Reviewerly/exHarmony huggingface dataset:
Integrated Model for Morphological Analysis and Named Entity Recognition Based on Label Attention Networks in Korean

In well-spaced Korean sentences, morphological analysis is the first step in natural language processing, in which a Korean sentence is segmented into a sequence of morphemes and the parts of speech of the segmented morphemes are determined. Named entity recognition is a natural language processing task carried out to obtain morpheme sequences with specific meanings, such as person, location, and organization names. Although morphological analysis and named entity recognition are closely associated with each other, they have been independently studied and have exhibited the inevitable error propagation problem. Hence, we propose an integrated model based on label attention networks that simultaneously performs morphological analysis and named entity recognition. The proposed model comprises two layers of neural network models that are closely associated with each other. The lower layer performs a morphological analysis, whereas the upper layer performs a named entity recognition. In our experiments using a public gold-labeled dataset, the proposed model outperformed previous state-of-the-art models used for morphological analysis and named entity recognition. Furthermore, the results indicated that the integrated architecture could alleviate the error propagation problem.
Under the Concealing Surface: Detecting and Understanding Live Webcams in the Wild

Given the central role of webcams in monitoring physical surroundings, it behooves the research community to understand the characteristics of webcams' distribution and their privacy/security implications. In this paper, we conduct the first systematic study on live webcams from both aggregation sites and individual webcams (webpages/IP hosts). We propose a series of efficient, automated techniques for detecting and fingerprinting live webcams. In particular, we leverage distributed algorithms to detect aggregation sites and generate webcam fingerprints by utilizing the Graphical User Interface (GUI) of the built-in web server of a device. Overall, we observe 0.85 million webpages from aggregation sites hosting live webcams and 2.2 million live webcams in the public IPv4 space. Our study reveals that aggregation sites have a typical long-tail distribution in hosting live streams (5.8% of sites contain 90.44% of live streaming contents), and 85.4% of aggregation websites scrape webcams from others. Further, we observe that (1) 277,239 webcams from aggregation sites and IP hosts (11.7%) directly expose live streams to the public, (2) aggregation sites expose 187,897 geolocation names and more detailed 23,083 longitude/latitude pairs of webcams, (3) the default usernames and passwords of 38,942 webcams are visible on aggregation sites in plaintext, and (4) 1,237 webcams are detected as having been compromised to conduct malicious behaviors.
Marine accident learning with fuzzy cognitive maps (MALFCMs)

Statistical analysis of past accidents in maritime may demonstrate the trends for certain contributing factors in accidents, however, there is a lack of a suitable technique to model the complex interrelations between these factors.Due to aforementioned complex interrelations and insufficient information stored in accident databases, it was not possible to understand the importance of each factor in accidents, which prevented researchers from considering these factors in risk assessments.Therefore, there is a need for a capable technique to estimate the importance of each factor.The results of such a technique can be used to inform risk assessments and predict the effectiveness of risk control options.Thus, this study introduces a new technique for Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs).The novelty of MALFCM is the application of fuzzy cognitive maps (FCMs) to model the relationships of maritime accident contributors by directly learning from an accident database as well as having the ability to combine expert opinion.As each fuzzy cognitive map is derived from real occurrences supported by expert opinion, the results can be considered more objective.Thus, MALFCM may overcome the main disadvantage of fuzzy cognitive maps by eliminating or controlling the subjectivity in results.• A novel MALFCM method to weight human-contributing factors into maritime accidents has been developed.• With MALFCM method the main disadvantage of traditional FCMs is overcome.• The MALFCM method can produce logical results even by solely using information from historical data in the absence of expert judgement.
Implementation Of Configuration Management Virtual Private Server Using Ansible

Virtualization technology has been applied to universities with computer study programs to support practicum in Network Management courses. Each user gets a Virtual Private Server (VPS) with container technology. VPS system that is prepared manually requires a long time, especially when the number of users is increasing. The activity repeats every semester so it becomes ineffective and inefficient. Application of automation using Ansible can help to manage VPS objects in the Promox Virtual Environment (PVE) Cluster dynamically. Network Development Life Cycle (NDLC) was used as a method in this study. The design of the VPS management automation system created supports the grouping of container, user and permission resource management for users. The design is implemented in Ansible Playbook. The test results show the average time of making VPS objects per student with an automation system 2 (two) times faster, that is 26.25 seconds compared to the old system which takes 2 minutes 15 seconds. Besides that, the Playbook that was created succeeded in automating the start and stop containers per group of students based on the practicum schedule dynamically so as to maintain the availability of services from the PVE Cluster.&#x0D;
The Scavenger Hunt: A Technique for Enhancing Culture Learning and Intercultural Communication Practice

In this changing era, which is characterized by the interconnected world, and the increasing mobility of people, the need to be able to communicate inter-culturally is evident. To be successful in intercultural communication, one does not only need to be linguistically competent but also culturally knowledgeable and sensitive. Bearing in mind these cultural needs, this paper details the scavenger hunt technique as a tool for enhancing culture learning and intercultural communication practice. Although this technique applies to students of any major, this study was specifically implemented to a group of English as a Foreign Language (EFL) students in Indonesia. Participants of this study were nine (9) male and twenty (20) female students of The English Language Education Program at a private university in Central Java, Indonesia. The students did the scavenger hunt in a group of four or five during their three-day trip to a multicultural setting in Indonesia: Bali island, and documented the activities in vlogs. The students’ vlogs which were data of this study were analyzed by using a qualitative content analysis approach. The findings showed that the scavenger hunt task resulted in the students' learning on cultural knowledge, intercultural communication, intercultural adaptation, information technology, and soft skills. During the three-day program, the students could also use English in a meaningful way. The study presented the strengths, weaknesses, and recommendations of this study, as well as some ideas for further research.Keywords: culture learning, intercultural communication, EFL,scavenger hunt.
Log Event Management Server Menggunakan Elastic Search Logstash Kibana (ELK Stack)

This study aims to build an Event Management Server Log using ELK Stack (Elastic searchLogstash Kibana) which can make it easier to read and analyze log services on the server. TheEvent Management Server log in this study uses CentOS 7 as the Central Server and CentOS7 as a client-server with ssh services installed. This research consists of five stages. The stagesare analysis, network design, server configuration, client configuration, and testing. Theexperimental results show that all ssh log services that occur on the client-server sent inrealtime to the central server. Even though the contents of the log file on the client-server hasdeleted. In This study, in addition to sending logs, it can also display a percentage of successreferences.
Learning with Random Learning Rates

In neural network optimization, the learning rate of the gradient descent strongly affects performance. This prevents reliable out-of-the-box training of a model on a new problem. We propose the All Learning Rates At Once (Alrao) algorithm for deep learning architectures: each neuron or unit in the network gets its own learning rate, randomly sampled at startup from a distribution spanning several orders of magnitude. The network becomes a mixture of slow and fast learning units. Surprisingly, Alrao performs close to SGD with an optimally tuned learning rate, for various tasks and network architectures. In our experiments, all Alrao runs were able to learn well without any tuning.
Gender voice classification with huge accuracy rate

Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique based on data mining and Java script.
A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection

Abstract Purpose The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately. To serve this purpose, a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset. Design/methodology/approach This paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems. The proposed approach, named Bagged and Voted Local Outlier Detection (BV-LOF), benefits from the Local Outlier Factor (LOF) as the base algorithm and improves its detection rate by using ensemble methods. Findings Several experiments have been performed on ten benchmark outlier detection datasets to demonstrate the effectiveness of the BV-LOF method. According to the results, the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average. Research limitations In the BV-LOF approach, the base algorithm is applied to each subset data multiple times with different neighborhood sizes ( k ) in each case and with different ensemble sizes ( T ). In our study, we have chosen k and T value ranges as [1–100]; however, these ranges can be changed according to the dataset handled and to the problem addressed. Practical implications The proposed method can be applied to the datasets from different domains (i.e. health, finance, manufacturing, etc.) without requiring any prior information. Since the BV-LOF method includes two-level ensemble operations, it may lead to more computational time than single-level ensemble methods; however, this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree. Originality/value The proposed approach (BV-LOF) investigates multiple neighborhood sizes ( k ), which provides findings of instances with different local densities, and in this way, it provides more likelihood of outlier detection that LOF may neglect. It also brings many benefits such as easy implementation, improved capability, higher applicability, and interpretability.
Learning Quintuplet Loss for Large-Scale Visual Geolocalization

With the maturity of artificial intelligence technology, large-scale visual geolocalization (LSVGL) is increasingly important in urban computing, where the task is to accurately and efficiently recognize the geolocation of a given query image. The main challenge of LSVGL faced by many experiments due to the appearance of real-word places may differ in various ways while perspective deviation almost inevitably exists between training images and query images because of the arbitrary perspective. To cope with this situation, in this article, we in-depth analyze the limitation of triplet loss, which is the most commonly used metric learning loss in state-of-the-art LSVGL framework and propose a new quintuplet loss by embedding all the potential positive samples to the primitive triplet loss. Extensive experiments are conducted to verify the effectiveness of the proposed approach and the results demonstrate that our new loss can enhance various LSVGL methods.
Time Series Prediction Based on Complex-Valued S-System Model

Symbolic regression has been utilized to infer mathematical formulas in order to solve the complex prediction and classification problems. In this paper, complex-valued S-system model (CVSS) is proposed to predict real-valued time series data. In a CVSS model, input variables and rate constants are complex-valued. The time series data need to be translated into complex numbers. The hybrid evolutionary algorithm based on complex-valued restricted additive tree and firefly algorithm is proposed to search the optimal CVSS model. Three financial time series data and Mackey–Glass chaos time series are collected to evaluate our proposed method. The experiment results show that the predicted data are very close to the target ones and our method could obtain the better RMSE, MAP, MAPE, POCID, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>, and ARV performances than ARIMA, radial basis function neural network (RBFNN), flexible neural tree (FNT), ordinary differential equation (ODE), and S-system.
Identification of local features on a group of images obtained in different electromagnetic ranges

The fusion of data obtained in different electromagnetic ranges is an important task for many areas of research. The combining data is necessary for security systems (when searching for people in difficult weather conditions (snow, fog, rain, dust), automated control systems (auto-driving, UAVs), etc. The process of data analysis involves identifying base features. It includes the search and selection of borders, salience maps (human attention cards), angles, analysis of color gradients, etc. Most often, the detected features highlighted in images recorded in one range do not coincide with data obtained in other ranges. This is due to the fact that different electromagnetic ranges operate with different physical characteristics of objects in frames. The paper presents an approach based on the search and analysis of the basic descriptive characteristics of objects and the search for their correspondences on images of the same object, recorded in different electromagnetic ranges. As such data, the directions of the gradients are revealed, the search for the boundaries and angles of objects, the selection of locally stationary regions, the search for the center of mass of objects, the identification of the middle lines of stationary regions with included structures. The search for features is carried out on the basis of data obtained at various scales. Simplification of images is carried out on the basis of an algorithm for analyzing stationary regions and replacing the current intensity with an average. On the set of test data obtained in the visible range, near and far-infrared range, depth maps, the applicability of the proposed approach is shown. As an example of the applicability of this approach, an example is shown of stitching a pair of images obtained in different electromagnetic ranges.
Secure Metric Learning via Differential Pairwise Privacy

Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of individuals given a set of correctly labeled pairwise data by domain experts. These important and precisely-labeled pairwise data are often highly sensitive in real world (e.g., patients similarity). This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning. Unlike traditional differential privacy which only applies to independent samples, thus cannot be used for pairwise data, DPP successfully deals with this problem by reformulating the worst case. Specifically, given the pairwise data, we reveal all the involved correlations among pairs in the constructed undirected graph. DPP is then formalized that defines what kind of DML algorithm is private to preserve pairwise data. After that, a case study employing the contrastive loss is exhibited to clarify the details of implementing a DPP- DML algorithm. Particularly, the sensitivity reduction technique is proposed to enhance the utility of the output distance metric. Experiments both on a toy dataset and benchmarks demonstrate that the proposed scheme achieves pairwise data privacy without compromising the output performance much (Accuracy declines less than 0.01 throughout all benchmark datasets when the privacy budget is set at 4).
Dimensionality assessment of binary response test items: A non-parametric approach of bayesian item response theory measurement

This study investigated dimensionality of Binary Response Items through a non-parametric technique of Item Response Theory measurement framework. The study used causal comparative research type of non-experimental design. The sample consisted of 5,076 public senior secondary school examinees (SSS3) between the age of 14-16 years from 45 schools, which were drawn randomly from three senatorial districts of Osun State, Nigeria. Instrument used for this study was 2018 Osun State unified multiple-choice mathematics achievement test items with empirical reliability coefficient of 0.82. Data obtained were analysed using Non-linear factor analysis, Stout’s Test of Essential Unidimensionality (STEU), Factor Analysis (FA), Full Information Factor Analysis (FIFA) and Bootstrap Modified Parallel Analysis Test (BMPAT). Results showed that both the BMPAT and STEU ascertained violation of unidimensionality assumption of the test items (the observed difference in the second eigenvalue of the observed data and that of second eigenvalue of the simulated data was statistically significant, p = 0.0099; Stout’s test rejected the assumption of essential unidimensionality, T = 10.6260, p&amp;lt;0.05). Non-linear factor analysis and full information factor analysis revealed that four dimensions embedded in the test items and loadings of the items showed within-item multidimensionality respectively. The authors’ concluded that modeling examinees’ performance with unidimensional model when it was actually multidimensional in nature would affect performance of examinees adversely and could lead to blur judgment. Consequently, it is recommended that unidimensional scoring method of Osun State unified mathematics achievement test implicit in Classical Test Theory should be jettisoned and an appropriate scoring model (multidimensional) should be embraced.
An Analysis of Proposed ESP Teaching Activities in Border Guard Automobile Transport Masters Training

The article describes the teaching activities of English for specific purposes in future border guard Automobile Transport Masters training at the National Academy of the State Border Guard Service of Ukraine named after Bohdan Khmelnytskyi.The main attention is paid to developing of Working Curriculum anchored on the notion of "English for specific purposes" (ESP) for the educational program of the second (master's) level of higher education on specialty 274 "Automobile Transport".The purpose of this research is to study the ESP teaching activities in future border guard Automobile Transport Masters training.The methodology of the course is aimed at developing the border guards' professional communicative competency, which would facilitate their effective service in the border crossing points.The course program provides border guards with the opportunity to develop language competence to communicate with foreigners while conducting border surveillance activities and border checks.The structure of this educational program takes into account the elements of the PROJECT TUNING and the categories of BLOOM TAXONOMY.The classification of the types of teaching activities of ESP in future border guard Automobile Transport Masters training has been determined and substantiated.They include five components: warm ups, vocabulary, reading, listening, grammar and speaking with their corresponding activities.The results of its implementation are the number of officers in experimental group who had high and middle levels of English language proficiency increased by 25 %; and the number of officers in control group who had high and middle levels of English language proficiency increased by 6%.
ROM

Requirement opinion mining aims to mine user opinions that can be used to help the mining of software requirements from various data sources. However, in the development of social network systems, software application platforms or stores and other data sources, the massive, noisy, non-standard data, makes the mining of effective requirement opinions more difficult. Therefore, there is less work in software requirements mining based on the data of software review in development social media or application market. This paper attempts to provide some knowledge support for requirement user story establishing in RE based on the opinion mining and clustering of massively software review data. First of all, this paper combines the requirements of the requirements engineering field to define the requirement opinions, functional requirement opinions and non-functional requirements opinions. Secondly, using the deep learning model to classify the functional requirement reviews and non-functional requirements reviews included in the reviews; Based on the differences between functional data and non-functional data, this paper defines three categories in the description of software functional data, and chooses to use sequence labeling methods to identify functional requirements. Then use the K-means clustering method based on word vector to cluster the review data, and combine TF-IDF and syntactic analysis to extract the aspect and aspect requirements or specific requirements of the requirement opinion respectively, so as to realize the requirement opinion mining of software review data. Finally, this article will give a case study based on the user review data of the mobile phone application service platform 360 mobile assistants.
Score-based likelihood ratios for camera device identification

Many areas of forensics are moving away from the notion of classifying evidence simply as a match or non-match. Instead, some use score-based likelihood ratios (SLR) to quantify the similarity between two pieces of evidence, such as a fingerprint obtained from a crime scene and a fingerprint obtained from a suspect. We apply trace-anchored score-based likelihood ratios to the camera device identification problem. We use photo-response non-uniformity (PRNU) as a camera fingerprint and one minus the normalized correlation as a similarity score. We calculate trace-anchored SLRs for 10,000 images from seven camera devices from the BOSSbase image dataset. We include a comparison between our results the universal detector method.