Reviewerly/exHarmony huggingface dataset:
The Flouting of Grice’s Conversational Maxims in &lt;i&gt;Pirates of the Caribbean&lt;/i&gt; 1-5

This research is aimed to investigate maxim floutings uttered by the pirate characters in Pirates of the Caribbean movie series. The goals of this research are to identify and classify the maxim flouting within the movie series, to describe how the pirate characters flout the Grice’s maxim, and also to discover the purpose of the maxim flouting itself. Qualitative and quantitative methods are used to analyze the data. The result shows that there are 39 cases of maxim flouting with 4 cases (10.3%) of flouting the maxim of Quantity, 13 cases (33.3%) of flouting the maxim of Quality, 16 cases (41.0%) of flouting the maxim of Relation, and 6 cases (15.4%) of flouting the maxim of Manner. The pirates tend to give too much information, simply say something which does not represent what they think by using: metaphor, irony, sarcasm, and hyperbole, give irrelevant answers, and also give ambiguous answers. There are several purposes of maxim flouting found in this movie series. They are: insulting, giving warning, challenging other characters, boasting about oneself, telling about facts, informing plans, convincing, expressing seriousness, demanding respect, expressing insincerity, expressing love, expressing disappointment, giving advice, expressing happiness, creating fear, creating humor, and avoiding making others sad.
PERCEIVED LEARNING ASSISTANCE AND PERCEIVED COMMUNITY BUILDING ASSISTANCE: STUDY ON E-LEARNING SYSTEM

The study aims to examine the perception of the use e-learning in terms of outcomes for lecturers and students who were actively using E-learning. Total sample of this study were 98 respondents. The data col-lection method is carried out by a survey. Hypothesis test in this study use uses smartPLS. The use of e-learning systems has a positive effect on perceived learning assistance, perceived community building, and the subsequent effect on perceived aca-demic performance between the lecturers and students group.
Spatial Intention Maps for Multi-Agent Mobile Manipulation

The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new intention representation for multi-agent vision-based deep reinforcement learning that improves coordination between decentralized mobile manipulators. In this representation, each agent's intention is provided to other agents, and rendered into an overhead 2D map aligned with visual observations. This synergizes with the recently proposed spatial action maps framework, in which state and action representations are spatially aligned, providing inductive biases that encourage emergent cooperative behaviors requiring spatial coordination, such as passing objects to each other or avoiding collisions. Experiments across a variety of multi-agent environments, including heterogeneous robot teams with different abilities (lifting, pushing, or throwing), show that incorporating spatial intention maps improves performance for different mobile manipulation tasks while significantly enhancing cooperative behaviors.
QoE Assessment and Management of MV/3D Video Services

The measurements of Quality of Experience (QoE) are essential to research for better analysis and optimization of Multi-View and 3D (MV/3D) videos. However, there is a lack of systematic description of QoE mechanism in MV/3D videos yet. This paper aims to give a comprehensive account of QoE assessment and management for MV/3D video services. We first conclude distinctive QoE influencing factors (IFs) in MV/3D videos by dividing them into immersion and interaction related IFs. Then the methods of QoE assessment in MV/3D video services (including subjective assessment and objective assessment) are presented. Finally, we summarize QoE management for improving immersion and interaction. We hope our work is capable of providing a useful guideline on the related issues and the future research directions in MV/3D video services.
Efektivitas Alat Sikat Gigi Otomatis Wall Mounted untuk Disabilitas pada SLB di Jember terhadap Indeks Kebersihan Mulut

Disability is a person with disabilities who has physical, mental, intellectual, sensory, and some motor disorders. Disabilities that have motor impairments are blind, mentally retarded, and disabled so they need an automatic wall-mounted toothbrush in cleaning teeth and mouth. A wall-mounted toothbrush is an innovative tool for blind, mentally disabled, and disabled people to be able to brush their teeth independently. The aim is to determine the effectiveness of automatic wall-mounted toothbrushes in children with disabilities, especially in the visually impaired, mentally retarded, and disabled. The study was conducted by pre-experimental method (One Group Pretest-Posttest Design) in SLB disabled people in Jember with 56 people, 17 people with visual impairment, 36 mental retardations, and 3 disabled people. The results of the study carried out a paired sample t-test showed a significance value (p &lt;0.05) in the blind and mentally retarded while the significance value (p&gt; 0.05) in the disabled. The conclusion of the study is that this tool is effective in the blind and mentally retarded but is less effective in the disabled.
Automatic Software Requirements Classification: A Systematic Literature Review

Context: Software requirements must be classified for later use of requirements in the design and implementation phases. This classification can be done manually which takes a lot of time and effort or recently it can be done automatically. Automatic requirement classification is an important and promising area in both industry and research. Objective: The objective of this paper is to investigate the existing automatic classification techniques in order to help researchers and software engineers to choose the appropriate requirement classification technique. Method: This research was conducted with three research questions and followed Systematic Literature Review (SLR) guidelines to analyze, evaluate, interpret the relevant researches. Results: 14 primary studies were conducted from the search processes. 2 out of them were published in Elsevier, 3 were published in Springer, 2 were published in IEEE, and the rest of them were published in different conferences and journals Conclusions: automatic classification of SW requirements is an important task; however, it has a little work in this field. This SLR summarize all existing techniques available in this topic. It needs more effort and research papers to enhance and increase the performance as well as the quality of proposed tools.
S3M: Siamese Stack (Trace) Similarity Measure

Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application's bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information. In this paper, we present S3M (extreme) -- the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results.
The Protagonist of Training among Employee Performance, Organizational Development and Employees’ Turnover

This study was conducted in the banking sector of Malakand Division Khyber Pukhtunkhwa. The objectives of the study were to investigate how training can affect employee performance, turnover, and overall organizational development in the banking sector. Data were collected from 180 individuals through a self-administered, five-point Likert scale questionnaire and was analyzed through SPSS, Regression and Pearson correlation analyses were conducted on the dataset. The results confirmed that employee training considerably affects employee performance, turnover, and overall organizational development.
Debtor Data Collection Information System at PT. PNM Pematangsiantar Branch

This study aims to create a web-based debtor information system at PT. PNM Pematangsiantar Branch. The design and manufacture are intended to facilitate debtor data management. So that employees can easily enter debtor data without having to write it conventionally. Using conventional methods has a negative impact. One of the negative effects of the system that is still in use is data search, data retrieval will take time because you have to open documents one by one. In addition to data search, data collection on prospective debtors wishing to establish a working relationship with companies is also a negative impact from conventional methods. With this application, it is hoped that the processing and data collection of debtor data will be faster and more accurate. The information system to be designed will be in the form of a web and use a MySQL database. It is hoped that this research can solve the problem of debtor information systems at PT.PNM Pematangsiantar Branch.
Understanding the Narrative Functions of Visualization in Digital Humanities Publications: A Case Study of the Journal of Cultural Analytics

The use and effects of visual representations in knowledge production have been a charged topic in scientific research. In the field of humanities studies, however, this topic remains under-examined despite the increasing applications of data visualization in the field. This paper aims to understand how visual representations facilitate narrative construction in published articles in the emerging field of digital humanities (DH). Through the methods of content analysis and close reading, we analyzed the narrative functions of visualizations in the argumentation process with a selected sample of research articles published in the Journal of Cultural Analytics from 2017 to 2019. With four observations from the analysis, this study presented a preliminary yet innovative examination of DH’s visual language and proposed suggestions on integrating existing functional frameworks of data visualization with the research contexts of digital humanities.
Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation

Inferring the probability distribution of sentences or word sequences is a key process in natural language processing. While word-level language models (LMs) have been widely adopted for computing the joint probabilities of word sequences, they have difficulty in capturing a context long enough for sentence probability estimation (SPE). To overcome this, recent studies introduced training methods using sentence-level noise-contrastive estimation (NCE) with recurrent neural networks (RNNs). In this work, we attempt to extend it for contextual SPE, which aims to estimate a conditional sentence probability given a previous text. The proposed NCE samples negative sentences independently of a previous text so that the trained model gives higher probabilities to the sentences that are more consistent with \textcolor{blue}{the} context. We apply our method to a simple word-level RNN LM to focus on the effect of the sentence-level NCE training rather than on the network architecture. The quality of estimation was evaluated against multiple-choice cloze-style questions including both human and automatically generated questions. The experimental results show that the proposed method improved the SPE quality for the word-level RNN LM.
Features of Microleveling in The Processing of Geophysical Data Obtained on a Moving Base

Summary This study describes microleveling methods and software for their application. Various microleveling methods are used for removing linear noise of magnetic data arising from the geophysical observations, in particular, in the aerial version with drones. Each method is characterized by its own algorithm, certain filter parameters, depending on the characteristics of the survey, advantages and disadvantages. The implementation of microlevelling techniques is possible with the help of specialized modules in modern software products. A general overview of the algorithms and an example of algorithm work on the results of magnetic field observations are presented.
The App Scavenger Hunt

The chapter focuses on projects designed to enhance student engagement with, and exploration of, mobile devices. Helping preservice teachers manage the often steep learning curve that goes hand in hand with connecting theory to practice is but one aspect of music teacher preparation. For the methods student and university professor alike, staying abreast of the current PK-12 school population’s musical needs poses unique conditions for curriculum development. Learning <italic>how</italic> to use technology while working with a diverse range of students presents challenges for all who are involved in teaching music with technology. The App Scavenger Hunt is an introductory project intended to foster collaboration by exploring the variety of apps available for later projects such as spontaneous musical jam sessions, group composition, and the (re)creation of cover tunes. These musicking experiences, in conjunction with field experiences in music methods classes, aided university students’ embrace of the potential for creative music making with mobile technology.
Performance Analysis of Multipath BGP

Multipath BGP (M-BGP) allows a BGP router to install multiple 'equally-good' paths, via parallel inter-domain border links, to a destination prefix. M-BGP differs from the multipath routing techniques in many ways, e.g. M-BGP is only implemented at border routers of Autonomous Systems (ASes); and while it shares traffic to different IP addresses in a destination prefix via different border links, any traffic to a given destination IP always follows the same border link. Recently we studied Looking Glass data and reported the wide deployment of M-BGP in the Internet; in particular, Hurricane Electric (AS6939) has implemented over 1,000 cases of M-BGP to hundreds of its peering ASes. In this paper, we analyzed the performance of M-BGP. We used RIPE Atlas to send traceroute probes to a series of destination prefixes through Hurricane Electric's border routers implemented with M-BGP. We examined the distribution of Round Trip Time to each probed IP address in a destination prefix and their variation during the measurement. We observed that the deployment of M-BGP can guarantee stable routing between ASes and enhance a network's resilience to traffic changes. Our work provides insights into the unique characteristics of M-BGP as an effective technique for load balancing.
Modelling the Regional Education System Sustainable Development in the Form of Optimal Control Problem

The article analyzes the education system problems as one of the constituent elements of the state sustainable development strategy and the regional education system as its basis. Currently, Russian education is witnessing transformational processes that are influenced by social and economic changes, national and global trends. Under these conditions, studying the possibility of controlling the education system and modelling the future region development becomes an urgent problem. In this paper, a structural diagram of the regional education system is constructed as a graph, the vertices of which are groups of people who receive general and professional education at various levels, and arcs show possible transitions between groups. Using the graph obtained, a model for the regional education system development is constructed as a multicriteria discrete optimal control problem, in which the phase variables are the numbers of objects (people) belonging to a particular group, and the controls are financial levers. The quality functionals in the model are proposed to be the total region “usefulness” from educational services and the difference between the vacant jobs number and the number of various professional educational institutions graduates. The sustainable development principles of the regional education system, such as the balance principle and the principle of model trajectory time consistency, are formulated in the article. A program that allows calculating the education system sustainable development trajectory is proposed.
On the Evolution of Knowledge Graph Abroad and Its Application in Intelligent Education

The development of education is more and more dependents on the intelligent learning support services in today’s information age. The intelligent education supported by artificial intelligence (AI) has drawn more attention. In particular, knowledge graph (KG) becomes the key to promote the development and innovation of education with the developing of AI. So, the evolution of the research hotspots of KG is reviewed based on the literature of Web of Science in this study. We explore the development of intelligent education on different aspects, including educational KG, cognitive diagnosis and personalized education.
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Human Activity Recognition by Using Privacy-conscious Sensor System

In these days, Japan is facing a declining birth rate and an aging population. In this situation, watching over the elderly single adults has become a big issue. To deal with this problem, we have developed the human activity recognition (HAR) system which is using privacy-conscious sensors. Here, the “privacy-conscious” means avoiding the use of the cameras and the microphones. This system is composed of several kinds of sensors; the human sensor, the temperature and humidity sensor, the illuminance sensor and the door open/close sensor. In addition, we developed human activity logging system to record daily activities. To recognize human activity, we analyzed the data collected from these systems by utilizing the machine learning. As a result, we found that the HAR by using the supervised machine learning can achieve 0.9 accuracy. In this case, the human activities are the meal, relax, work, out go, face wash, cleaning, cooking, washing, sleep and others. In addition, we also found that the HAR by using unsupervised machine learning can achieve 0.9 accuracy. In this case, the human activities are the relax, out go, sleep and others.
User Exits: Defusing a Potential Time Bomb

Your SAP implementation probably started with the aim of being “vanilla”—i.e., having no Z code and making no changes to the way standard SAP works. In real life, things never turn out that way, and you end up with tons of Z programs and user exits all over the place that also use Z code.
Analysis of the Management Information System Application Related with Service Data at Hajj Regional General Hospital of Makassar

This study analyzes the information system at the Hajj Regional General Hospital of Makassar as a service that is needed for both professional health workers and the served community. The information is about service achievement targets, managerial strategic planning, efficiency and effectiveness of health services.This study aims to obtain information service data that will be utilized by a Management Information System (MIS) which includes computerization needs, information personnel, usage of computers and information system support. The research design is a descriptive survey which was conducted from July to August 2019 and data collection was performed by using questionnaires, observation and document review. The results of the study from 50 respondents showed that 29 respondents (58%) filled in basic data as service data information was very high and very complete (79.87%.) Application of the hospital MIS based on the need for computerization and MIS personnel is very high, which are 88.87% and 89.96%. MIS has a great position and is more important in the hospital business industry due to service information is an indication of health service standards. Hospital MIS is an information system that performs all transaction management therefore information management has results in decision making by providing an indicator for the appearance of health services.One of its functions is to handle the recording of service costs, both for action and medication starting from outpatient care until the patient is discharged from the hospital. This research focuses on inpatient service data information that will be used as MIS, which includes computerization needs, information personnel, and the usage of computers and supporting information systems. Our study revealed that the application of MIS at Hajj Regional General Hospital of Makassar must be supported by the media of communication devices, application programs and human resources who have the profession in information systems and advice, the leadership and administrators that will improve the efforts of MIS which can be handled professionally to create an orderly administration, management and advance the quality of health services.&#x0D; &#x0D; Keywords: Information Systems, Management, Service Data
ANNSESDIHM: An Artificial Neural Network based Signal Enhancement Scheme for Digital Inline Holography (DIH) Microscope

Digital inline holography (DIH) is a proven technique for cell and microparticle analysis. This technique has been used for auto characterization of microparticles. Recently, it has been used for the characterization of complete blood count (CBC) from a whole blood sample, where the comparable results between the traditional LH750 Haematology analyser and the DIH microscope system show a correlation of 0.87 for red blood cells (RBC) and 0.92 for white blood cells (WBC). However, we observed that the DIH microscope system is very sensitive to dust and other foreign microparticles from the surrounding environment. This hampers the quality of the micrograph. Moreover, the non-uniform illumination also hinders the signal quality of the images of the cell diffraction patterns. To overcome these problems, we have investigated various neural network-based signal enhancement schemes. The results show a significant improvement in the signal quality to that of the noisy version. The study shows that convolutional neural network (CNN) architecture is performing better in terms of signal improvement compared to the extreme learning machine (ELM). However, the ELM network is converging faster (within 1 epoch) compared to CNN. Also, we have studied the same with a fully connected network, which shows no improvement in the signal quality even for noises with low variance. The result demonstrates that we can use the unsupervised method, such as autoencoder (CNN) to improve the signal quality.