Reviewerly/exHarmony huggingface dataset:
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation

Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale that interprets the behavior of KD has yet remained largely understudied. In this paper, we introduce a novel task-oriented attention model, termed as KDExplainer, to shed light on the working mechanism underlying the vanilla KD. At the heart of KDExplainer is a Hierarchical Mixture of Experts (HME), in which a multi-class classification is reformulated as a multi-task binary one. Through distilling knowledge from a free-form pre-trained DNN to KDExplainer, we observe that KD implicitly modulates the knowledge conflicts between different subtasks, and in reality has much more to offer than label smoothing. Based on such findings, we further introduce a portable tool, dubbed as virtual attention module (VAM), that can be seamlessly integrated with various DNNs to enhance their performance under KD. Experimental results demonstrate that with a negligible additional cost, student models equipped with VAM consistently outperform their non-VAM counterparts across different benchmarks. Furthermore, when combined with other KD methods, VAM remains competent in promoting results, even though it is only motivated by vanilla KD. The code is available at https:// github.com/zju-vipa/KDExplainer.
Targeting uncertainty in smart CPS by confidence-based logic

Since Smart Cyber–Physical Systems (sCPS) are complex and decentralized systems of dynamically cooperating components, architecture-based adaptation is of high importance in their design. In this context, a key challenge is that they typically operate in uncertain environments. Thus, an inherent requirement in sCPS design is the need to deal with the uncertainty of data coming from the environment. Existing approaches often rely on the fact that an adequate model of the environment and/or base probabilities or a prior distribution of data are available. In this paper, we present a specific logic (CB logic), which, based on statistical testing, allows specifying transition guards in architecture-based adaptation without requiring knowledge of the base probabilities or prior knowledge about the data distribution. Applicable in state machines’ transition guards in general, CB logic provides a number of operators over time series that simplify the filtering, resampling, and statistics-backed comparisons of time series, making the application of multiple statistical procedures easy for non-experts. The viability of our approach is illustrated on a running example and a case study demonstrating how CB logic simplifies adaptation triggers. Moreover, a library with a Java and C ++ implementation of CB logic’s key operators is available on GitHub.
Medical Recommendation System using Co-Occurrence Graphs

Medical diagnosis [1] is the process of determining which disease explain a person’s symptoms and signs. Correct diagnosis depend on the knowledge and experience of doctors, huge amount of knowledge is distributed in textbooks and documents. it takes a huge amount of time to learn a lot of information for diagnosis, moreover, diagnosis is often challenging, because many signs and symptoms are nonspecific or some symptoms can be many of diseases. Therefor the knowledge processing must be automated. A co-occurrence graph is incorporated from sets of documents that represent knowledge, which is similar to human brain, when people read a book, the book content will become to knowledge. For co-occurrence graph, it created by information extraction from documents, the information that extract from document is read sentence by sentence and words of each sentence will create relation to each other, then the co-occurrence graph will appear and represent to brain knowledge. In this paper will presents an experiment to bring the co-occurrence graph theory to apply with medical information and create the system that contribute the diagnosis from symptoms.
LEARNING CLEAN AND HEALTHY LIVING BEHAVIOR WITH POP-UP BOOK AND POSTER MEDIA ON CHILDREN AGES 6-12 YEARS AT ELEMENTARY SCHOOL, MALANG

Abstract Clean and Healthy Living Behavior (CHLB) is an effort to improve health quality through clean and healthy daily life behaviors. The problem of health behavior in children is related to personal hygiene, the environment, and the emergence of various diseases that often attack children. One of the preventive measures is to teach CHLB to children as early as possible by using interesting and easy-to-understand media for children. This study aims to determine the effect of a combination of a pop-up book and poster media on the ability of CHLB children aged 6-12 years. This study used a cross-sectional approach with 90 respondents divided into 3 groups. Collecting data using a questionnaire and analyzed by univariate, bivariate, and multivariate. The results showed that 66.67% of respondents had knowledge in the sufficient category and 10% in the less category before being given a combination of the two methods. Each respondent experienced a significant change in their knowledge. Statistical testing with MANOVA analysis on the comparison between treatment groups on knowledge, attitudes, and behavior obtained a calculated F value of 172,000 with a significance of &lt;0.001 indicating that there were significant differences between treatment groups on knowledge, attitudes, and behavior. The use of pop-up books and posters can be used in the learning process because they have a major impact on increasing children's knowledge, attitudes, and behavior in an effort to maintain health through clean and healthy living behaviors.
Impact of Data Balancing During Training for Best Predictions

To protect the middle class from over-indebtedness, banking institutions need to implement a flexible analytic-based evaluation method to improve the banking process by detecting customers who are likely to have difficulty in managing their debt. In this paper, we test and evaluate a large variety of data balancing methods on selected machine learning algorithms (MLAs) to overcome the effects of imbalanced data and show their impact on the training step to predict credit risk. Our objective is to deal with data unbalance to achieve the best predictions. We investigated the performance of these methods by different learners when classification models are trained using MLAs.
Review of <i>Seismic Hazard and Risk Analysis</i>

Review Article| August 11, 2021 Review of Seismic Hazard and Risk Analysis Seismic Hazard and Risk Analysis, Jack W. Baker, Brendon A. Bradley, and Peter J. Stafford, Cambridge University Press, 2021, ISBN: 978‐1‐108‐42505‐6, Hardback, $79.99. Julian J. Bommer Julian J. Bommer * 1Department of Civil & Environmental Engineering, Imperial College London, London, United Kingdom *Corresponding author: j.bommer@imperial.ac.uk https://orcid.org/0000-0002-9709-5223 Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2021) 92 (5): 3248–3250. https://doi.org/10.1785/0220210146 Article history first online: 11 Aug 2021 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Julian J. Bommer; Review of Seismic Hazard and Risk Analysis. Seismological Research Letters 2021;; 92 (5): 3248–3250. doi: https://doi.org/10.1785/0220210146 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search Seismology is a wonderfully broad field that encompasses many disciplines, contributing to the understanding of earthquakes and unveiling the secrets of the Earth’s interior (and even the interiors of other planets). There are several ways in which seismology contributes to a better world, notable among which is the discrimination between the signals generated by earthquakes and explosions that makes possible the maintenance of treaties banning tests of atomic weapons. Early warning systems, another example of applied seismology, have the potential to save lives and reduce the impact of major earthquakes. But there can be little doubt that the most effective... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Scientific Exploration for Density Estimation and Crowd Counting of Crowded Scene

Abstract Crowd density estimation model is a typical concept which compute the counting of the people in the crowded image. There are many of research papers are written in this area to solve the different kind of real world problems. This paper shows the state of art and different types of framework proposed on density estimation and crowd counting techniques. The population of our country increases year by year, so the purpose of this paper is to explore different techniques available for crowd density estimation. There are lot of applications existing based on this technique like retail system, surveillance system etc., where the foot traffic is very vital in retail system to organize merchandise in aisles, optimize store layout, understand peak times and potentially even protect against theft. In surveillance system, this is mainly used for security purpose. In this, the researcher captures the crowd using the camera and the technique of counting the crowd which is divided into two parts unsupervised and supervised learning. These techniques are further divided into parts which is shown in the paper. The survey presents the different types of datasets of the crowded image which is useful for simulating the people counting in the crowded scenes. In detection based approach, this technique counts the people with the help of face detection technique. In this researchers face the problems of count the people in crowded scenes and also face the problem in surveillance applications. The cluster based approach data works well on the scattered crowd. This method needs image frames; it does not work on still images. In regression approach the model trained is subject to the point of view map. In the model were to be used in a different scene of a changed perspective map, it will have many inaccuracies in its result. The current research work intends to provide a general idea on crowd density estimation and counting approaches employed in visual surveillance in the perception of the study of computer vision. The analysis of review categorizes and frameworks quite a lot approximation related to crowd density and calculating approaches which are beneficial in providing the analysis related to the crowd places of interest.
Enterprise architecture design using TOGAF at foundation of triputra persada horizon education

The use of information technology in the field of education is currently a top priority for managing academic and supporting activities. Tri Putra Persada Horizon Education Foundation which manages two high schools, namely the College of Health Sciences and the College of Information and Computer Management should face a challenge to align business strategy with information technology, and how to integrate all the parts involved in the business and represent it in an information system. To find out the business strategy and governance of information technology used at the Tri Putra Persada Horizon Education Foundation, an Enterprises Architecture Framework is needed, one of which is TOGAF ADM. The Enterprises Architecture design contained in TOGAF ADM includes a vision architecture that defines the vision of the company or agency, a mapped business architecture in the form of value chain analysis, an information system architecture in which there is a data architecture and application architecture and the last is technology architecture. This research produced an enterprise architecture design blueprint consisting of artifacts, in the form of catalogues, matrices, and diagrams based on the phases of TOGAF ADM. The result of the Enterprise Architecture design was an integrated information system recommendation and the technology architecture. The design is expected to be a reference in improving the quality of business and is expected to make it easier to achieve the business goals of the Tri Putra Persada Horizon Education Foundation.
Attention-based Pyramid Dilated Lattice Network for Blind Image Denoising

Though convolutional neural networks (CNNs) with residual and dense aggregations have obtained much attention in image denoising, they are incapable of exploiting different levels of contextual information at every convolutional unit in order to infer different levels of noise components with a single model. In this paper, to overcome this shortcoming we present a novel attention-based pyramid dilated lattice (APDL) architecture and investigate its capability for blind image denoising. The proposed framework can effectively harness the advantages of residual and dense aggregations to achieve a great trade-off between performance, parameter efficiency, and test time. It also employs a novel pyramid dilated convolution strategy to effectively capture contextual information corresponding to different noise levels through the training of a single model. Our extensive experimental investigation verifies the effectiveness and efficiency of the APDL architecture for image denoising as well as JPEG artifacts suppression tasks.
Deep Learning under Model Uncertainty

Deep learning has proven to lead to very powerful predictive models, often outperforming classical regression models such as generalized linear models. Deep learning models perform representation learning, which means that they do covariate engineering themselves so that explanatory variables are optimally transformed for the predictive problem at hand. A crucial object in deep learning is the loss function (objective function) for model fitting which implicitly reflects the distributional properties of the observed samples. The purpose of this article is to discuss the choice of this loss function, in particular, we give a specific proposal of a loss function choice under model uncertainty. This proposal turns out to robustify representation learning and prediction.
GCN with External Knowledge for Clinical Event Detection

In recent years, with the development of deep learning and the increasing demand for medical information acquisition in medical information technology applications such as clinical decision support, Clinical Event Detection has been widely studied as its subtask. However, directly applying advances in deep learning to Clinical Event Detection tasks often produces undesirable results. This paper proposes a multi-granularity information fusion encoder-decoder framework that introduces external knowledge. First, the word embedding generated by the pre-trained biomedical language representation model (BioBERT) and the character embedding generated by the Convolutional Neural Network are spliced. And then perform Part-of-Speech attention coding for character-level embedding, perform semantic Graph Convolutional Network coding for the spliced character-word embedding. Finally, the information of these three parts is fused as Conditional Random Field input to generate the sequence label of the word. The experimental results on the 2012 i2b2 data set show that the model in this paper is superior to other existing models. In addition, the model in this paper alleviates the problem that “occurrence” event type seem more difficult to detect than other event types.
Hate speech and deliberation

Is enclave deliberation leading to more hate speech? Can deliberation norms tackle hate speech or do they lead to “civilized” forms of group libel? None of these questions can be answered without first identifying how hate speech manifests in its various forms. Hate speech disparages an individual or group on grounds such as race, gender, nationality, ethnicity, language, religion, or sexual orientation. Studies on online harassment have often analysed hate speech as a matter of incivility. However, group derogatory comments are not the commonest form of online incivility, and nor is hate speech restricted to comments. Based on speech act theory and on the theory of digital objects, this chapter conducts an integrative literature review in order to provide a taxonomy of hate speech in digital conversations, allowing an identification of how hate speech manifests in digital conversations, how these forms can be assessed empirically, and what differentiated effects they have on online deliberation.
Digital Tracing and Malaysia's Personal Data Protection Act 2010 amid the Covid-19 Pandemic

Digital tracing is a proven effective means for the Malaysian government to trace and control the spread of COVID-19. However, the process of tracing and tracking in order to manage the spread of the pandemic have in many ways compromised personal information to third party applications. Malaysia is not the only country that uses digital tracing to manage the spread of the pandemic. Various countries have chosen different methods for digital contact tracing to manage the spread of COVID-19 and some are less respectful of privacy than others. This paper analyses Malaysia’s Personal Data Protection Act 2010 (PDPA) and its effectiveness in protecting personal data during the pandemic as Malaysians continue to utilise the contact tracing mobile applications such as MySejahtera and SELangkah. The researchers applied doctrinal research method and analysed the current Malaysian legislation on data protection. It should be noted that the PDPA does not apply in the case of government collection and would not require federal and state agencies to be transparent in their data management.
Headed-Span-Based Projective Dependency Parsing

We propose a new method for projective dependency parsing based on headed spans.In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i.e., a span) in the surface order.We call such a span marked by a root word headed span.A projective dependency tree can be represented as a collection of headed spans.We decompose the score of a dependency tree into the scores of the headed spans and design a novel O(n 3 ) dynamic programming algorithm to enable global training and exact inference.Our model achieves state-of-the-art or competitive results on PTB, CTB, and UD 1 .
IMPROVING STUDENTS’ SPEAKING SKILL THROUGH YOUTUBE VIDEO: SYSTEMATIC REVIEW

This study aims to describe youtube video as an effective medium to improve English speaking skills and to gather some information related to the use of YouTube videos in teaching and learning English. The approach used in this research was systematic analysis. This research used 10 sources that consisted 4 theses and 6 articles. Technique of data analysis used in this study was DescriptiveResearch.The data in this study are sentences. All sentences have been selected by researchers from several relevant journals and theses. Data collection is carried out by researchers by searching several journals or theses that have similarities in reading texts or research media. Data analysis using pre-test and post-test that has been investigated by researchers. The result of this study is using YouTube videos as an alternate since It can be seen from differences in the pre-test post-test results of students taken from various sources by using Youtube video. In reality, Youtube video media can not only be adapted to children but can also be extended to all level of educationas well as allowing students to quickly practice their own speaking skills.
Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints

Subset selection with cost constraints is a fundamental problem with various applications such as influence maximization and sensor placement. The goal is to select a subset from a ground set to maximize a monotone objective function such that a monotone cost function is upper bounded by a budget. Previous algorithms with bounded approximation guarantees include the generalized greedy algorithm, POMC and EAMC, all of which can achieve the best known approximation guarantee. In real-world scenarios, the resources often vary, i.e., the budget often changes over time, requiring the algorithms to adapt the solutions quickly. However, when the budget changes dynamically, all these three algorithms either achieve arbitrarily bad approximation guarantees, or require a long running time. In this paper, we propose a new algorithm FPOMC by combining the merits of the generalized greedy algorithm and POMC. That is, FPOMC introduces a greedy selection strategy into POMC. We prove that FPOMC can maintain the best known approximation guarantee efficiently.
A Novel Method for Handling Partial Occlusion on Person Re-identification using Partial Siamese Network

Person-reidentification (Re-ID) is one of the tasks in CCTV-based surveillance system for verifying whether two detected objects are the same person or not.Re-ID visually matching one person or group in various situations obtained from different cameras or on the same camera but at different times.This method replaces the task of surveillance through surveillance cameras that was previously carried out conventionally by humans because it is prone to errors.The challenge of Re-ID is the pose of varied objects, occlusions, and the appearance of people who tend to be similar.Occlusion issues receive special attention since the performance of Re-ID can decrease due to partial occlusion.This can occur because the reidentification process relies on features of the person such as the color and pattern of clothing.The occlusion resulted in the feature not being caught by the camera resulting in a reidentification error.This paper proposed to overcome this problem by dividing the image into several parts (partial) and then processed in different neural network (NN) but with the same architecture.The research conducted is applying the CNN algorithm with the Siamese network architecture and applying the contrastive loss function to calculate the similarity distance between a pair of images.The test results show that the partial process obtained an accuracy of 86%, 77%, 68%, and 56% for occlusion data of 20%, 40%, 60%, and 80%.This accuracy is three to five percent higher than images without partial.