks. A heuristically pretrained recursive malicious context learner is fixed for knowledge acquisition, data accommodation and pseudonymization in the form of a transformer in the tree structured recursive neural network. The data stream is trained using recursive bidirectional training to capture long‐term dependencies. The COVID‐19 data streams are selectively sampled on‐the‐fly using psycho linguistic words in the proposed experiment and are labeled deceptive/nondeceptive based on knowledge learned during pretraining. The proposed recursive malicious context learner successfully resolves the problem of on‐the‐fly‐interactive labeling of a dynamically changing data stream.