Reviewerly/exHarmony huggingface dataset:
A Method of Knowledge Modeling for Emergency Management in Information Service Domain

In order to do a good job in providing information services for emergency response to emergencies, efficiently handle and respond to various emergencies. This paper presents a method of knowledge modeling for emergency management in information service domain. The ontology of emergency information service field is researched. The hierarchical and non-hierarchical relationships between classes which in the ontology of information service field is analyzed. The construction of the ontology model of the emergency disposal information service for blocking the river bank breach is investigated. The processe of constructing the ontology model of the emergency response is presented in detail. The experimental results show that an ontology model of emergency response in the information services field is able to describe the relationship between information service activities, information resources, and combat forces in the emergency response process. Besides, it can obtain the inner information of the relationships between service activities through reasoning and make predictions about the requirements for information resource in emergency managements. This method has potential to provide auxiliary decision support for the intelligent development of information service work.
An Opinion Summarization-Evaluation System Based on Pre-trained Models

As social media appeal more frequently used, the task of extracting the mainstream opinions of the discussions arising from the media, i. e. opinion summarization, has drawn considerable attention. This paper proposes an opinion summarization-evaluation system containing a pipeline and an evaluation module for the task. In our algorithm, the state-of-the-art pre-trained model BERT is fine-tuned for the subjectivity analysis, and the advanced pre-trained models are combined with traditional data mining algorithms to gain the mainstreams. For evaluation, a set of hierarchical metrics is also stated. Experiment result shows that our algorithm produces concise and major opinions. An ablation study is also conducted to prove that each part of the pipeline takes effect significantly.
The Effect of Teacher’s Corrective Feedback Through Online Conferencing on Elementary Students’ English-Speaking Confidence

The purpose of this study is to investigate the effect of teacher's corrective feedback through online conferencing on elementary students' English-speaking confidence. This study was conducted for 4 months from August to December 2020. There were 6 participants, aged 8 to 13 enrolled in a private education institute where they attended English classes using mobile devices. During this case study, the students were asked to use English, learners’ target language when interacting with their teacher. When learners struggled to understand the teacher’s English instructions, the teacher guided them in Korean. All the classes were video-recorded and transcribed by the teacher. Data were analyzed in order to examine the progress of participants’ voluntary English production stimulated by having conversational interactions with the teacher. The findings were as follows. First, participants’ anxiety level was high in the beginning. Second, they were able to speak English words, give their opinions in English, and join the conversation in English with the teacher. Lastly, feedback from the teacher through conversational interactions helped learners understand how to speak in English better and build up confidence. As a result, it is necessary to interact with the teachers and peers using learners’ target language to improve English communication skills.
Secret and Trustable Communication Channel over Blockchain Public Ledger

Blockchain public ledger is a trusted medium that provides incorruptible data storage and traceable transactions. However, due to its public verifiability, it is hard to compromise two functionalities at once, a covert channel and a verifiable communication medium. The existing methods are either vulnerable to statistical analysis or lack trustable delivery notifications due to off-chain message delivery. This paper fills the gap by proposing a Multi-addresses Random Set Encoding (MaRSE) to increase message covertness by retaining the natural transactions pattern and resisting statistical attacks. The method uses on-chain message delivery to preserve blockchain-based verification that provides a trustable communication medium for the communicating parties. As a result, our proposed method is robust to a brute force attack due to its unpredictable transaction sequence and high number possibility of addresses combinations. The proposed system has been implemented on Ethereum rinkeby networks with the stegano-transactions being recorded within block numbers 4932833 to 4932893.
DocTalk: Combining Dependency-Based Text Graphs and Deep Learning into a Practical Dialog Engine

Today’s deep learning dominates the field of natural language processing (NLP) with text graph-based approaches being another promising approach. However, both have inherent weaknesses. We present our system called DocTalk that brings together a model that combines the strength of the two approaches. DocTalk’s symbiotic model widens its application domain, enhanced with automatic language detection and effective multilingual summarization, keyword extraction, and question answering on several types of documents. Taking advantage of DocTalk’s flexibility, we built it into a dialog engine, coupled with an easy-to-use web interface.
Enhancement of Indistinct Sequence Stratigraphic Correlations Using Geochemical Signatures: An Example from the Paleozoic Successions Saudi Arabia

Abstract The Paleozoic sequence in Kingdom of Saudi Arabia (KSA) represents a thick succession of carbonate, shale, and siltstone/sandstone lithologies that were deposited in a range of environments. This succession hosts abundant unconventional reservoirs with huge amount of natural gas reserves. However, similarity in lithologies and poor biostratigraphic constrains are making facies analysis, stratigraphic correlation, and geosteering applications problematic. In this study, we documented the use of an automated statistical protocol for the identification of chemofacies and correlatable chemozones within highly homogenous formations.
A Method for Dataset Labeling for Activity Recognition in Videos

Social Sciences and Demographics are in search of multiples ways to analyze person actions besides global factors like the environment. This paper aims to propose a protocol to construct a dataset from videos, then applying it to build a dataset of Peruvian News. Finally, we evaluate it using different Machine Learning (ML) methods, achieving an accuracy ranging 85% and 89%.
Analisis Campuran Tanah Liat dan Batu Kapur Bukit Pecatu Sebagai Instrumen Pemanen dan Penampung Air Hujan

This research originated from the large number of limestone mining in the Pecatu Hill area and it caused deep niches. On the other hand, the problem of water scarcity is the main thing, because the characteristics of karst rocks can escape, dissolve and absorb water. The condition of the ex-niche mining and water shortages are two different problems. But in this study, trying to combine these problems into a solution with the aim of making experiments that can make limestone from Pecatu Hill have the potential to hold water for a long time in the form of a miniature reservoir model. This miniature reservoir model is made with a mixture of clay suspensions of various concentrations (0%, 5%, 10% and 15%). This miniature reservoir model is tested for discharge and absorption of water which is assumed to be rainwater puddle to determine the ability to absorb rainwater. The result of this test is that the miniature reservoir with a mixture of 15% clay suspension has the ability to withstand a decrease in rainwater shrinkage of 0.04L / minute and has a small water absorption capacity of 10.91% compared to other concentrations. This shows that the miniature limestone reservoir of Pecatu Hill with a mixture of 15% clay suspension has potential as an instrument for harvesting and collecting rainwater. So this is expected to be utilized by the villagers of Pecatu Hill as an instrument for harvesting and collecting rainwater.
Application Research on Integration Service of Library Information Resources Using Linked Data

To improve the efficiency of library users to find information, this paper analyzes the resource fusion method based on ontology and associated data to solve semantic heterogeneity, and puts forward a method of semantic fusion of library digital resources based on linked data. This article makes up for the deficiency of the traditional bibliographic resources integration model based on the related bibliographic resources, and establishes the integrated bibliographic resources model with related bibliographic resources. At the same time, using the related data query language SPARQL and Java programming, the process of searching bibliographic attributes from bibliographic associated data sets is explored in detail. Finally, the semantic relationship and access path between various types of data and data in the digital library are established, and the problem of forming a good service operation mechanism is proposed. The system running results show that the scheme can meet the needs of future semantic web users, and it is conducive to the effective use of digital library information resources.
Influences and Challenges of Algorithmic Push Regarding Sports News: A Research Based on Analytic Hierarchy Process

With its extensive use in today’s new media industry, the algorithmic push technology is also widely used in sports news video, sports information, and sports-related self-media. It can be said that the combination of algorithmic push technology and sports news media breaks through the traditional mode of sports news production and making, exerting a profound impact on the communication of sports news. This paper first reviewed the influence of algorithmic push exerted on news communication, analyzed the new relationship between algorithmic push and the audience of sports news and the influence of algorithmic push on the audience’s behavior on this basis, and finally put forward suggestions to optimize the algorithmic push mechanism.
Extracting domain behaviors through multi-criteria, polymorphism-inspired variability analysis

Extracting domain knowledge is important for different purposes, including development of new systems and maintenance of existing systems in the domain. Automatically supporting this task is challenging; most existing methods assume high similarity of variants which limits reuse of the generated domain artifacts, or provide very low-level features which hinder domain structure and behavior. In this paper, we propose a holistic method for extracting domain knowledge in the form of feature models that capture mandatory, optional and variant domain behaviors. Particularly, the method gets low-level implementations, applies polymorphism-inspired mechanisms and multi-criteria decision making for generating candidate domain behaviors, utilizes machine learning techniques to classify local, global and irrelevant domain behaviors, and finally analyzes dependencies and presents the outcomes in the form of feature models. The approach is evaluated on two datasets: one of open-source video games, named apo-games, following a clone-and-own scenario; and the other on variants of a monopoly game, simulating a scenario of independent development of similarly behaving components. • Extracting domain knowledge may improve system development and maintenance. • Domain behaviors can be automatically extracted from implementations of systems. • In clone-and-own scenarios, the domain infrastructure can be automatically drafted. • In independent development scenarios, a conceptual domain model can be created.
Duo Features with Hybrid-Meta-Heuristic-Deep Belief Network Based Pattern Recognition for Marathi Speech Recognition

Marathi speech recognition is challenging due to the many dialects, variability in pronunciation, and the limited size of speech corpus available for developing a speech recognition system. This work addresses the critical issues in developing a speech recognition system for the Marathi Language. The paper evaluates various approaches for feature extraction and pattern recognition and proposes optimized methods for feature extraction and pattern recognition of the Marathi language. Evaluation is classified into two parts—Feature Extraction Techniques and Pattern Recognition Techniques. In feature extraction, MFCC and spectral features are evaluated, and their results are compared for analysis purposes using six measures. In pattern recognition, DBN is optimized with different techniques such as WOA, GWO, CBBO, and ROA and analyzed using six performance measure parameters. Finally, the duo feature hybrid algorithm for feature extraction and new pattern recognition approach RCBO-DBN has been proposed, and its performance is compared with earlier techniques.
Features Applications Visual Teaching Facilities in Biology Class

In the process of teaching biology, the use of visual teaching aids plays an important role. Along with the content, form and various teaching methods, visual aids are an integral part of the educational process at school. The article discusses the features of using visual teaching aids as one of the ways to increase the interest of schoolchildren in the Biology subject. The results of experimental work carried out in one of the secondary schools in the city of Elabuga of the Republic of Tatarstan are presented. Based on the results of the work, appropriate conclusions were drawn about the effectiveness of the use of visualization tools in the educational process.
Iterated Matrix Reordering

Heatmaps are a popular data visualization technique that allows to visualize a matrix or table in its entirety. An important step in the creation of insightful heatmaps is the determination of a good order for the rows and the columns, that is, the use of appropriate matrix reordering or seriation techniques. Unfortunately, by using artificial data with known patterns, it can be shown that existing matrix ordering techniques often fail to identify good orderings in data in the presence of noise. In this paper, we propose a novel technique that addresses this weakness. Its key idea is to make an underlying base matrix ordering technique more robust to noise by embedding it into an iterated loop with image processing techniques. Experiments show that this iterative technique improves the quality of the matrix ordering found by all base ordering methods evaluated, both for artificial and real-world data, while still offering high levels of computational performance as well.
An Insight into the Existing Reversible Arithmetic and Logic Unit Designs

International Technology Roadmap for Semiconductors-ITRS2.0 predicts an end to traditional scaling and shrinking of chips by 2028. The future depends on the alternative technology to fill the gap and perform better than the existing technology. There are numerous technologies emerging, one among them being the reversible logic is fast gaining the importance due to the quantum technology for minimal dissipation of energy whose operation is reversible in nature. Arithmetic and logic operations are the core of any processing system and its importance is found in all the modern devices. Reversible logic assures to overcome the limitations of the current technologies and several designs have been proposed to build optimized, efficient reversible arithmetic and logic unit. This paper makes an effort to compare the existing ALU designs based on the different design approaches.
Joint Distribution Adaptation via Wasserstein Adversarial Training

This paper considers the unsupervised domain adaptation problem, in which we want to find a good prediction function on the unlabeled target domain, by utilizing the information provided in the labeled source domain. A common approach to the domain adaptation problem is to learn a representation space where the distributional discrepancy of the source and target domains is small. Existing methods generally tend to match the marginal distributions of the two domains, while the label information in the source domain is not fully exploited. In this paper, we propose a representation learning approach for domain adaptation, which is addressed as JODAWAT. We aim to adapt the joint distributions of the feature-label pairs in the shared representation space for both domains. In particular, we minimize the Wasserstein distance between the source and target domains, while the prediction performance on the source domain is also guaranteed. The proposed approach results in a minimax adversarial training procedure that incorporates a novel split gradient penalty term. A generalization bound on the target domain is provided to reveal the efficacy of representation learning for joint distribution adaptation. We conduct extensive evaluations on JODAWAT, and test its classification accuracy on multiple synthetic and real datasets. The experimental results justify that our proposed method is able to achieve superior performance compared with various domain adaptation methods.
Key frame extraction for Human Action Videos in dynamic spatio-temporal slice clustering

Abstract Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. This paper proposed a key frame extraction algorithm based on dynamic spatio-temporal slice clustering. This algorithm firstly uses the dynamic spatio-temporal slice position selection method based on the human mask heat map to calculate the position of slice to realize the dynamic selection of slice positions, then complete the extraction of spatio-temporal slice images. After clustering the spatio-temporal slice images, this method extracts key frames according to the clustering results. The experimental results prove the validity of spatio-temporal slice location selection method, the proposed algorithm can effectively solve the problems of information redundancy and key information missing in existing methods. We conduct experiments on a challenging human action dataset UCF101 and show that our method can detect key frames with high accuracy.
Wavelet shrinkage in nonparametric regression models with positive noise

Wavelet shrinkage estimators are widely applied to analyse datasets in wavelet domain from several fields of science. They typically act by reducing magnitudes of empirical coefficients in a discrete wavelet transformation to estimate wavelet coefficients. In nonparametric regression problems, most of the shrinkage rules are derived from models composed of an unknown function with additive Gaussian noise. Although Gaussian noise assumption is reasonable in several real data analysis, mainly for large sample sizes, it is not general. Contaminated data with positive noise can occur in practice and nonparametric regression models with positive noise bring challenges in wavelet shrinkage point of view. In this sense, this work proposes bayesian shrinkage rules to estimate wavelet coefficients from a nonparametric regression framework with additive and strictly positive noise under exponential and lognormal distributions. Computational aspects are discussed and simulation studies to analyse the performances of the proposed shrinkage rules and compare them with standard techniques are done. An application for winning times Boston Marathon dataset is also provided.
Research on student assessment of secondary vocational schools in the Internet era

In today’s advanced Internet age, the assessment of students in secondary vocational schools still uses the traditional paper-and-pencil test, which has the problems of complicated workload, low efficiency and so on. The MAP Growth assessment system developed by the Northwest Evaluation Association, can provide with a multi-dimensional and personalized assessment for each student. Furthermore, its assessment report can be automatically generated for different subjects after 24 hours. These are the points lacking in traditional student evaluation. Based on the characteristics of the MAP Growth system, this research conducts an in-depth analysis of the problems existing in the current student assessment of secondary vocational schools, and aims to promote the reform of vocational education.
Improving machine learning model performance on detection of network infiltration

Network Intrusion Detection research datasets concentrate on recognition of malignant traffic and often present benign traffic as one class. Some classes with extremely low representation of flows in intrusion detection datasets can still be learned and discovered with high precision. However the Infiltration class, comprising 1% in CSE-CIC-IDS2018 dataset, was found to partially overlap with the Benign class. Whereas benign internet traffic can be classified into sub-classes using machine learning. A question is asked in this research whether it is possible to be more specific using machine learning methods to discover which Benign sub-classes are overlapping with Infiltration. This research indicates an overlap with social networking and browsing.In this research a method, for classification of a second level of granularity of traffic is proposed, and an experiment is designed to find a class instance space subset overlap in an imbalanced dataset with temporal aggregate statistics features of major classes. It allows for further indication of features, where oversampling could have a positive impact towards predicting the rare class in a search of decision boundary criteria which can be added from information of other classes by overlap analysis.
The Design of Stroke EEG Channel Selection System Using Spatial Selection Method

Stroke can be interpreted as a dysfunction of the nervous system that occurs suddenly and caused by blockage of blood vessels in the brain. Generally, the effort used to reduce stroke patients is the diagnostic method using Magnetic Resonance Imaging (MRI). However, the cost of examination using the MRI method is relatively expensive and not portable. One solution to overcome this problem is to use an Electroencephalograph (EEG) device to detect stroke signals in the brain that measure electrical activity detecting abnormalities in the brain. This action uses special sensors, namely electrodes attached to the head and connected to the computer. In previous research, EEG stroke signal processing was carried out using the Brain Symmetry Index and Hilbert Huang Transform (BSI-HHT) methods. However, this study did not specifically discuss channel selection in EEG stroke signals. Given these problems, in this study, the authors will process the EEG stroke signal using the modified Spatial Selection method using the Fast Fourier Transform (FFT) method through the active channel composition configuration so that it can be processed to obtain relevant results. Furthermore, the classification process is carried out using the k-Nearest Neighbor (k-NN) and Extreme Learning Machine (ELM) methods. Implementing the k-Nearest Neighbor (k-NN) classification shows that the spatial selection method can find the suitable channel composition with the same accuracy results as normal data in several areas. In contrast, the ELM classification can increase accuracy by 2% greater than normal data in the high mean area with a few channel compositions.