Reviewerly/exHarmony huggingface dataset:
Integrated Media Platform-based Virtual Office Hours Implementation for Online Teaching in Post-COVID-19 Pandemic Era

In post-COVID-19 pandemic era, students' learning effects and experience may sharply decrease when teaching is transferred from offline to online.Several tools suitable for online teaching have been developed to guarantee and promote students' learning effects.However, they cannot fully consider teacher-student interaction in online teaching.To figure out this issue, this paper proposes integrated media platform-based virtual office hours implementation for online teaching.Specifically, an integrated media platform (IMP) is first constructed.Then, virtual office hours (VOH) is implemented based on the IMP, aiming at increasing studentteacher interactions.For evaluating the effectiveness of this scheme, 140 undergraduate students using IMP are divided into one control group and three experimental groups that respectively contain text, voice and video modes.The experiment results indicate that applying VOH in the IMP can improve students' online presence and test scores.Furthermore, students' participating modes during VOH implementation can largely affect their degree of presence, which can be well classified by using principal component analysis.The implication of this work is that IMP-based VOH is an effective and sustainable tool to be continuously implemented even when the COVID-19 pandemic period ends.
THINKING DEVELOPMENT IN MATHEMATICS TEACHING

The article considers the need for the development of thinking in the process of teaching mathematics at school. The documents adopted on improving education in the republic, as well as the republican test, emphasize the importance of students mastering mathematical knowledge. As stated in the article, favorable conditions for the development of thinking are created in teaching mathematics. Mathematics itself is a subject that studies the numerical and spatial relationships of reality. The student’s thinking is closely connected with the process of assimilation of educational material. The article also notes that the process of thinking occurs in parallel with the perception of knowledge, the preservation in memory and the application of knowledge in practice.
Реклама как утопический конструкт: новые тенденции мифотворчества в современном информационном обществе

In the modern world people’s dreams about comfort are coming true, but we do not cease to dream because fantasizing is immanent to human nature and is a basis of creativity and realization of our desires. In an information society, this deeply individual process of desiring is organized by social institutions with the help of an information infrastructure, largely relied on advertising technologies. Advertising is adapts to the individual of the consumerist society who has evolved an immunity to the discourse of things; yet it is not losing its utopic function offering us ready constructs for symbolic consumption.
When a Computer Cracks a Joke: Automated Generation of Humorous Headlines

Automated news generation has become a major interest for new agencies in the past. Oftentimes headlines for such automatically generated news articles are unimaginative as they have been generated with ready-made templates. We present a computationally creative approach for headline generation that can generate humorous versions of existing headlines. We evaluate our system with human judges and compare the results to human authored humorous titles. The headlines produced by the system are considered funny 36\% of the time by human evaluators.
Exposure of 3D-Stereoscopic Videos Defects using Binocular Disparity

3D video quality issues that may bother the human visual systemand adversely affect the 3D seeing knowledge are notable and turn out to be more applicable as the accessibility of 3D video content increments, fundamentally via 3D silver screen, yet additionally via 3D TV. In this paper, we introduce four algorithms that adventure accessible stereo uniqueness data, keeping in mind the end goal to distinguish aggravating stereoscopic impacts, specifically Stereoscopic Window Violations (SWV), bowed window impacts, UFO items and profundity bounce cuts on stereo videos. In the wake of distinguishing such effects, the introduced algorithms describe them, in light of the pressure they cause to the watcher's visual framework. Subjective agent illustrations, quantitative test comes about on a uniquely crafted video dataset, a parameter variation study and remarks on the calculated multifaceted nature of the algorithms are given, with a specific end goal to survey the precision and execution of stereoscopic quality deformity discovery.
Teknik Pembelajaran Kolaboratif untuk Pembelajaran Bahasa Inggris Bagi Guru-Guru Bahasa Inggris di SMK Dharma Utama Sidomulyo Lampung Selatan

English which has a different grammar than Indonesian and regional languages ​​causes difficulties for students who use the language. One factor that can affect student learning outcomes is the use of effective and efficient learning techniques. A solution to motivate students in learning English is by using a variety of learning techniques. One learning technique that can be used by teachers in teaching English is collaborative learning techniques. The targets of the implementation of this community service activity are the teachers in the field of English study at SMK Dharma Utama Sidomulyo. From this activity, the results and benefits from this service were provided to provide knowledge about collaborative techniques and their types that can be used in learning English which contain explanations and use of techniques used in learning activities. In addition, teachers also get the opportunity to use collaborative techniques that are guided by service teams.
Research on the development mode of digital channels of commercial banks based on blockchain finance

—In 2021, blockchain was included in the national five-year plan for the first time, and in the chapter of “Accelerating Digital Development and Building Digital China”, blockchain was listed as one of the seven key industries of digital economy in the 14th Five-Year Plan. The rapid development of blockchain technology not only promotes the high-speed operation of China’s real economy and greatly improves the overall service quality of China’s digital economy, but also provides new development ideas for the innovative development of digital channels of commercial banks. In this paper, through the use of literature research method and qualitative analysis method, we deeply analyze the challenges faced by the development mode of commercial banks at the present stage, etc. Accordingly, we propose corresponding countermea-sures and suggestions from the perspectives of research and development of technology, customer privacy protection and improvement of service efficiency, so as to continuously empower the digital transformation of commercial banks in China.
Automatic detection of acute lymphoblastic leukemia using machine learning

The identification of acute leukemia blast cells in colored microscopic images is a challenging task. Usually, visual assessment for microscopic images of blood samples is performed. However, considering the quick advances in utilizing different image processing techniques, rapid and more accurate assessment can be achieved. Therefore, this paper proposes an enhanced automatic method to detect Acute Lymphoblastic Leukemia (ALL) utilizing microscopic blood sample images. Our proposed methodology includes Color-based segmentation using the K-means clustering technique with morphological feature extraction algorithms and cell classification. The proposed method was tested on blood microscopic images from the ALL-IDB1 database, University of Milano, Italy. ALL_IDB1 is comprised of 108 blood cell images (healthy and leukemia) in which the lymphocytes are labeled by expert oncologists. The accuracy achieved was 97.2%. This relatively high accuracy suggests that the colour-based method for segmentation was more efficient and acceptable compared to the traditional thresholding methods and the methods that do not consider the overlapped cells.
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation.

Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT. To this end and we propose a transfer learning solution. Specifically and 1) A vanilla Transformer is pre-trained on massive bilingual text-only corpus to obtain prior knowledge; 2) A multimodal Transformer named VLTransformer is proposed with several components incorporated visual contexts; and 3) The parameters of VLTransformer are initialized with the pre-trained vanilla Transformer and then being fine-tuned on MMT tasks with a newly proposed method named cross-modal masking which forces the model to learn from both modalities. We evaluated on the Multi30k en-de and en-fr dataset and improving up to 8% BLEU score compared with the SOTA performance. The experimental result demonstrates that performing transfer learning with monomodal pre-trained NMT model on multimodal NMT tasks can obtain considerable boosts.
Neural Contract Element Extraction Revisited

We investigate contract element extraction. We show that LSTM-based encodersperform better than dilated CNNs, Transformers, and BERT in this task. We alsofind that domain-specific WORD2VEC embeddings outperform generic pre-trainedGLOVE embeddings. Morpho-syntactic features in the form of POS tag and tokenshape embeddings, as well as context-aware ELMO embeddings, do not improveperformance. Several of these observations contradict choices or findings ofprevious work on contract element extraction and generic sequence labelingtasks, indicating that contract element extraction requires carefultask-specific choices.
A Case Study on English Curriculum Design for non-English Major Graduates

As a higher level of English teaching system in China, postgraduate English teaching is facing a comprehensive reform from teaching content to curriculum system.Based on researches of scholars on postgraduate English curriculum and teaching mode, and combined with the practice of postgraduate English teaching in our university, this paper adopts the form of questionnaire and interview to analyze and discuss the postgraduate English curriculum, teaching mode, students' needs and their English application ability.By putting forward the corresponding curriculum system construction scheme, it provides a new teaching conception and attempt for further English teaching reform in our university.
Popularity of University based on Sentiment Analysis in Social Network Media

Since the emergence of the social media, many studies are conducted on social media to gain information on social media users. Among these studies are sentiment analysis which is an analysis of user sentiments and emotions towards an object, term, or event based on what they post. Sentiment analysis are often conducted on sites like Facebook and Twitter because of their huge number of users and popularity. This paper aims to create a GUI-based sentiment analysis application to find out popularity of universities based on Twitter user’s sentiment. For this purpose, we firstly collected 600 tweets datasets, which is a mixture of 200 tweets each from Princeton University, Stanford University and University of Oxford for a period of 4 days (12/1/2018 to 15/1/2018). Second, the tweets were classified based on their sentiment into “positive”, "neutral" and “negative” tweets. Finally, the results were being analyzed in terms of Precision, Recall and F1 score. These information will help universities to gather information of public sentiment towards their institution and allow them to recognize their strength and weakness. Universities can use that information to improve their public image if needed in the future.
The Impact of Customer Retention Strategies and Operations at Emirates Airline

Many of the studies suggest that the retention strategies are important for survival of any business . Hence arrangement of resources in time , communication on time, effective operations and handling the customers in a sophisticated way. Adhering to the rules and regulations of the airline and following all the safety measures in order to ensure the smooth operations coordinating with other department in terms of shortage, pre-planning of the additional resources and by conducting safety briefing. ensuring the customers stick to the schedules.
Automatically Designed Deep Gaussian Process for Turbomachinery Application

Abstract Thanks to their flexibility and robustness to overfitting, Gaussian Processes (GPs) are widely used as black-box function approximators. Deep Gaussian Processes (DGPs) are multilayer generations of GPs. The deep architecture alleviates the kernel dependance of GPs, while complicates model inference. The so-called doubly stochastic variational approach, which does not force the independence between layers, shows its effectiveness in large dataset classification and regression in the literature. Meanwhile, similar to deep neural network, DGPs also require application-specific architecture. In addition, the doubly stochastic process introduces extra hyperparameters, which further increases the difficulty in model definition and training. In this study, we apply doubly stochastic variational inference DGP as surrogate model on high-dimensional structural data regression drawn from turbomachinery area. A discrete optimizer, which is based on classification discriminating good solutions from bad ones, is utilized to realize automatic DGP model design and tuning. Empirical experiments are performed firstly on analytical functions to demonstrate the capability of DPGs in high-dimensional and non-stationary data handling. Two industrial turbomachinery problems with respectively 80 and 180 input dimensions are addressed. The first application consists in a turbine frame design problem. In the second application, DGP is used to describe the correlation between 3D blade profiles of a multi-stage low pressure turbine and the corresponding turbine total-total efficiency. Through these two applications, we show the applicability of the proposed automatically designed DGPs in turbomachinery area by highlighting their outperformance with respect to classic GPs.
A Comparative Study of Dendrite Neural Networks for Pattern Classification.

Dendrite neurons are an alternative for classiﬁcation tasks, providing competitive results when compared to typical classiﬁcation methods. Dendrite networks allow each dendrite to build a close boundary to assign each incoming pattern to its respective class. Hyperboxes, hyperellipsoids and hyperspheres are novel ways for dendrite computing. In this research we test these models and some hybrid variances trained by stochastic gradient descent. Results show that hyperellipsoid work well as classiﬁers with low-dimensional tasks, while hyperspheres score better than the others in the case of image processing. However, when hybridizing, hyperboxes show poor results but hyperellipsoid and hyperspheres obtain even better results than two layer prerceptrons for many datasets.
From Research to Proof-of-Concept: Analysis of a Deployment of FPGAs on a Commercial Search Engine.

FPGAs are quickly becoming available in the cloud as a one more heterogeneous processing element complementing CPUs and GPUs. There are many reports in the literature showing the potential for FPGAs to accelerate a wide variety of algorithms, which combined with their growing availability, would seem to also indicate a widespread use in many applications. Unfortunately, there is not much published research exploring what it takes to integrate an FPGA into an existing application in a cost-effective way and keeping the algorithmic performance advantages. Building on recent results exploring how to employ FPGAs to improve the search engines used in the travel industry, this paper analyses the end-to-end performance of the search engine when using FPGAs, as well as the necessary changes to the software and the cost of such deployments. The results provide important insights on current FPGA deployments and what needs to be done to make FPGAs more widely used. For instance, the large potential performance gains provided by an FPGA are greatly diminished in practice if the application cannot submit request in the most optimal way, something that is not always possible and might require significant changes to the application. Similarly, some existing cloud deployments turn out to use a very imbalanced architecture: a powerful FPGA connected to a not so powerful CPU. The result is that the CPU cannot generate enough load for the FPGA, which potentially eliminates all performance gains and might even result in a more expensive system. In this paper, we report on an extensive study and development effort to incorporate FPGAs into a search engine and analyse the issues encountered and their practical impact. We expect that these results will inform the development and deployment of FPGAs in the future by providing important insights on the end-to-end integration of FPGAs within existing systems.
Visual sentiment analysis of bank customer complaints using parallel self-organizing maps

Social media has reinforced consumer power, allowing customers to obtain more and more information about businesses and products, voice their opinions as well as convey their grievances. In this article, we introduce a descriptive analytics system for visual sentiment analysis of customer complaints using the self-organizing feature map (SOM). The network eventually learns the underlying classification of grievances that can then be visualized using different methods too. Executives of analytical customer relationship management (ACRM) will derive valuable business insights from the maps and enforce prompt remedial measures. We also propose a high-performance version of the CUDASOM (Compute Unified Device Architecture (CUDA)-based self-organizing function Map) algorithm implemented using NVIDIA®&apos;s parallel computing platform, CUDA, which accelerates the processing of high-dimensional text data and produces fast results. The effectiveness of the proposed model has been demonstrated on a dataset of customer complaints about the products and services of four leading Indian banks. CUDASOM recorded an average speedup of 44 times. Our technique can expand studies into smart grievance redressal systems to provide the complaining consumers with quick solutions.
Approach to M-learning acceptance among university students: An integrated model of TPB and TAM

A growing number of higher education institutions have adopted tools to promote mobile learning. However, studies into the driving factors of its adoption are insufficient. This article identifies the aspects that have an effect on the adoption of mobile learning (m-learning) among university students. The theory of planned behavior (TPB) and technology acceptance model (TAM) have been shown to be valid and powerful models in the research on the adoption of learning technologies. Based on TPB and TAM, we propose a model to explain how perceptions influence m-learning adoption among Colombian university students. To confirm the acceptability of the model, a self-administered questionnaire was applied to 878 undergraduate university students from the Instituto Tecnologico Metropolitano (ITM), a higher education institution in Colombia The results suggest that all of the constructs of TPB and TAM have a moderate impact on the intention to adopt m-learning. Specifically, perceived usefulness and attitude have a significant influence on students’ acceptance of m-learning. These results can stimulate future research and promote an effective diffusion of m-learning in developing countries.
Big data regression via parallelized radial basis function neural network in Apache Spark

Among many versatile neural network architectures, radial basis function neural network (RBFNN) is one that is used to address classification and regression problems. The supervised and unsupervised parts of the RBFNN are not able to handle a large volume of data. The proposed work overcomes this drawback; it has implemented a parallel distributed version of RBFNN implemented with Apache Spark. Henceforth, we will refer to this version of RBFNN as PRBFNN. Incorporating K-means|| or parallel bisecting K-means in-between the input and the hidden layer, and the employment of parallel least square estimation (LSE) with an outer product of matrices is a novel contribution in the work. The PRBFNN employed Gaussian as well as logistic activation functions (AFs) in the hidden layer for nonlinear transformation. The efficacy of the PRBFNN was analyzed with two real-world datasets under 10-fold cross-validation (10-FCV) setup for a regression problem. Here, we want to make a point of presenting the PRBFNN to handle regression in the big data paradigm.
Counseling On How To Store Medicine That Is Good and Correct

Storage is an activity to store and maintain by placing received drugs in a place deemed safe from theft as well as physical disturbances that can damage the quality of the drug. Mistake in storing drugs will affect the quality and content of the active drug substance. The stability and properties of the drug will also be affected if the storage is still wrong. This will make the duration of drug treatment longer because the drug's efficacy has decreased. The problem in this research is the low level of public knowledge about how to store medicines properly and correctly in Sungai Bakung Village. With these problems, researchers are interested in conducting research in Sungai Bakung Village, Banjarmasin Timur District because of the flood disaster, research was carried out in the vicinity of the residence. The purpose of this study was to provide information to the public by distributing educational leaflets on how to store medicines properly.This research method for the sampling technique was carried out randomly with 10 respondents. Data collection was carried out using pre-test and post-test questionnaires, as well as distributing educational leaflets on proper and correct drug storage methods.The results of the evaluation of the participants 'knowledge and attitudes towards drug storage were carried out through visits to the homes of the participants in the mentoring activities and from the results of the average value of the respondents' answers during the pre-test (51%) and post-test (89%) that is done shows an increase.
Open Art: Methods for Curing Hypothyroidism using Reinforcement Learning

The invention is a process for curing hypothyroidism with three steps. First, blood is measured to detect thyroid hormone concentration with a synthetic device. Second, the measured concentration is compared to a target thyroid hormone concentration using a convolutional neural network. Third, a reinforcement learning agent controls a drug delivery valve, optimizing thyroid hormone levels.
FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning

The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Language Processing tasks in recent years. Pretrained models usually differ in pretraining objectives, architectures, and datasets they are trained on which can affect downstream performance. In this contribution, we fine-tuned German BERT and German ELECTRA models to identify toxic (subtask 1), engaging (subtask 2), and fact-claiming comments (subtask 3) in Facebook data provided by the GermEval 2021 competition. We created ensembles of these models and investigated whether and how classification performance depends on the number of ensemble members and their composition. On out-of-sample data, our best ensemble achieved a macro-F1 score of 0.73 (for all subtasks), and F1 scores of 0.72, 0.70, and 0.76 for subtasks 1, 2, and 3, respectively.