Reviewerly/exHarmony huggingface dataset:
The semantic roles of ‘kaku-joshi’ in Japanese textbooks

Case particles are an important Japanese language learning aspect that the learners should have learned since the beginner level, yet there are still many who still do not grasp the meaning and function of each particle. This is because in the instructional process, the elaboration of such articles is confined to the external structure, while the inner structure (semantic roles) is rather neglected in the textbooks. This study seeks to explain each semantic role of the particles 'kaku-joshi' contained in Japanese language teaching materials starting from elementary to secondary levels used in Indonesia. The method used is descriptive, with textual analysis employed as a data analysis technique. Findings reveal that all Japanese case particles have appeared in elementary level textbooks, namely particles GA, WO, NI, DE, TO, E, KARA, MADE, and YORI which are used to follow arguments or syntactic functions in the form of subject, object, complement, and adjunct. However, not all semantic roles appear in the textbook; 14 semantic roles are evidently not present. The findings of this study can be used as reference material for Japanese instructors in the teaching of Japanese case particles, so that the differences among the particles will be clearer and more easily understood by Japanese language learners in Indonesia.
Performance Evaluations of IEEE 802.11ad and Human Pose Detection towards Intelligent Video Surveillance System

Towards an intelligent video surveillance system (high throughput and high detection accuracy), in this paper, we quantitatively validate the network performance of 60GHz IEEE 802.11ad and the contribution of 4K resolution to image-based human pose detection. In the performance evaluation of 11ad, we evaluate TCP throughputs by changing the communication distances (up-to 6m). In the performance evaluation of human pose detection, we evaluate the detection accuracy of human pose detection by changing a distance between a camera and a subject (up-to 100 m) and input image resolutions.
Track Matching Based on ELM for HFSWR

High frequency surface wave radar (HFSWR) plays an important role in marine surveillance and target tracking. However, in the complex environment of interference, the ship target signals will be submerged, which lead to track breakage and miscorrelation. How to effectively detect and track specific targets continuously is the key problem to be solved in signal processing for HFSWR. In this paper, we propose a track matching method based on extreme learning machine (ELM). In this method, track features are fully explored: 1. Extract the key features of ship attributes based on track information; 2. Establish the matching relationship between track data and ship model based on ELM. The track matching accuracy is verified by both the simulation and field experiments, validating the effectiveness of the new method.
QGIS, database ed epigrafia: un progetto in corso

The study of epigraphical and archaeological evidence related to a specific topic, in this case infant mortality, has led to the creation of a geo-referencing project in order to collect, store and analyze information about the young deceased and their families. This paper presents a geo-referenced storage and management system that combines both open source software as QuantumGIS and PgAdmin. The use of a RDBMS has been implemented and purposively structured, taking into account the content and the form of the inscriptions studied.
An Innovative Model (HS) to Enhance the Security in Windows Operating System - A Case Study

Confidentiality, authentication, privacy and integrity are the pillars of securing data. The most generic way of providing security is setting up passwords and usernames collectively known as login credentials. Operating systems use different techniques to ensure security of login credentials yet brute force attacks and dictionary attacks along with various other types which leads to success in passing or cracking passwords.The objective of proposed HS model is to enhance the protection of SAM file used by Windows Registry so that the system is preserved from intruders.
Dual Network Fusion for Person Re-Identification

Feature learning based on deep network has been verified as beneficial for person re-identification (Re-ID) in recent years. However, most researches use a single network as the baseline, without considering the fusion of different deep features. By analyzing the attention maps of different networks, we find that the information learned by different networks can complement each other. Therefore, a novel Dual Network Fusion (DNF) framework is proposed. DNF is designed with a trunk branch and two auxiliary branches. In the trunk branch, deep features are cascaded directly along the channel direction. One of the auxiliary branch is channel attention branch, which is used to allocate weight for different deep features. Another one is multi-loss training branch. To verify the performance of DNF, we test it on three benchmark datasets, including CUHK03NP, Market-1501 and DukeMTMC-reID. The results show that the effect of using DNF is significantly better than a single network and is comparable to most state-of-the-art methods.
Visual Monitoring of Complex Algorithms

Purchases, conversations, access to information, music and movies: more and more of our online life is mediated by complex algorithms that are designed to make the experience of the Web customized and more "personal".These algorithms can process an amount of heterogeneous data that would take enormous resources for the human mind to cope with, and find valuable patterns in it.Their use is not limited to our online experience as similar algorithms have been also implemented, for example, to inform policymakers: suggesting where to deploy police forces around the urban context, assessing criminality risk scores of offenders, or allocating high school students to the most suited school.While the consequences of the decisions made by algorithms have a great impact on people's lives, the way they are built and designed makes them de facto "black boxes": a series of legal and technical barriers prevents from accessing and understanding how a certain input influences a given output.Overseeing their decision processes becomes then of the utmost importance.This paper argues that visualizations can become a powerful tool to monitor algorithms and make their complexity accessible and usable by visually showing the relation between the inputs and the outputs in a manner that mimics an observational study approach.The paper analyzes a case study developed as an experiment to test opportunities and criticalities in using visualization to represent the presence and the activity of algorithms.This represents a shift from the main purpose of visualizations and Data Visualization in general: since its strong suit is to support human decisionmaking processes by transforming data into knowledge, the substitution of people by machines in this activity seems to make visualizations obsolete.A computer doesn't need to "see" the data to make a decision -or at least not in the same way as people do -no matter how multidimensional and heterogeneous the data is.With the diffusion of algorithms, the need to inspect their accountability and performance will simply move visualizations at a later stage.From a decision-making tool visualization becomes a monitoring and awareness tool.
Basic Petrological and Structural Analysis in the Zimbi Gold Area (East Cameroon)

The plutono-metamorphic series of the locality of Zimbi, extends over a narrow strip of 135 km 2 on the eastern edge of the gold district of Baden-Colomine in East Cameroon.The work carried out in this locality is based on a cartographic approach (processing of maps and Landsat-7 ETM + images), combined with a geological and mining survey on the ground using conventional tools.On the morphostructural level, we observe NE-SW oriented penetrative lineaments.The trajectories of these are deviated or not in contact with other less dense lineaments, organized in networks of directions N-S, NE-SW, ENE-WSW, E-W, NW-SE.Parallel to the main Cameroon Center Shear (N070E), the ENE-WSW network which induces a significant reversal of foliation on contact, defines a structuring dexter shear zone accompanied by NE-SW and EW satellite shear zones with dexter kinematics and NW-SE and NS of senestrial kinematics.The lithological diversity allows the facies of calc-alkaline granite to predominate.Zimbi's range of structural elements highlights a magmatic fluidity substituted in foliation.The foliation directed NE-SW, of dip (40-90 °) either towards the NW, or towards the SE; carries a linearization of stretch subméridienne.The folding that affects it is induced by two directions of compression perpendicular N-S to NE-SW and E-W to NW-SE, which define the voltage slots favorable to mineral concentrations by circulation of mineralizing fluids.The shear zones and the quartz-feldspathic veins, structures favorable to mineralization such as gold, secondarily affect this complex.These geomorphological, lithological and structural geology data make the plutono-metamorphic procession of Zimbi a privileged area for mining research, in the gold district of Baden-Colomine.
Impact of High-Capacity Internet Access on Technical Level of e-Learning

The paper deals with the quality evaluation of electronic communication services that provide technical background for e-learning systems in the environment of the NGA (Next Generation Access) communication networks, especially in high-capacity Internet access networks. It explains the basic principles and technical capabilities of the access networks. Then it examines e-learning as an application area and its individual forms (including services). In detail it focuses on the relationship between selected e-learning forms and the demands for data transmission including its implementation using particular electronic communication services with specific requirements for qualitative parameters. The experimental measurement results of Internet access throughput are presented and discussed.
Gambaran kualitas dan mutu radiograf

Objectives: The study is aimed to give further explanation and understanding of radiograph quality assurance based on scientific literatures.&#x0D; Literature Review: A radiograph’s quality can be assessed based on the produced image itself. The radiograph must meet the standard values to be interpreted by practitioners and radiologists. There are various kinds of standard value of a radiograph that should be met before doing an interpretation, therefore a review is needed to assess the quality of a radiograph. This literature will discuss how to assess the quality of a radiograph. Based on the review of previous literatures, differences have been found between one quality assessment and another, so that the quality of radiograph is fulfilled. &#x0D; Conclusion: A radiograph has many aspects to be assessed to determine its quality.
Segmentation of Foreground in Image Sequence with Foveated Vision Concept

The human visual system has no difficulty to detect moving object. To design an automated method for detecting foreground in videos captured in a variety and complicated scenes is a challenge. The topic has attracted much research due to its wide range of video-based applications. We propose a foveated model that mimics the human visual system for the detection of foreground in image sequence. It is a two-step framework simulating the awareness of motion followed by the extraction of detailed information. In the first step, region proposals are extracted based on similarity of intensity and motion features with respect to the pre-generated archetype. Through integration of the similarity measures, each image frame is segregated into background and foreground points. Large foreground regions are preserved as region proposals (RPs). In the second step, analysis is performed on each RP in order to obtain the accurate shape of moving object. Photometric and textural features are extracted and matched with another archetype. We propose a probabilistic refinement scheme. If the RP contains a point initially labeled as background, it can be converted to a foreground point if its features are more similar to neighboring foreground points than neighboring background points. Both archetypes are updated immediately based on the segregation result. We compare our method with some well-known and recently proposed algorithms using various video datasets.
Reversible Data Hiding in Encrypted Video Using Reversible Image Transformation

This work focuses on a method to hide images in videos in a manner that the secret image can be losslessly recovered from the target image with minimal distortion. This lossless recovery can be done by using Reversible Data Hiding (RDH). To ensure the privacy of the video owner, the target media is encrypted before applying RDH. Reversible Image Transformation (RIT) is a framework used to ensure that. Audio Steganography techniques are used for further encryption. This can be used in cloud technology so that the cloud may add information into the target video without compromising its integrity. RDH also has other possible applications in the field of military, defence and medicine.
An Article Review on “The Use of Politeness Strategies in the Classroom Context by English University Students"

This study aims to review and look at politeness strategies in the Classroom Context by English University Students. Politeness in English language teaching is still a major concern. This is considered a way of ensuring positive contact in the classroom. As an essential actor in the class, teachers and students must also cultivate politeness as a way to establish positive engagement in the classroom. In order to investigate the politeness phenomenon in EFL interaction, the researchers applied a descriptive qualitative research method. Two sections of English literature were included in this research. The key data sources were the individual student presentations recorded. The recording took fifty transcripts, which lasted between five and seven minutes for each performance. The transcripts were examined and debated on the basis of Brown and Levinson's theory of politeness. The findings of this research indicate that English students used different types of words to express their courtesy within the classroom. Such phrases were in the form of greetings, thanks, apologies and fillers. Some terms were also taken from the vernacular language of students who acted as a tool to soften their presentation. Such words were marked as positive and negative politeness. The results of this study could be used as a means of establishing effective interaction between teachers and students in the classroom.
Indonesian Short Essay Scoring Using Transfer Learning Dependency Tree LSTM

Distributed representation of a sentences cannot only be seen with the sequence but also with dependency.In this paper, we proposed an answer assessment model that considers with dependency relational each word.The dependency relational is obtained by universal dependency modelling from CoNLL format data.The dependency relational is used in Long Short-Term Memory (LSTM) architecture by modified the hidden state, which is called dependency tree LSTM.The proposed method has an improvement on QWK and accuracy in 2.38% and 2.05%, respectively, that compared with the LSTM state of the art in the English short essay.Furthermore, the proposed method with an Indonesian short essay shows the evaluation of QWK and accuracy of 68.07%and 82.51%, respectively.
Predictive Analysis of Mental Fog Using Machine Learning

Mental fog, also known as confusion, is one of the main reasons for poor performance in the learning process or any type of daily task that involves and requires thinking.Detecting confusion in the human mind is a real time paradigm that appears to be more difficult and important tasks that can be applied to online education, driver fatigue detection, etc.The Random Forest model achieve a better performance compared to other machine learning approaches and shows a great robustness evaluated by cross validation.We can predict if a student is confused about 100% accuracy.In addition, we found that the most important characteristic for detecting brain confusion is the beta 2 and gamma 1 wave of the Electroencephalography (EEG) signal.Our results suggest that machine learning is a potentially powerful tool for modeling and understanding brain activity.This work could be beneficial to individuals, to Ministry of Health, patients with brain diseases and to any other organization that deals on human state of mind in terms of performance.
Reliability Analysis in Planning of Traction Substation Reconstruction Based on Fuzzy Set Theory

The study proposes a probabilistic method using triangular fuzzy numbers to analyze the reliability of the traction substation. With this approach, the reliability assessment of the traction substation can be performed considering changes in the values of reliability indicators of electrical equipment, determined on the basis of the fuzzy set theory
PROCESS GENRE APPROACH IN TEACHING WRITING NARRATIVE TEXT: ITS IMPLEMENTATION, BENEFITS AND OBSTACLES (A Qualitative Research at a Senior High School in Cianjur)

Nowadays, teachers should be more creative and innovative in teaching.In the real condition, students still have difficulties in writing class. To improve students’ writing ability in writing various genres of text, the teacher should choose an innovative strategy. Process genre is one of the approaches commonly used in teaching writing. This research aims to find outthe useof Process Genre Approach in teaching the narrative text as well asthe benefitsand obstacles of using Process Genre Approach in teaching narrative text. This research applied qualitative research. The data of this research were collected from three instruments:classroom observation, questionnaire, and interview. This research was carried out in a senior high school in Cianjurby involving twenty-three students of ten-grade and an English teacher.The first finding shows that in applying process genre approach in teaching writing of narrative text, there were six steps conducted by the teacher following Badger and White (2000) steps: preparation, modeling, planning, join constructing, independent constructing, and revising and editing. The second finding showsthat there were some benefits of using Process Genre Approach in teaching writing narrative text. Teacher’s interview reveals that the benefits of using process genre approach in teaching writing narrative text were the students can write a text step by step and the students not only can write a text but they can learn about a genre of text in detail. In addition, students questionnaire discloses several benefits of using process genre approach in teaching writing narrative text such as the students are easier to write a text and the students can make a text in detail. The last finding shows there were two obstacles, the first was students’low grammar mastery and the second was students low vocabulary mastery.
PID Controller with Real Number Genetic Algorithms

A genetic algorithm (GA) is an artificial intelligence technique that can be applied to any engineering specialty. In this study, the proposed GA finds optimal values for PID controller parameters Kp, Ki and Kd, which are widely used in the industry. Chromosomes, composed of Kp, Ki and Kd genes, represented by real numbers, evolve and are evaluated by the mean square error (MSE) of the desired output. In that sense, the solution is the chromosome with the lowest MSE, which produces less transient output. In addition, the GA has been encoded in MATLAB language and results have been compared with other works.
Multispectral brain Magnetic Resonance Imaging denoising using non-local means with statistical nearest neighbor

The basic operation of computer vision is filtering. It is the overture process in applications of biomedical image processing. MRI denoising improves the diagnosis accuracy, quality of image with operations performed on it such as image segmentation and registration; and also parameter estimation. It has been observed that the noise in MRI follows gaussian and rician distribution. In this paper, algorithms are applied on multispectral brain MRI that are either Tl-weighted or T2-weighted. The proposed method Multispectral non-local means with statistical nearest neighbor is applied and the results are compared with other NLM filters available. The experimental results show that proposed method provides better noise reduction than NLM with nearest neighbor and adaptive NLM.
The specificity of the philosophical and artistic comprehension of the world as a problem of the integrity of spiritual culture

The purpose of the article is to analyze the approaches of the ratio of philosophical and artistic understanding of the world, to consider the problem of the integrity of spiritual culture, as well as the search for models of the ratio of wisdom and creativity. The methodology is based on the application of General scientific and special methods of cog- nition, in particular: analysis, synthesis, logical method and generalization method. The scientific novelty lies in the introduction of the statement that philosophical creativity, following man into art, meets with a completely new image of man. To be human is to deviate from the goal, to distance oneself from abstraction, to overcome identification with the idea. Man can also be understood as creativity, which originates in the life of noumenal, that is, in freedom. Conclu- sions. Philosophy and art are those spiritual spaces in which not only individual beingness is embodied, but, in fact, in which the goal of further development of both man and humanity is formed. The creative potential of artists and philos- ophers creates a certain mood and is often the impetus for the concentration and ascent of the human soul through good to comprehend themselves.
Scaling Up Heterogeneous Waveform Clustering for Long-Duration Monitoring Signal Acquisition, Analysis, and Interaction: Bridging Big Data Analytics with Measurement Instrument Usage Pattern

Modern oscilloscopes, digitizers and data loggers generate a large amount of waveform data for long-duration waveform capturing and analysis. The contrast of time scales of long-duration waveform capturing (e.g., hours or days in high sampling rate) and analysis (e.g., signal fragments of several microseconds) produces unique big data challenges. The proposed long-duration waveform clustering algorithms are designed for signal waveform analysis and user interaction for various “big-data” waveform analysis scenarios. To cope with the real-time processing demand and the hardware constraints of the target platforms, the proposed algorithm utilizes multiple layers of data pre-sorting, database query, and waveform similarity-based clustering for versatile speed-precision tradeoffs. We integrated the system as an intuitive big waveform data analytics framework which provides unprecedented performance and productivity to engineers and scientists. Experimental result shows superb speed and data volume capability.