Reviewerly/exHarmony huggingface dataset:
Adult humour in advertisement: a semiotic study of don’t aviation and mint video

Humour was and is commonly used as a creative means in advertisements. Adult humour specifically commonly used in mature focused products advertisements or in a restrictive timeslot. Don’t Aviation and Mint itself was an advertisement for both an alcohol beverages and mobile phone services with plenty of sexual innuendos that uploaded in Ryan Reynolds’ Youtube channel in which could be accessed even in restricted mode of Youtube. Thus, this study aims to (1) analyse the Don’t Aviation and Mint video for semiotic patterns and (2) elaborate how humour is used in the aforementioned video. It was carried out using a descriptive qualitative approach with semiotic analysis based on the Saussure model of sign. The narrative analysis of the signs unravels the use of humour as an explanatory tool, creative ways in structuring the advertisement and inciting audiences’ attraction. This study also shows that Don’t Aviation and Mint used adult humour in various ways in both monologue and visual aspects. The adult humour itself was presented with varying degree of clarity through farce, slangs, a swear word and emojis.
A Human-Interpretable Machine Learning Approach to Predict Mortality in Severe Mental Illness

Background: Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. However, many trained ML algorithms operate as “black boxes”, producing a prediction from input data without a clear explanation of their workings. Non-transparent predictions are of limited utility in many clinical domains, where decisions must be justifiable.Methods: Here, we apply class-contrastive counterfactual reasoning to ML to demonstrate how specific changes in inputs lead to different predictions of mortality in people with severe mental illness (SMI), a major public health challenge. We produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific changes to the input. We apply it to routinely collected data from a mental health secondary care provider in patients with schizophrenia. Using a data structuring framework informed by clinical knowledge, we captured information on physical health, mental health, and social predisposing factors. We then trained an ML algorithm to predict the risk of death.Results: The ML algorithm predicted mortality with an area under receiver operating characteristic curve (AUROC) of 0.8 (compared to an AUROC of 0.67 from a logistic regression model), and produced class-contrastive explanations for its predictions. Conclusions: In patients with schizophrenia, our work suggests that use of medications like second generation antipsychotics and antidepressants was associated with lower risk of death. Abuse of alcohol/drugs and a diagnosis of delirium were associated with higher risk of death. Our ML models highlight the role of co-morbidities in determining mortality in patients with SMI and the need to manage them. We hope that some of these bio-social factors can be targeted therapeutically by either patient-level or service-level interventions. This approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. This is a step towards interpretable AI in the management of patients with SMI and potentially other diseases.Funding: UK Medical Research Council.Declaration of Interest: RNC consults for Campden Instruments Ltd and receives royalties from Cambridge University Press, Cambridge Enterprise, and Routledge. SB, PL and PJ declare they have no conflicts of interest to disclose.Ethical Approval: The CPFT Research Database operates under UK NHS Research Ethics approvals (REC references 12/EE/0407, 17/EE/0442; IRAS project ID 237953).
An Auto-Approval Approach for Laboratory Test Assessment

Background: Auto-approval is the task of automatically evaluating the consistency of a test result throughout the laboratory information system rather than its manual evaluation by the biochemists. Most of the existing auto-approval systems rely on a rule-based solution obtained from expert knowledge. However, it is a challenging issue to produce a complete and general rule-base for every single test type. To that end, the studies have relied only on a small subset of laboratory tests. Methods: The rule-based auto-approval process was re-investigated in this study, and the rules predetermined by human experts were utilized as a pre-filtering step for grouping the laboratory test result via some common criteria. Subsequently, a machine learning-based approval method, smart-approval, was proposed to approve the tests more precisely. At this point, the expert knowledge in the rule-based pre-filtering was extended by the tendency to imitate the experts’ behavior in the smart-approval step. Two novel datasets (entitled with plot and real-time datasets) containing human experts’ responses to previously studied tests have been used to train the machine learning models. Results: Experiments have been handled on several machine learning models on plot dataset to obtain the trained models based on cross-validation. Here, the random forest classifier provided the best approval performance while also outperforming the approval success of existing studies in the literature. To observe the real-time performance of these trained models, they were also evaluated on real-time unseen data for 4 months. Here, random forest reaffirmed that it was the best approval model. Conclusions: The proposed auto-approval system relying on random forest can provide convincing classification performance on both of the obtained datasets. With the correct approval rate of 98.51%, it surpasses many powerful approval methods in the literature.
Estimation of Clear Sky Normal Irradiance over Northern Nigeria Atmosphere

Energy from the sun is an ideal new energy source for power systems, in a context of sustainable development, enthusiasm for concentrated solar power technologies is developing. Accurate estimation of clear-sky radiation is needed in many engineering, architectural and agricultural applications in order to integrate solar energy into the power grid. An evaluation of the irradiance input to solar power systems is required in many applications. Clear-sky models represent the maximum input of solar power systems, which is especially useful for forecasting solar irradiance and numerical weather prediction. This work examined the application of Yang model to estimate the monthly mean clear sky normal irradiance for northern Nigeria using meteorological variables like temperature, relative humidity and solar radiation considering the shading effect of the complex topography of terrain in Norther region of Nigeria, also to know the variation of beam radiation and diffuse radiation among the selected stations and also to ascertain the significance of aerosols, water vapor, and other transmittances in the estimation of the beam and diffuse radiation in the northern atmosphere. The modeling was computed using monthly mean maximum temperature and relative humidity gotten from the Nigeria Meteorological Agency (NIMET) for the period of fourteen years (1983-1997. The beam and diffuse irradiance for the northern atmosphere is compared by estimating their mean and standard deviation. Also, detailed information about the trend of radiation in each of the selected states in the northern hemisphere of Nigeria was obtained using a graphical method of data analysis. Result reveals that the value of beam and diffused radiation getting to the earth's surface depends on the aerosols, water vapour, atmospheric Ozone, gas transmittance and Rayleigh scattering. From the result above, the maximum beam radiation and the minimum diffused radiation occur during the raining season and the minimum beam radiation and maximum diffuse radiation occur during the dry season. This is due to the variations of these atmospheric constituents (aerosols, water vapour, atmospheric Ozone, gas transmittance and Rayleigh scattering) in the northern atmosphere on these seasons.
XFinder: Detecting Unknown Anomalies in Distributed Machine Learning Scenario

In recent years, the emergence of distributed machine learning has enabled deep learning models to ensure data security and privacy while training efficiently. Anomaly detection for network traffic in distributed machine learning scenarios is of great significance for network security. Although deep neural networks have made remarkable achievements in anomaly detection for network traffic, they mainly focus on closed sets, that is, assuming that all anomalies are known. However, in a real network environment, unknown abnormalities are fatal risks faced by the system because they have no labels and occur before the known anomalies. In this study, we design and implement XFinder, a dynamic unknown traffic anomaly detection framework in distributed machine learning. XFinder adopts an online mode to detect unknown anomalies in real-time. XFinder detects unknown anomalies by the unknowns detector, transfers the unknown anomalies to the prior knowledge base by the network updater, and adopts the online mode to report new anomalies in real-time. The experimental results show that the average accuracy of the unknown anomaly detection of our model is increased by 27% and the average F1-Score is improved by 20%. Compared with the offline mode, XFinder’s detection time is reduced by an average of approximately 33% on three datasets, and can better meet the network requirement.
The use of storytelling techniques in extratextual activities for the development of children's expressive language skills

Language skills, the key elements for children’s development, are often used as a benchmark to measure the development of all abilities he/she possessed. For early childhood, time spent at school is an opportunity for them to develop their language skills, especially expressive language. The storytelling method often found in PAUD is still less innovative. This triggers boredom and result in neglecting learning process that is detrimental to students, especially in improving expressive language skills. The use of relevant techniques in extratextual activities during the learning process combined with the storytelling method will be very helpful for children. There are 12 extratextual techniques for teachers to do and nine techniques that can be done by children. These two categories of extratextual activities can make the storytelling method richer and ensure the achievement of learning targets specifically related to children’s language acquisition. This extratextual activity can trigger the development of children's expressive language in terms of adding new vocabulary, increasing the meaning of old and new vocabulary and developing vocabulary into sentences in everyday conversation. The conditioning carried out in the application of this extratextual activity also has a positive influence in terms of four aspects of language development, namely the development of phonology, semantics, grammar and pragmatics. In phonology, children are strengthened how to pronounce the alphabet correctly; in semantics, children get meaning reinforcement and additional meaning from a vocabulary; on grammar and pragmatics, improvement is given to the arrangement of correct grammar when old and new vocabulary is used in conversation.
Pengembangan Media Interaktif Pengembangan Media Interaktif Berbasis Game Edukasi dalam Meningkatkan Kreativitas Guru Sekolah Dasar di Masa Pandemi Covid-19

The presence of the Covid-19 Virus in the world has not only changed the learning culture, but has become a new challenge in developing teacher creativity in the teaching and learning process. This conventional learning pattern that has evolved into technology-based distance learning requires teachers to be able to adapt and adapt to the limited learning needs of students during the pandemic. Digital technology tools are currently very developed, but they are enough to help educators to adapt to the diverse learning needs of today's students. So it is necessary to use the right learning media in carrying out learning during the pandemic. This training aims to train abilities and skills in increasing the creativity of teachers in using interactive learning media during the pandemic using the Quizizz platform. Quizizz is an interactive game-based online platform that can be used in a fun teaching and learning process during the Covid-19 pandemic by utilizing an Android smartphone. The method used is the lecture method and interactive training by involving the teacher as a simulation model.
Photovoltaic panels identification in aerial images

The solar power generation is increasing rapidly around the world.These systems are often installed on roofstops and other structures that are difficult to access.Despite this difficulty, regular inspection of the system is a requirement to ensure efficiency.As a result, a challenge arise to develop efficient and low-cost inspection methods to monitor structural failures, dirt and other characteristics of the system.Thus, in this work an Unmanned Aerial Vehicle was used to carry out the inspection, obtaining critical images that are necessary to the solar panels maintenance and a machine learning algorithm was applied to identify them.
Reading Promotion Programme on literature appreciation among the young adults in Sri Lankan Schools Conducted by the Ministry of Education

A pilot reading program as an action research project was introduced by The National Institute of Library &amp; Information Sciences (NILIS) in order to inculcate reading appreciation among young adults in Sri Lankan schools. A sample of 25 school libraries from the Maho Educational Zone was selected for the research study. Each grade nine student studying in the above-selected twenty-five schools was given a young adult literature book to be read within fourteen days, after which the student was required to prepare a portfolio based on the book. The school librarians marked the students' portfolio pieces, and this practice was continued up to the beginning of the third semester, using various literary genres. The students completed the given task within the specified period. According to the students' final portfolio evaluation, it was seen that they had gradually developed their competencies under the affective domain based on the above curriculum. Eventually, the Ministry of Education implemented the above program among all the junior and senior secondary schools in Sri Lanka as “Books are the Best Friends” with the assistance of NILIS.&#x0D;
KEMAMPUAN CALON GURU SEKOLAH DASAR DALAM MENGATASI KESULITAN BELAJAR SISWA

The problem of learning difficulties in students does not look at the intellectual abilities of students. Many students with low intelligence can achieve high learning achievement, exceeding the intelligence of students with high intelligence, but it also cannot be denied that high intelligence provides great opportunities for students to achieve high learning achievement. The purpose of this study was to determine the ability of prospective elementary school teachers in overcoming learning difficulties in students. The type of research used is descriptive qualitative research. The approach used in this study is qualitative. The instruments used in this study were observation, interviews, and documentation. The data analysis technique is descriptive qualitative data analysis technique. The results of this study indicate that there are several efforts made by prospective teachers in overcoming learning difficulties experienced by students including difficulties in learning to read, difficulties in learning to write, and difficulties in learning to count. The teacher's way of dealing with students who have learning difficulties. 1) Selecting and determining learning strategies that will be used by teachers to overcome learning difficulties experienced by students. 2) Selecting and establishing a learning approach that is by the conditions experienced by students, 3) Selecting and determining learning methods and techniques that are considered the most appropriate and effective so that they can be used as teacher guidelines in carrying out their teaching activities, 4) Designing and preparing teaching aids that appropriate and interesting for students to help students in the learning process. Many things are done by teachers to overcome learning difficulties. Teachers need to provide opportunities for students to ask things that have not been understood about the lessons described, provide guidance to students who have learning difficulties, be it group guidance or individual guidance.
Design and validation of an electronic teaching case on critical thinking

Based on the teaching case methodology, a case was designed and validated in an electronic format (e-case), about critical thinking in relation to violence in the teenage couples. The design considers a situation-problem in the form of narrative, exposed to the high school students, who through a series of questions and activities proceed to the analysis, take a position and propose solutions. The e-case was carried out on HTML5 using WIX.com, and contains the following entries: introduction, characters, story, questions, activities, evaluation and contact. This article reports the techno-pedagogical design and the validation by experts and with the target population. The results were favorable and coincident regarding the content, didactic treatment and design of the website. Likewise, guidelines are provided for the design of an e-case and validation model for those who wish to get involved in this strategy.
Solving assignment problems via Quantum Computing: a case-study in train seating arrangement

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should resolve complex problems currently considered to be intractable.This new research area is called Quantum Computing.What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems.This work focuses on solving assignment-like combinatorial optimization problems by exploiting this novel computational approach.A case-study, denoted as the Seating Arrangement Optimization problem, is considered.It is modeled through the Quadratic Unconstrained Binary Optimization paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv, and a quantum-classical hybrid system.The obtained experimental results are compared in terms of solution quality and computational efficiency.
Motion-Augmented Self-Training for Video Recognition at Smaller Scale

The goal of this paper is to self-train a 3D convolutional neural network on an unlabeled video collection for deployment on small-scale video collections. As smaller video datasets benefit more from motion than appearance, we strive to train our network using optical flow, but avoid its computation during inference. We propose the first motion-augmented self-training regime, we call MotionFit. We start with supervised training of a motion model on a small, and labeled, video collection. With the motion model we generate pseudo-labels for a large unlabeled video collection, which enables us to transfer knowledge by learning to predict these pseudo-labels with an appearance model. Moreover, we introduce a multi-clip loss as a simple yet efficient way to improve the quality of the pseudo-labeling, even without additional auxiliary tasks. We also take into consideration the temporal granularity of videos during self-training of the appearance model, which was missed in previous works. As a result we obtain a strong motion-augmented representation model suited for video downstream tasks like action recognition and clip retrieval. On small-scale video datasets, MotionFit outperforms alternatives for knowledge transfer by 5%-8%, video-only self-supervision by 1%-7% and semi-supervised learning by 9%-18% using the same amount of class labels.
Evaluation of E-Resources in Madurai Kamaraj University Library: A User-Centered Approach Towards Collection Development

University Libraries play an indispensable role in promoting higher education by developing varied resource collections and providing multiple services. Since the resources are meant for the users, their perception about the different collection is very significant. This paper examines the evaluation if electronic resources by users of Madurai Kamaraj University Library. The study is intended to know the user satisfaction level regarding the various types of E-Resources which can be used as a tool in collection development. A survey was conducted among the respondents and the study format that majority of the users rated E Resources in the library as Good and they were satisfied to a large extent with these resources.
AN ANALYSIS OF LESSON PLAN IMPLEMENTATION THROUGH ZOOM IN TEACHING WRITING BASED ON INTERNATIONAL PRIMARY CURRICULUM IN SUNRISE SCHOOL BALI

The present research studied teachers’ ability and constraints in preparing and implementing the lesson plan for writing through Zoom during the pandemic Covid-19 in the Year 6 Primary School Denpasar. The research was designed using a Mixed Method. Data were collected by means of five instruments and the obtained data on the five variables were analyzed descriptively to measure the arithmetic means, range, standard deviation, and variance. This study showed that the teachers’ ability in preparing for lesson plan were categorized in high category. The teachers’ ability in implementing the lesson plan were also categorized as high category. Some constraints were encountered by the teachers in preparing and implementing lesson plan for writing through Zoom. It is suggested that EFL students and teachers to be trained to improve the use of online learning as well as their literacy in information technology, especially the one related to EFL instruction online.
Collusion using blockchain

Most of the literature published to date focuses on issues of collusion between blockchain participants (see Chapter 8). However, there is much less discussion regarding how companies that operate in the “real space” could use blockchains to facilitate the creation and operation of illegal agreements. Against this background, the current chapter starts by exploring the different forms that such collusion could take. I first consider the impact of smart contracts, and the specific issues they entail (Section 1). The next section analyzes the stability of such agreements. It notably highlights factors that influence this stability (Section 2). I conclude by explaining how these can and should be detected and prosecuted (Section 3).