Reviewerly/exHarmony huggingface dataset:
RACISM MITIGATION AMONG MALAYSIANS THROUGH THE APPLICATION OF PUBLIC SERVICE ANNOUNCEMENTS (PSAs)

Public Service Announcement (PSAs) works as medium of communication to mass ranged of audience. In fact, PSAs have been utilized as a mechanism of informing and educating society on social cases and promotes behavioral changes. Roehm (2018) and Jolani et al.(2017) both indicate PSAs is a tool to educate people with the intention to urge positive action whilst increasing awareness level of the audiences on the highlighted issues. The social issues of racism in Malaysia still occurred even after celebrating it’s 62nd independence. As a multiracial and multicultural country, racism is a threat than can gives negative impact to the country’s national security, harmony and racial unity. Hence this study is to explore and discuss on Public Service Announcement (PSAs) and its applications to mitigate racism among Malaysians. The aim of this study is to propose a guideline on designing an impactful PSAs in order to cater social issues such as racism in Malaysia. This research will contribute as an additional knowledge for advertising agencies, designers and academic syllabus.
A Lightweight Convolutional Neural Network for Camera ISP

Mobile cameras are ubiquitous in our daily life. The widely used sRGB (standard RGB) images are generated from the Raw data by a series of image signal processing (ISP) operations, such as demos acing, white balance, tone mapping, etc. In this paper, we realize the camera imaging process based on a convolutional neural network, that is, using an end-to-end convolutional neural network instead of the traditional image processing algorithm. Specifically, we first propose a pyramid U - N et for camera ISP and then optimize it to a light weight network. In addition, we extend the trained model to different mobiles by using a few images to finetune the pre-trained network. Experimental results demonstrate that the proposed network achieves robust ISP result with the fewest parameters and inference time.
IsSwap: Deep Fake Detection

In this era of technology the intake of information through digital media has grown exponentially and it has provided people with personal motives to spread falsified information among the masses to create biased opinions and a sense of unrest. The falsified information is provided to the people especially during elections to create political unrest among the masses or simply to spread a rumour. Since most of the information consumed by people is in the form of videos, it has become a great target spot for people with malicious intent. Deep-Fake is an emerging threat to celebrities, political faces and JSON common people. The paper is aimed at overcoming the given challenge by providing a fast and reliable method to determine the authenticity of a given video. We have proposed a model for detecting deep fake videos via XceptionNet and ResNet50 together with multiple hidden layers of neural networks. The results shows that the proposed method outperformed other state-of-the-art methods in terms of precision and recall rate and performed well in predicting whether the video is fake or real.
Dual Model Weighting Strategy and Data Augmentation in Biomedical Question Answering

Biomedical Question Answering aims to extract an answer to the given question from a biomedical context. Due to the strong professionalism of specific domain, it’s more difficult to build large-scale datasets for specific domain question answering. Existing methods are limited by the lack of training data, and the performance is not as good as in open-domain settings. We propose a model weighting strategy for the final answer prediction in biomedical domain, which combines the advantage of two models, open-domain model QANet and BioBERT pretrained in biomedical domain data. Especially, we adopt effective data augmentation strategies to improve the model performance, including round-trip translation and summarization. The public biomedical dataset collected from PubMed provided by BioASQ is used to evaluate our approach. The results show that the model performance has been improved significantly on BioASQ 6B, 7B and 8B datasets compared to the single model.
Stance Detection Using Multi-Head Attention Based Bidirectional GRU

In social media, the purpose of stance detection is to analyze and judge the stance held on specific topics contained in the text, that is, support, opposition, and neutrality. This kind of data mining for social network stance tendencies has gradually become an important research direction. Stance detection is regarded as a sub branch of sentiment analysis, and in previous study, the comments stance are identified by machine learning models successfully. These models extract some semantic features, emotion features and so on. In the study, we extract the most significant features and build a feature-rich Support Vector Machine (SVM) that outperforms these models. We introduce a multi-head attention-based bidirectional Gated Recurrent Unit Network (MHA-BiGRU) to detect stance comments in a given corpus. The experimental results show that the model we proposed surpass the general model of the baseline models and get expected results.
IMPACT OF ISO TOTAL QUALITY MANAGEMENT PRINCIPLES ON DEPARTMENT OF MUNICIPAL AFFAIRS AND TRANSPORT

Total quality management (TQM) is a collection of strategies for constantly improvement inside organisations. The government of UAE has made considerable measures towards the adoption of TQM. Despite that, many organisations in UAE are not able to adopt the principles of TQM. The purpose of this study is to explore the influence of TQM principles proposed by the International Organization for Standardization on Department of Municipal Affairs and Transport quality. This study employed quantitative technique to address research questions. The conclusion of this study reveals that department of municipal affairs and transport industries need to adopt the principles of TQM according to the seven principles of ISO to achieve high Department of Municipal Affairs and Transport Quality. This study recommends that transport industries in UAE should establish a department of quality office in each college headed by specialist managers in TQM who will be responsible for following-up and monitoring the application of TQM like continuous improvement, teamwork spirit, and sharing decision-making.&#x0D; &#x0D; JEL: H12; H50&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0971/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Correlational analysis of incident factors in the academic performance of university higher education students under the context of COVID-19

The COVID-19 pandemic changed the teaching modality radically, which led to virtual education in institutions, which had to quickly adapt as well as students. The research was carried out with a non-experimental, descriptive and correlational quantitative approach, applied to a sample of 1804 students from a public university, two statistical studies were carried out, one supported by the academic database and the second through the application of a validated instrument to identify which factors have the greatest influence on the academic performance of students in the virtual modality. In the statistical analysis, it was found that academic performance improved in the virtual semester with respect to the face-to-face one; demographic factors: gender, age, study cycle, and career groups had a significant influence on academic performance. Finally, no significant influence was found between the technological and methodological dimensions with the academic performance of the virtual semester.
@DNEG VFX showcase

other Share on @DNEG VFX showcaseSIGGRAPH '21: ACM SIGGRAPH 2021 Computer Animation FestivalJuly 2021 Article No.: 5Pages 1https://doi.org/10.1145/3446562.3458692Published:27 July 2021Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months12Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Novel GAN based User Desensitization Data Generation Algorithm

In recent years, how to use user data rationally has become a hot topic of discussion. In order to solve the problem that existing user desensitised data generation methods lead to the unavailability of the user profile analysis function of applications, this paper proposes a generative adversarial network-based algorithm for generating user privacy desensitised data, which improves the usability of the data while ensuring data privacy. The algorithm is based on generative adversarial networks, using a differential autoencoder to extract user data features and migrate the features to the desensitised data; it also uses a multi-headed attention mechanism to intensively extract the main features of the user data; in order to ensure that the desensitised data can be used for the Android app's own user portrait analysis, its discriminator uses the app's own neural network learning model to adjust the output of the generator. Due to the stochastic nature of the differential auto-encoder learning process and the approximation process, the desensitised data are independent of the user's original data, thus protecting the user's private data. The desensitised data are guided by the discriminator with the characteristics of the original data, and the results of user profiling based on this desensitised data are approximately the same as the results of the original data analysis, which protects user privacy without compromising profiling.
Corpora in the Classroom

Corpus Linguistics (CL) is a research approach to the empirical investigation of language variation and use, utilizing a quantifiable collection of “texts” known as a corpus (corpora, plural) and various computer-based tools and applications.
Efficient (k, n)-threshold secret sharing method with cheater prevention for QR code application

&lt;p&gt;To protect secret message, secret sharing technique divides it into n shares and distributes them to n involved participants. However, it is hardly to prevent a dishonest participant to cheat other by providing a fake share. To overcome this weakness, this paper presents an efficient (k, n)-threshold secret sharing approach with the functionality of cheater identification using meaningful QR codes. The secret message would be split into k pieces, and used as the coefficients of polynomial function to generate n shares. These shares would be concealed into cover QR codes based on its fault tolerance to generate meaningful QR code shares. The meaningful QR code shares are helpful to reduce the curiosity of unrelated persons when transmitted in public channel. The legitimacy of QR code share would be verified before secret reconstruction to prevent cheater in secret revealing procedure. Some experiments were done to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is efficient, highly secure and highly robust, and it also achieves a higher embedding capacity compared to previous methods.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Comparative Performance Study of Classification Models for Opinion Mining

Opinion mining has become a major part in today's economy. People would want to know more about a product and the customers opinion before buying it. Companies would also want to know the opinions of the customers. Therefore, analyzing the customer’s opinion is important. A new customer would consider a product as good by analyzing the opinions of other customers. The opinions are collected from various areas, which include blogs, web forums, and product review sites. Classifying these large set of opinions requires a good classifier. In view of this, a comparative study of three classification techniques - Naive Bayes classifier with Kernel Density Estimation (KDE), Support Vector Machine (SVM), Decision Tree and KNN was made. To evaluate the classifier accuracy, precision, recall and F-measure techniques are used. Experimental results show that the Naive Bayes with Kernel Density Estimation (KDE) classifier achieved higher accuracy among others.
Image Aesthetic Description Based on Semantic Addition Transformer Model

Image aesthetic quality assessment has been a hot research topic in the field of image analysis during the last decade. Most recently, people have proposed comment type assessment to describe the aesthetics of an image using text automatically. However, existing works have rarely considered the quality of the aesthetic description. In this work, we propose a novel neural image aesthetic description network framework, named Deep Image Aesthetic Reviewer (DIAReviewer), based on Semantic Addition Transformer Model, the learning of Residual Network, and the Attention Mechanism in a single framework. Beyond that, we design a Semantic Addition module to compromise the image feature and semantic information to focus on the comment quality, such as fluency and complexity. We introduce a new image dataset named Aesthetic Review Dataset (ARD), which contains one or more aesthetic comments for each image. Finally, the experimental results on ARD show that our model outperforms other methods in content complexity and sentence fluency of aesthetic descriptions.
Application and Comparative Assessment of Data Mining and Time Series Forecasting Models to Indian Coal Mining Production and Employment Parameters

Forecasting is designed to help decision making and planning before the actual event occurs. The main purposeof the application of time series forecasting models to Indian mining data is to get insight into the wide-rangingprinciples and methodologies for forecasting various parameters as well as current trends and future perspectives.This paper highlights the application of some major methods of time series forecasting such as the AutoregressiveIntegrated Moving Average (ARIMA) method, Regression method, Fuzzy Time Series method, Group Method ofData Handling (GMDH Model), and Neural Networks. Based on a series of comparative analyses depending uponthe capabilities and limitations of each model, the perspective of the multi-model based forecasting approach ispresented.
Emotion analysis method based on domain dictionary and machine learning

With the continuous generation of interactive data on the Internet, the value behind these data needs to be tapped. By constructing a scoring system based on domain dictionary and using machine learning model, the emotional information behind specific domain data can be effectively tapped, thus providing reference value for enterprise decision-making and other social activities.
The Research Integrity Arms Race and the Role of Specialists to maintain the Integrity of the published Record

The APE Conferences aim to provide a better understanding of scholarly communication and the role of information in science, education and society. They encourage debate about the future of value-added scientific publishing, information dissemination and access to scientific results and offer an independent forum for open discourse.
Gt-Simnet: Improving Code Automatic Summarization Via Multi-Modal Similarity Networks

Code summarization aims to generate high-quality functional summaries of code snippets to improve the efficiency of program development and maintenance. It is a pressing challenge for code summarization models to capture more comprehensive code knowledge by integrating the feature correlations between the semantics and syntax of the code. In this paper, we propose a multi-modal similarity network based code summarization method: GT-SimNet. It proposes a novel code semantic modelling method based on a local API dependency graph (local-ADG), which exhibits an excellent ability to mask irrelevant semantics outside the current code snippet. For code feature fusion, GT-SimNet uses the SimNet network to calculate the correlation coefficients between local-ADG and AST nodes and perform fusion under the influence of the correlation coefficients. Finally, it completes the prediction of the target summary by the code generator. We conduct extensive experiments to evaluate the performance of GT-SimNet on two Java datasets. The results shows that GT-SimNet achieved BLEU scores of 38.73% and 39.22% on the two datasets, respectively, 1.58%~2.23% higher than the best existing baseline. Importantly, GT-SimNet reduces the BLEU scores by 20% after removing local-ADG. This indicates that local-ADG is effective for the semantic representation of the code.
Reject-Inference

Rejects had not the opportunity to perform. Marginal Rejects are often cherry-picked based upon other data, or cheapened through down-sells, which distorts an Accepts-only model. Reject inference addresses resultant distortions but is contentious. (1) <bold>The basics</bold>—i) pointers—basic considerations; ii) missing at random, or not; iii) terminology—data manipulation, allocation, methodology; iv) characteristic analysis—for reject inference; v) swap-set analysis—proposed versus past; v) population flow diagram. (2) <bold>Intermediate models</bold>—especially ‘known Good/Bad’, which may use bureaux’s performance data. Others are Accept/Reject and Cashed/Uncashed. Possible formulae are provided for extrapolated performance assignments. (3) <bold>Inference smorgasbord</bold>—i) supplementation; ii) performance surrogates; iii) reject is Bad; iv) augmentation; v) weight of evidence (WoE) adjustments; vi) iterative reclassification; vii) extrapolation of accept performance. (4) <bold>Favoured technique</bold>—involving i) fuzzy-parcelling—record cloning and weight adjustments; ii) extrapolation—graphical setting of performance-adjustment parameters; iii) attribute-level adjustments—where needed; v) practicalities—variable names and coding, with an example.
Prototype Design of Bukaloka Mobile Marketplace Application Using Five Planes Method

Bukaloka is an application from Bali which is engaged in tourism regarding e-vouchers. The e-voucher service provided by Bukaloka allows users to easily buy and exchange e-vouchers without the need to print vouchers. The Bukaloka Marketplace application is not yet available for the mobile platform, so it does not facilitate access for mobile users. User Experience design is needed to facilitate application development, as well as user acceptance. The User Experience design method used is Five Planes so that the basic foundation that produces a prototype with a high fidelity level can be tested to resemble the final application. Testing the prototype using the User Acceptance Test Method with a Likert's Summated Rating. The result is that the Marketplace prototype gets a score of 815 which falls into the very good category range.
Setting up Hadoop Environment and Running MapReduce Job for Teaching BDA in Universities – An Experience

Apache Hadoop is a collection of open source software libraries for processing and analysis of big data. Hadoop is popular for its flexible and scalable architecture that stores and processes big data on commodity hardware machines and allows distributed processing on clusters of nodes. The demand for trained data analytics professionals has resulted in the incorporation of big data analytics in University curriculum. As a result, there is a need to set up a Hadoop environment in University labs, for teaching big data analytics. Though, detailed information for setting a multi-machine Hadoop is readily available, however, several additional configurations are required for setting up a cluster and for running a big data application. This paper discusses the experiences and challenges faced during the setup of a multi-node Hadoop cluster and running of MapReduce jobs. It presents system settings and working memory configurations that are required to be done for Hadoop and for running an application. This will benefit the academia for setting up a Hadoop cluster and to run data analytics applications on it.
Constrained harmonization algorithm for pooling multi‐site datasets

Abstract Background Pooling datasets from multiple studies can significantly improve statistical power: larger sample sizes can enable the identification of otherwise weak disease‐specific patterns. When modern learning methods are utilized (e.g., for predicting progression to dementia), differences in data acquisition‐methods / scanner‐protocols can enable the model to “cheat”, i.e. utilizes site‐specific artifacts rather than disease‐specific features. In this study, we develop a method to harmonize the performance of DNN classifiers across scanners/sites, via so‐called fairness constraints, thereby encouraging consistent behavior while controlling for site‐specific nuisance variables. Method We conducted two studies: (a) to demonstrate feasibility of pooling across sites (Site‐Pooling) and (b) to pool data across scanners (Scanner‐Pooling). For Site‐Pooling, our analysis included summaries from Freesurfer processed T1‐weighted images of the Wisconsin Alzheimer's Disease Research Center (ADRC) and German Center for Neurodegenerative Diseases (DZNE). The Freesurfer summaries were used to train a two layer neural network classifier and five‐fold cross‐validation performance was assessed. For Scanner‐Pooling experiments, Freesurfer processed MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to train a deep 3D convolutional network. Performance average on a held‐out test dataset was evaluated. In both cases, a constraint to equalize the performance of the trained classifier across the domains (sites/scanners) was incorporated during training. Result Table 1 shows the results of AD/MCI classification for site‐pooling analysis. Our proposed method is compared against a naive pooling approach which does not incorporate the “harmonization constraint”. As shown, the proposed method improves the “difference of errors” measure by 8% / 7% and with only a small drop in overall error rates. Figure 1 illustrates the results from our scanner‐pooling analysis. The performance across the three scanners, GE, Siemens and Philips, is evaluated pair‐wise. A consistent improvement in harmonization is observed and only ∼2% drop in overall error rate is seen. Conclusion We provide a harmonization constraint based algorithm to mitigate site specific differences when performing analysis of pooled brain imaging datasets in AD studies. In contrast to a method which modifies the data, we achieve harmonization by constraining the classifier to perform similarly across sites/groups/scanners, improving reproducibility.