Reviewerly/exHarmony huggingface dataset:
Child Activity Recognition using Deep Learning

The human action recognition is the subject to predicting what an individual is performing based on a trace of their development exploiting a several strategies. Perceiving human activities is an ordinary region of eagerness in view of its various potential applications; though, it is still in start. It is a trending analysis area possessed by the range from dependable automation, medicinal services to developing the smart supervision system. In this work, we are trying to recognize the activity of the child from video dataset using deep learning techniques. The proposed system will help parent to take care of their baby during the job or from anywhere else to know what the baby is doing. This can also be useful to prevent the in-house accident falls of the child and for health monitoring. The activities can be performed by child include sleeping, walking, running, crawling, playing, eating, cruising, clapping, laughing, crying and many more. We are focusing on recognizing crawling, running, sleeping, and walking activities of the child in this study. The offered system gives the best result compared with the existing methods, which utilize sensor-based information. Experimental results proved that the offered deep learning model had accomplished 94.73% accuracy for recognizing the child activity.
Cyber Insurance and Technology Innovation

The General Data Protection Regulation (GDPR) imposes a wide range of compliance obligations on affected companies that are sure to create both initial and ongoing compliance challenges. The threat of potentially devasting fines and penalties raises the stakes for companies to manage both their compliance obligations and the risk that something will go wrong. As with other significant corporate risks, companies should consider transfer of GDPR risks through insurance. Coverage for the gamut of GDPR liabilities, including fines and penalties, will depend on the language of the policy at issue and the law applicable to the interpretation of the policy. Given today’s competitive cyber insurance market, insureds are well positioned to seek out and negotiate for broad coverage for GDPR exposures.
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level

As the relative power, performance, and area (PPA) impact of embedded memories continues to grow, proper parameterization of each of the thousands of memories on a chip is essential. When the parameters of all memories of a product are optimized together as part of a single system, better trade-offs may be achieved than if the same memories were optimized in isolation. However, challenges such as a sparse solution space, conflicting objectives, and computationally expensive PPA estimation impede the application of common optimization heuristics. We show how the memory system optimization problem can be solved through computational intelligence. We apply a Pareto-based Differential Evolution to ensure unbiased optimization of multiple PPA objectives. To ensure efficient exploration of a sparse solution space, we repair individuals to yield feasible parameterizations. PPA is estimated efficiently in large batches by pre-trained regression neural networks. Our framework enables the system optimization of thousands of memories while keeping a small resource footprint. Evaluating our method on a tractable system, we find that our method finds diverse solutions which exhibit less than 0.5% distance from known global optima.
A Short Note on Educational Futures in the Post-Covid-19 World

Instructive fates have maybe never been more bantered than during the Covid-19 pandemic. Instructors, strategy creators, educators, guardians, understudies and kids have ended up in 2020 in what we would call an unfathomable situation of being, instructing, learning and dynamic. The entire world, paying little mind to geological area, has been confronting remarkable the descriptive word that has been utilized such a huge amount in 2020 strife, where constrained section into turning into a virtual study hall – an online space and virtual presence began inside seven days, absent a lot of exploration, comprehension, or specific reasoning cycle. A particularly quick change for a large number of us made a fast pressure, weakness, just as expectations and thoughts – thinking back, examining current conditions or mulling over what's to come. In reality, various examination papers have cooperatively analysed conceivable outcomes, establishing a fascinating climate inside which we can learn something about our own ontologies and potential for future learning. Strategy Futures in Education has consistently been vested in the thoughts of things to come, training, strategy, and what to look like towards the future with conceivable outcomes and possibilities, viewing us as dynamic specialists all the while nonetheless, it isn't surprising for different diaries to offer such an expression. Specifically, the investigation of authentic and ebb and flow conditions and how they address, through hypothetical and observational proof, the future course of training, has been at the cutting edge of many exploration projects and expositions.
Face Mask Recognition to Identify People Wearing Masks to Support Covid-19 Prevention Policies

Nowadays, Covid-19 pandemic has penetrated all the human beings daily life. One of the most common and strongest way to prevent spread is wearing a face mask in public places. People gained a new term to their vocabulary; “new normal”. The new normal contains wearing a face mask as well. Therefore, a face mask recognition system is a vital need for helping daily life processes. This paper acquaints a face mask recognition to identify masked and unmasked faces to support Covid-19 policies. The face mask recognition in this paper developed by deep learning algorithm using the CNN architecture VGG-16. Our results suggest that deep learning-based method achieved high accuracy (99%) in both the validation and testing datasets.
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback

Fluid human-agent communication is essential for the future of human-in-the-loop reinforcement learning. An agent must respond appropriately to feedback from its human trainer even before they have significant experience working together. Therefore, it is important that learning agents respond well to various feedback schemes human trainers are likely to provide. This work analyzes the COnvergent Actor-Critic by Humans (COACH) algorithm under three different types of feedback-policy feedback, reward feedback, and advantage feedback. For these three feedback types, we find that COACH can behave sub-optimally. We propose a variant of COACH, episodic COACH (E-COACH), which we prove converges for all three types. We compare our COACH variant with two other reinforcement-learning algorithms: Q-learning and TAMER.
Peran Media Utama Citaku Terhadap Kertampilan Berfikir Peserta Didik Kelas IV SD

Education using the 2013 curriculum must be able to improve students' skills and values that apply in society. Critical thinking skills, among others, that must be possessed by students to support students' thinking and self-development. For this reason the need for innovation and good creativity from the teacher so that students get the goals that have been designed in learning. In learning, teachers usually still use conventional learning systems and are not added to the media. These impacts cause students to be unenthusiastic, enthusiastic about learning and even result in actual skills emerge and will increase students' potential to sink. Therefore teachers must design and create media to improve students' critical thinking skills. These media UTAMA CITAKU media which have a role in improving students' critical thinking skills.
Cross-document Event Identity via Dense Annotation

In this paper, we study the identity of textual events from different documents. While the complex nature of event identity is previously studied (Hovy et al., 2013), the case of events across documents is unclear. Prior work on cross-document event coreference has two main drawbacks. First, they restrict the annotations to a limited set of event types. Second, they insufficiently tackle the concept of event identity. Such annotation setup reduces the pool of event mentions and prevents one from considering the possibility of quasi-identity relations. We propose a dense annotation approach for cross-document event coreference, comprising a rich source of event mentions and a dense annotation effort between related document pairs. To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface. In addition to the links, we further collect overlapping event contexts, including time, location, and participants, to shed some light on the relation between identity decisions and context. We present an open-access dataset for cross-document event coreference, CDEC-WN, collected from English Wikinews and open-source our annotation toolkit to encourage further research on cross-document tasks.
Correlation of Static and Dynamic Components in S. T. Coleridge’s Poetical Texts

The paper aims to reveal the correlation of static and dynamic components in S. T. Coleridge’s poetical works published a long time after they were written. The study is based on stylometric analysis; the research methodology includes statistical methods, such as Busemann coefficient and chi-squared test. Scientific novelty of the work lies in the fact that relying on the analysis of S. T. Coleridge’s creative heritage (two well-known poems served as a research material), the researcher for the first time proposes a model of interaction of static and dynamic components in his artistic narrative. The following conclusions are justified: in the opening stanza the ratio of static and dynamic components is almost equal, in the final stanza dynamic component tends to prevail, the level of dynamism varies substantially within the line.
Development of creativity of the "digital generation" students through the organization of creative activities in the creatively oriented educational process of the university

Today, the world around us, including the professional environment, is changing rapidly. Employers are having new requirements for employees. The employee must not only be a good performer, but also have spatial and functional mobility, i.e. be a creative person who is able to adapt and improve in connection with changing circumstances. Therefore, at present, it is necessary to pay great attention to the development of students ' creativity in educational classes at universities. The authors understand creativity as the ability of a person to non-standard creative thinking and behavior, with further awareness and development of their experience. Well-organized training sessions that develop creative abilities, such as business and role-playing games, case studies, project activities, combined with the use of electronic educational resources, contribute to the education of students ' confidence, self-control, teamwork, teach business communication and respect for others, teach them an unconventional approach to traditional habitual phenomena and help them in the initial development of professional activity. The authors note the great role of the teacher in the development of students ' creative abilities, especially if the teacher himself is a creative person. A creative teacher has professional and personal attractiveness in the eyes of students, due to his openness, benevolence and accessibility in communication, especially with the "alienated" communication that is common in the modern educational world. The authors prove on the basis of data from constant surveys and conversations with students and graduates in training classes, during personal meetings, in social messengers (VKontakte, WhatsApp, LinkedIn) and other means of communication that their active participation in business and role-playing games, project competitions, student debates, solving cases helped them in the initial development of professional activity.
Generalizing Message Passing Neural Networks to Heterophily Using Position Information

Message Passing Neural Networks (MPNNs) is a promising architecture for machine learning on graphs, which iteratively propagates the information among nodes. The existing MPNNs methods are more suitable for homophily graphs in which the geometrically close nodes have similar features and class labels. However, in real-world applications, there exist graphs with heterophily and the performance of MPNNs may be limited when dealing with the heterophily graphs. We analyze the limitations of MPNNs when facing heterophily graphs and owe it to the indistinguishability of nodes during aggregating and combining. To this end, we propose a method under the MPNNs architecture called Position Enhanced Message Passing model (PEMP) that endows the node with position information to make the node distinguishable. Extensive experiments on nine real-world datasets show that our method achieves state-of-the-art performances in most heterophily graphs while preserving the performances of MPNNs on homophily graphs.
Adaptive Ranking Relevant Source Files for Bug Reports Using Genetic Algorithm

Precisely locating buggy files for a given bug report is a cumbersome and time-consuming task, particularly in a large-scale project with thousands of source files and bug reports. An efficient bug localization module is desirable to improve the productivity of the software maintenance phase. Many previous approaches rank source files according to their relevance to a given bug report based on simple lexical matching scores. However, the lexical mismatches between natural language expressions used to describe bug reports and technical terms of software source code might reduce the bug localization system’s accuracy. Incorporating domain knowledge through some features such as the semantic similarity, the fixing frequency of a source file, the code change history and similar bug reports is crucial to efficiently locating buggy files. In this paper, we propose a bug localization model, BugLocGA that leverages both lexical and semantic information as well as explores the relation between a bug report and a source file through some domain features. Given a bug report, we calculate the ranking score with every source files through a weighted sum of all features, where the weights are trained through a genetic algorithm with the aim of maximizing the performance of the bug localization model using two evaluation metrics: mean reciprocal rank (MRR) and mean average precision (MAP). The empirical results conducted on some widely-used open source software projects have showed that our model outperformed some state of the art approaches by effectively recommending relevant files where the bug should be fixed.
YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker

Pre-trained model such as BERT has been proved to be an effective tool for dealing with Information Retrieval (IR) problems. Due to its inspiring performance, it has been widely used to tackle with real-world IR problems such as document ranking. Recently, researchers have found that selecting "hard" rather than "random" negative samples would be beneficial for fine-tuning pre-trained models on ranking tasks. However, it remains elusive how to leverage hard negative samples in a principled way. To address the aforementioned issues, we propose a fine-tuning strategy for document ranking, namely Self-Involvement Ranker (SIR), to dynamically select hard negative samples to construct high-quality semantic space for training a high-quality ranking model. Specifically, SIR consists of sequential compressors implemented with pre-trained models. Front compressor selects hard negative samples for rear compressor. Moreover, SIR leverages supervisory signal to adaptively adjust semantic space of negative samples. Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance. SIR is a lightweight and general framework for pre-trained models, which simplifies the ranking process in industry practice. We test our proposed solution on MS MARCO with document ranking setting, and the results show that SIR can significantly improve the ranking performance of various pre-trained models. Moreover, our method became the new SOTA model anonymously on MS MARCO Document ranking leaderboard in May 2021.
Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise.

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model. Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people's trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model. Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people's trust perceptions.
Digital score of Scherzo for solo bassoon

Musical score of 'Scherzo for solo bassoon'. Title taken from item. Quantity: 1 Electronic document(s).
LibGuides: Lee Connected Collaboration: Figure 19: Using Picture Books as Anchor Texts

Learn how to choose the latest and greatest picture books to integrate curriculum skills. When working with a tight schedule using picture books is an efficient and engaging way to capture student interest and make the most of your time.
The Use Of Mobile Learning In Teaching And Learning Session During The Covid-19 Pandemic In Malaysia

Mobile learning (m-learning) is a teaching and learning method during home learning during the covid-19 pandemic that hit the world. This situation demands teachers and students to conduct online learning sessions. Accordingly, teachers also need to be creative in finding alternative methods and strive to develop educational innovations to ensure that the teaching and learning process at home can be carried out effectively. The purpose of writing this concept paper is to discuss the twelve elements of m-learning to provide information to teachers to consider and apply it in teaching and learning sessions at home. The twelve elements are the definition and theory of m-learning, the development of m-learning in Malaysia, the difference between e-learning and m-learning, the characteristics of m-learning, basic features in m-learning, m-learning activities, the advantages of m-learning in teaching and learning, selection criteria for the use of the mobile application in m-learning, factors affecting m-leaning in education, the quality of mobile application features, the importance of the use m-learning in education and the issues and challenges of m-learning based on past studies. In addition, further research is also discussed in this concept paper. Based on past studies, m-learning is an appropriate alternative method used during this covid-19 pandemic for teachers and students to carry out the teaching and learning process at home.
SVA Library Research Guides: Online Resources and Services: Article Databases

The following guide outlines the collections, services, and support available remotely to students, faculty, and staff.
The Effectiveness of Using Youtube Applications as Learning Media to Increase Reading and Writing Interest of Elementary School Students

The Education Era 4.0 has demanded students' to improve their literacy skills, especially elementary school Students'. The purpose of this study was to determine the effectiveness of the use of the Youtube application as a learning medium to improve children's literacy skills. Unfortunately, until now there has been no research that discusses the Effectiveness of Using Youtube Applications as Learning Media to Increase the Reading and Writing Interests of Elementary School Students'. The research method used is a qualitative descriptive approach which is carried out through field research and interviews. In addition, we also conducted a literature study related to previous relevant research studies. Based on the results of the study, there was an increase in student learning outcomes by using Youtube as a learning medium. This shows that the presentation of creative learning materials can stimulate Students' interest and motivation which will have an impact on increasing Students' literacy skills. It can be concluded that the Youtube application has proven to be effective in presenting interesting learning videos so that it can increase the interest and literacy skills of elementary school Students'. This literacy and literacy ability will later be very useful for elementary school students' as a provision to support Students' in the 4.0 Education Era for a better future.
Unified and Multilingual Author Profiling for Detecting Haters

This paper presents a unified user profiling framework to identify hate speech spreaders by processing their tweets regardless of the language. The framework encodes the tweets with sentence transformers and applies an attention mechanism to select important tweets for learning user profiles. Furthermore, the attention layer helps to explain why a user is a hate speech spreader by producing attention weights at both token and post level. Our proposed model outperformed the state-of-the-art multilingual transformer models.
Direct Advantage Estimation

The predominant approach in reinforcement learning is to assign credit to actions based on the expected return. However, we show that the return may depend on the policy in a way which could lead to excessive variance in value estimation and slow down learning. Instead, we show that the advantage function can be interpreted as causal effects and shares similar properties with causal representations. Based on this insight, we propose Direct Advantage Estimation (DAE), a novel method that can model the advantage function and estimate it directly from on-policy data while simultaneously minimizing the variance of the return without requiring the (action-)value function. We also relate our method to Temporal Difference methods by showing how value functions can be seamlessly integrated into DAE. The proposed method is easy to implement and can be readily adapted by modern actor-critic methods. We evaluate DAE empirically on three discrete control domains and show that it can outperform generalized advantage estimation (GAE), a strong baseline for advantage estimation, on a majority of the environments when applied to policy optimization.