Reviewerly/exHarmony huggingface dataset:
Model for successful development and implementation of Cyber Security Operations Centre (SOC)

Cyberattacks have changed dramatically and have become highly advanced. This latest phenomenon has a massive negative impact on organizations, such as financial losses and shutting-down of operations. Therefore, developing and implementing the Cyber Security Operations Centre (SOC) is imperative and timely. Based on previous research, there are no international guidelines and standards used by organizations that can contribute to the successful implementation and development of SOC. In this regard, this study focuses on highlighting the significant factors that will impact and contribute to the success of SOC. Simultaneously, it will further design a model for the successful development and implementation of SOC for the organization. The study was conducted quantitatively and involved 63 respondents from 25 ministries and agencies in Malaysia. The results of this study will enable the retrieval of ten success factors for SOC, and it specifically focuses on humans, processes, and technology. The descriptive analysis shows that the top management support factor is the most influential factor in the success of the development and implementation of SOC. The study also contributes to the empirical finding that technology and process factors are more significant in the success of SOCs. Based on the regression test, the technology factor has major impact on determining the success of SOC, followed by the process and human factors. Relevant organizations or agencies can use the proposed model to develop and implement SOCs, formulate policies and guidelines, strengthen human models, and enhance cyber security.
Corpus Creation and Language Identification in Low-Resource Code-Mixed Telugu-English Text

Code-Mixing (CM) is a common phenomenon in multilingual societies.CM plays a significant role in technology and medical fields where terminologies in the native language are not available or known.Language Identification (LID) of the CM data will help solve NLP tasks such as Spell Checking, Named Entity Recognition, Parts-Of-Speech tagging, and Semantic Parsing.In the current era of machine learning, a common problem to the above-mentioned tasks is the availability of Learning data to train models.In this paper, we introduce two Telugu-English CM manually annotated datasets (Twitter dataset and Blog dataset).The Twitter dataset contains more romanization variability and misspelled words than the blog dataset.We compare across various classification models and perform extensive bench-marking using both Classical and Deep Learning Models for LID compared to existing models.We propose two architectures for language classification (Telugu and English) in CM data: (1) Word Level Classification (2) Sentence Level word-byword Classification and compare these approaches presenting two strong baselines for LID on these datasets.
Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)

One of the purposes of earthquake engineering is to mitigate the damages in buildings and infrastructures and, therefore, reduce the impact of earthquakes on society. Seismic ground response analysis refers to the process of evaluating the ground surface motions based on the bedrock motion. On the other hand, deep learning techniques have been developing fast, and they are establishing their application in the civil engineering field. This study proposes two convolutional neural network (CNN) models to estimate the seismic response of the surface based on the seismic motion measured at 100 m level beneath the surface, and selected the one which outperforms the other as the main model. The performances of the main model are compared with those of a physical software SHAKE2000. Twelve sites that include 100 earthquake datasets, whose moment magnitude is higher than 6 and PGA is higher than 0.1 g, were selected. In addition, the corresponding earthquake datasets are used for the CNN model. Whereas the conventional software overestimated the values of the amplitudes for most of the sites, the proposed CNN model predicts fairly well both the values of the amplitudes and the natural periods where responses amplify the most with few exceptions. The proposed model especially outperforms the conventional software when the natural periods range from 0.01 to 0.3 s. For specific sites, the average mean squared errors of the proposed model are even dozens of times lower than those of the conventional physical software.
A Corpus-based Syntactic Analysis of Two-termed Unlike Coordination

Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction.Although coordination has been explored extensively in the linguistics literature, the rules and constraints that govern its structure are still largely elusive and widely debated amongst linguists.This paper presents a study of two-termed unlike coordinations in particular, where the two conjuncts of the coordination phrase form valid constituents but have distinct categories.We conducted a syntactic analysis of the phrasal categories that can be conjoined in such unlike coordinations through a computational corpusbased approach, utilizing the Corpus of Contemporary American English (COCA) as the main data source, as well as the Penn Treebank (PTB).The results show that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination.This research provides new data and perspectives through the use of statistical techniques that can help shape future theories and models of coordination.
The Impact of Familiarity on Visualizations of Spatial Uncertainty

While visualization can support understanding complex phenomena, their effectiveness might vary with the recipient’s familiarity with both the phenomenon and the visualization. The current study contrasted interpretations of simulated hurricane paths using student populations from a high frequency hurricane area versus no local hurricane risk. Non-expert understanding of trajectory predictions was supported via two visualizations: common cones of uncertainty and novel dynamic ensembles. General patterns of performance were similar across the two groups. Participants from the high hurricane risk area did show narrower decision thresholds, in both common and novel visualization formats. More variability was consistently considered possible when viewing the dynamic ensemble displays. Despite greater likelihood of experiences with variability of trajectories outside of forecast paths, greater familiarity tended towards narrower interpretations of the need for evacuations within the variability possible. The results suggest an advantage of dynamic ensembles in grasping uncertainty even in populations familiar with hurricanes.
Fine-grained Factual Consistency Assessment for Abstractive Summarization Models

Factual inconsistencies existed in the output of abstractive summarization models with original documents are frequently presented.Fact consistency assessment requires the reasoning capability to find subtle clues to identify whether a model-generated summary is consistent with the original document.This paper proposes a fine-grained two-stage Fact Consistency assessment framework for Summarization models (SumFC).Given a document and a summary sentence, in the first stage, SumFC selects the top-K most relevant sentences with the summary sentence from the document.In the second stage, the model performs fine-grained consistency reasoning at the sentence level, and then aggregates all sentences' consistency scores to obtain the final assessment result.We get the training data pairs by data synthesis and adopt contrastive loss of data pairs to help the model identify subtle cues.Experiment results show that SumFC has made a significant improvement over the previous state-of-the-art methods.Our experiments also indicate that SumFC distinguishes detailed differences better.
PENGARUH TINGKAT PENDIDIKAN, PENGANGGURAN , DAN KEMISKIAN TERHADAP KRIMINALITAS DI INDONESIA

This study aims to determine and analyze the influence of (1) Level of Education, (2) Unemployment, and (3) Poverty against crime in Indonesia by using the panel regression equation model and using the Fixed Effect Model (FEM) approach. The estimation results show that (1) the level of education has a negative and not significant effect on criminality in Indonesia, (2) unemployment has a negative and significant effect on crime in Indonesia, (3) poverty has a positive and significant influence on crime in Indonesia.This type of research is descriptive and associative. Data type is secondary data. This study uses panel data, which uses 31 provinces in Indonesia using the Fixed Effect Model (FEM) approach.The results of this study indicate that: (1) The level of education has a negative and insignificant influence on crime in Indonesia, (2) Unemployment has a negative and significant effect on crime in Indonesia, (3) Poverty has a positive and significant influence on crime in Indonesia.
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning

Generating informative and appropriate responses is challenging but important for building human-like dialogue systems.Although various knowledge-grounded conversation models have been proposed, these models have limitations in utilizing knowledge that infrequently occurs in the training data, not to mention integrating unseen knowledge into conversation generation.In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation.Unlike traditional approaches that parameterize the specific representation for each entity, EARL utilizes the context of conversations and the relational structure of knowledge graphs to learn the category representation for entities, which is generalized to incorporating unseen entities in knowledge graphs into conversation generation.Automatic and manual evaluations demonstrate that our model can generate more informative, coherent, and natural responses than baseline models.
Comparison of methods for explicit discourse connective identification across various domains

Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles.We here assess the performance on explicit connective identification of four parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang and Lan, 2015; Dis-Sent, Nie et al., 2019; and Discopy, Knaebel and Stede, 2020), along with a simple heuristic.We also examine how well these systems generalize to different datasets, namely newspaper text (PDTB), scientific text (BioDRB), prepared spoken text (TED-MDB) and spontaneous spoken text (Disco-SPICE).The results show that Discopy outperforms the other parse methods in all datasets, with the exception of DiscoSPICE.Moreover, performance drops significantly from the PDTB to all other datasets.We provide a more fine-grained analysis of domain differences and connectives that prove difficult to parse, in order to highlight the areas where gains can be made.
Minimum Distribution Support Vector Clustering

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.
Literature-Augmented Clinical Outcome Prediction

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models. 1 Based on each individual patient's clinical notes, we train language models (LMs) to find relevant papers and fuse them with information from notes to predict outcomes such as in-hospital mortality.We develop methods to retrieve literature based on noisy, information-dense patient notes, and to augment existing outcome prediction models with retrieved papers in a manner that maximizes predictive accuracy.Our approach boosts predictive performance on three important clinical tasks in comparison to strong recent LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%.
Argumentation Mining in Scientific Literature for Sustainable Development

Science, technology and innovation (STI) policies have evolved in the past decade.We are now progressing towards policies that are more aligned with sustainable development through integrating social, economic and environmental dimensions.In this new policy environment, the need to keep track of innovation from its conception in Science and Research has emerged.Argumentation mining, an interdisciplinary NLP field, gives rise to the required technologies.In this study, we present the first STI-driven multidisciplinary corpus of scientific abstracts annotated for argumentative units (AUs) on the sustainable development goals (SDGs) set by the United Nations (UN).AUs are the sentences conveying the Claim(s) reported in the author's original research and the Evidence provided for support.We also present a set of strong, BERT-based neural baselines achieving an f1-score of 70.0 for Claim and 62.4 for Evidence identification evaluated with 10-fold cross-validation.To demonstrate the effectiveness of our models, we experiment with different test sets showing comparable performance across various SDG policy domains.Our dataset and models are publicly available for research purposes 1 .
Devising a method for constructing the optimal model of time series forecasting based on the principles of competition

This paper reports the analysis of a forecasting problem based on time series. It is noted that the forecasting stage itself is preceded by the stages of selection of forecasting methods, determining the criterion for the forecast quality, and setting the optimal prehistory step. As one of the criteria for a forecast quality, its volatility has been considered. Improving the volatility of the forecast could ensure a decrease in the absolute value of the deviation of forecast values from actual data. Such a criterion should be used in forecasting in medicine and other socially important sectors. To implement the principle of competition between forecasting methods, it is proposed to categorize them based on the values of deviations in the forecast results from the exact values of the elements of the time series. The concept of dominance among forecasting methods has been introduced; rules for the selection of dominant and accurate enough predictive models have been defined. Applying the devised rules could make it possible, at the preceding stages of the analysis of the time series, to reject in advance the models that would surely fail from the list of predictive models available to use. In accordance with the devised method, after applying those rules, a system of functions is built. The functions differ in the sets of predictive models whose forecasting results are taken into consideration. Variables in the functions are the weight coefficients with which predictive models are included. Optimal values for the variables, as well as the optimal model, are selected as a result of minimizing the functions built. The devised method was experimentally verified. It has been shown that the constructed method made it possible to reduce the forecast error from 0.477 and 0.427 for basic models to 0.395 and to improve the volatility of the forecast from 1969.489 and 1974.002 to 1607.065
Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame.
JACOB Voice Chatbot Application Using Wit.ai for Providing Information in UMN

In this modern age, chatbots have been widely used as an information provider. The popularity of voice-based chatbots or voice chatbots has also increased with the presence of technologies such as Microsoft Cortana, Amazon Lex, Apple Siri, and Google Assistant. Interaction with voice chatbot is done using a voice which is more convenient than the text-based chatbots. Universitas Multimedia Nusantara (UMN) requires a tool to provide information for the Informatics joint-degree program. In this paper, a voice chatbot named JACOB is proposed. JACOB is built as a web-based application and implemented with Web Speech API. The sentences received by JACOB are translated using Wit.ai. The Jaro-Winkler distance is also applied in this research to reduce errors in identifying the user’s name. This implementation has been tested successfully, and the total satisfaction score measured using End User Computing Satisfaction is 78.53%.
SNR improvement based on piecewise linear interpolation

Abstract Interpolation improves the resolution of the curve. Based on the stationary characteristics of the signal and the non-stationary characteristics of the noise, the theoretical proof indicates that the piecewise linear interpolation can improve the signal-to-noise ratio, which is further confirmed by simulation results.
Using Expert System Application to Diagnose Online Game Addiction in Junior High School Students: Case Study in Five Big City in Indonesia

The development of computer technology gradually increases. An artificial intelligence-based system also begins to be developed and used in various fields. One product of artificial intelligence is an expert system, used for psychological field. This study aimed to explain and describe the using of expert system to diagnose online game addiction to Junior High School Students. This is based on online game addiction phenomenon happening to Indonesian student especially in Junior High School. The implementation of this expert system used certainty factor method. Steps for developing this system were divided into four, namely designing expert system or architecture of expert system, representing knowledge, designing database, and testing and implementing the system. The results indicate that this system is divided into two domains, including user and admin. The user domain is provided for users who are willing to do online consultations using system expert application. Meanwhile, the admin domain is provided for an admin to manage each datum and question from the user who conducts an online consultation. From 1000 samples, it is obtained that 69% amongst total samples of Junior High School Student have a low-level addiction to online game, 25% experience medium-level addiction, and 6% are highly addicted.
Blockchain Technology: Can Data Security Change Higher Education Much Better?

Most college celebrities may not be very familiar with blockchain, except maybe for a few people who follow the financial cycle of a cryptocurrency called Bitcoin. Blockchain is a security technology that can replace traditional academic transcripts and provide a new model for sharing scientific information. This makes blockchain a useful learning tool or even as a medium for channeling or storing academic data. Apart from that, Blockchain can also be used to determine the ownership of a person's intellectual property. The proliferation of false credentials micro-credentials encourages academics to be able to strengthen the data security system implemented. Therefore, to strengthen and make it easier to validate the validity of these micro-credentials, this blockchain technology is considered very suitable to be used as a solution. Therefore, in this study the author uses the literature review method with the aim of finding out whether blockchain technology can update the Indonesian education system to a better domain or not.
Is reputation on Stack Overflow always a good indicator for users' expertise? No!

Stack Overflow (SO) users are recognized by reputation points. The reputation points are often a great avenue for users to build their career profile and demonstrate their expertise in some domains. Prior studies used users' reputation as a proxy to estimate their experience and expertise. However, there are various ways for a user to earn reputation points that do not require much expertise, such as asking high-quality questions. Therefore, it is important to understand the meaning of a high-reputation point and if the reputation could be used as a good indicator for users' expertise and experience on Stack Overflow. In this study, we explore how users earn reputation points on Stack Overflow by mining their reputation-related activities (e.g., asking questions, answering questions, and editing posts). We study the reputation-related activities of 93,053 high-reputation users that have at least 1,000 reputation points. We find that 1) 13.8% of the studied users earn their majority reputation points through asking questions rather than answering questions. 2) In general, most of the posted answers received no or very few reputation points with users gaining their points from a very small proportion of highly-voted answers. 12% of users' entire reputation comes from one single answer. We suggest future research and Stack Overflow introduce a new metric (i.e., vindex) to evaluate the expertise of a user.