Reviewerly/exHarmony huggingface dataset:
ACTION VERBS IN ACTION VIA AUGMENTED REALITY (AVIAR)

In Outcome-based Education (OBE), action verbs (AVs) used must be clear and measurable to ensure successful learning experiences. Students who fail to understand the implications of the stated AVs are likely to fail inmeeting their lecturers' expectations. Motivated by the Dual-coding theory, AVIAR is developed to help students and teachers visualise the underlying expectations of AVs through Augmented Reality (AR). Past studies show that AR can help students master vocabulary faster when they are able to visualise the words learned. When the selected AVs scanned using AVIAR, virtual information will appear either in English, Arabic or Malay. Hopefully, AVIAR will reduce mismatches between student comprehension and teacher expectation.
statistical mechanical analysis of catastrophic forgetting in continual learning with teacher and student networks

When a computational system continuously learns from an ever-changing environment, it rapidly forgets its past experiences. This phenomenon is called catastrophic forgetting. While a line of studie...
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks

GQA~\citep{hudson2019gqa} is a dataset for real-world visual reasoning and compositional question answering. We found that many answers predicted by the best vision-language models on the GQA dataset do not match the ground-truth answer but still are semantically meaningful and correct in the given context. In fact, this is the case with most existing visual question answering (VQA) datasets where they assume only one ground-truth answer for each question. We propose Alternative Answer Sets (AAS) of ground-truth answers to address this limitation, which is created automatically using off-the-shelf NLP tools. We introduce a semantic metric based on AAS and modify top VQA solvers to support multiple plausible answers for a question. We implement this approach on the GQA dataset and show the performance improvements. Code and data are available in this link \url{https://github.com/luomancs/alternative_answer_set.git}.
THE IMPORTANCE OF TEACHERS’ HOLISTIC APPROACH TO WORKING WITH CHILDREN WITH BEHAVIORAL PROBLEMS

Abstract: Today's modern school, globalization and the development of technology, bring great challenges, not only for teachers, but for the whole society. Problems in children's behavior are a serious social and psychological problem. People who follow children’s development and want them to change are those who are constantly improving, applying the key concepts of holistic education, until they see the change.The purpose of this paper is to highlight the importance of a holistic approach, in which the teacher is not only an educator but also a companion and a facilitator, one that helps and facilitates the learning process (Farbes, 1996). It is important that we monitor the child's development from all aspects. Encouraging children for positive development and creativity, seeing them as decision makers in their environment, are  just some of the elements of applying holism.Key words: undesirable behaviors; holistic approach; prevention of behavior;
The Protection and Access to Relevant Data-- General Issues

The development of AI has evolved from technology driven to data driven. The advancement of the Internet and the Internet of things (IoT), supercomputers and cloud server technologies enable us to collect and process huge amounts of data, as well as to perform complicated calculations, all at the speed of light.The enormous amount of data further enhances machine learning and data-based innovations. Machine-learning technology together with big data has been applied widely in all areas of our daily lives. Data have become the raw materials of production, a new source of economic and social value. The issue of the protection of and access to relevant data will have tremendous impact on the future of AI. This chapter discusses the protection of relevant data and issues that might block access to such data and points out solutions. It is divided into five sections, the introduction, typology of data, protection of relevant data, access to relevant data, and findings and conclusions.
Practical quantum electronic voting

Electronic voting is a very useful but challenging internet-based protocol that despite many theoretical approaches and various implementations with different degrees of success, remains a contentious topic due to issues in reliability and security. Here we present a quantum protocol that exploits an untrusted source of multipartite entanglement to carry out an election without relying on election authorities, simultaneous broadcasting or computational assumptions, and whose result is publicly verifiable. The level of security depends directly on the fidelity of the shared multipartite entangled quantum state, and the protocol can be readily implemented for a few voters with state-of-the-art photonic technology.
Facies changes and a major negative δ13C shift suggest the base of Mila Formation as the likely base of the Miaolingian Series, Alborz Mountains, northern Iran

By Hadi Amin-Rasouli, Yaghoob Lasemi. Sequence stratigraphy and d13C composition shows that the basal Mila Formation is the base of the Miaolingian Series in northern Iran, which has implications for the terminal early Cambrian extinction.
The Relationship Between Foreign Direct Investment Oriented Economic Growth in Uganda: An Empirical Study Based on VAR Model

The purpose of this paper is to investigate the relationship between Foreign Direct Investment (FDI) and Economic growth as measured by Gross Domestic Product (GDP) over Uganda, from 1980-2018. Vector Autoregressive Model (VAR) and Granger Causality test were used. The results show thatlag 1 is the optimal lag hence bivariate VAR (1) model was used. GDP and FDI exhibits long-term equilibrium since the two-time series are cointegrated in long run. The causality test indicates that there exists a unilateral relationship between FDI and GDP, and FDI causes GDP growth and not vice versa. Understanding these causality links can help in future forecasting of Uganda's economic growth.
Tracker and Monitoring System for Self-Quarantined Individuals

Quarantine has been establish as a method to reduce the spread of infectious disease. Ensuring maximum compliance of quarantined individual has been the key to a successful quarantine. Tracking the movement and monitoring of vitals such as temperature can help health officials in their war against infectious diseases such as Coronavirus Disease 2019. The tracker and monitoring of quarantined individual system is developed with this goal in mind. The combination of device and system that carefully tracks and monitors quarantined individuals can be used to alleviate the task of health officials and reduce the risk of an outbreak from developing further.
Architecture of Digital Twin for Network Forensic Analysis Using Nmap and Wireshark

The notion of Digital Twin (DT) has arisen to allow the welfare of upcoming archetypes such as digitalization in forensic analysis. In recent times, information technology has reduced somatic space among peoples for communique and also delivers the plinth for cybercrimes. Muggers break a set of rules and policies, and steal information, therefore it is essential to search out the enemies. System forensics is principally about nursing, seizing, examining the network traffic, and exploring the security policy defilements. The foremost objective of a segment of forensic concerning the network is to determine the cradle of an intruder, and offer a sign to classify the bouts. This study suggests a framework of DT for forensic subdivision class as forensic analysis via a network to detect bouts, and it is providing well-organized protection. In this chapter, the authors are utilizing a self-embedded watermark that delivers reliability, with the existence of the waterline, entails a haven code that offers validity to confirm the effectual protection by utilization of many available informant port scanning predefined frameworks or code included as Network Mapper (Nmap), Wireshark, Advance port scanner. Operation is utilized for evaluating effective salvation and examination. The process needs capturing the data from a network, storing it, and performing its analysis using certain tools such as Nmap and Wireshark which are some of the most accurate tools available for examining DT communique and cyber forensic. To instigate the assemble, and salvation section, Wireshark brings into play the usage of power shell to attain data from the complex lattice. The considered section is applied using customers’ outlined data related to a Nmap tool, where the keyword “IP detail” comprises data of entire sachets. A minor structure of dual machines individually consuming the running system, and a single system having IP address 192.168.0.100 is utilized to take off port screening of bout on the target machine having IP address 192.168.102.
Rotaflip: A New CNN Layer for Regularization and Rotational Invariance in Medical Images.

Regularization in convolutional neural networks (CNNs) is usually addressed with dropout layers. However, dropout is sometimes detrimental in the convolutional part of a CNN as it simply sets to zero a percentage of pixels in the feature maps, adding unrepresentative examples during training. Here, we propose a CNN layer that performs regularization by applying random rotations of reflections to a small percentage of feature maps after every convolutional layer. We prove how this concept is beneficial for images with orientational symmetries, such as in medical images, as it provides a certain degree of rotational invariance. We tested this method in two datasets, a patch-based set of histopathology images (PatchCamelyon) to perform classification using a generic DenseNet, and a set of specular microscopy images of the corneal endothelium to perform segmentation using a tailored U-net, improving the performance in both cases.
The Importance of Cognitive Styles: Dependence and Field Independence (DIC) in the Memory Process

It is in the framework of a cognition orientation, or theory of information processing, that we seek the theoretical models that can best support us in the analysis of human cognitive functioning, specifically in the tasks of learning and problem solving.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining

Nowadays, customer's demands for E-commerce are more diversified, which introduces more complications to the product retrieval industry. Previous methods are either subject to single-modal input or perform supervised image-level product retrieval, thus fail to accommodate real-life scenarios where enormous weakly annotated multi-modal data are present. In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories. To promote the study of this challenging task, we contribute Product1M, one of the largest multi-modal cosmetic datasets for real-world instance-level retrieval. Notably, Product1M contains over 1 million image-caption pairs and consists of two sample types, i.e., single-product and multi-product samples, which encompass a wide variety of cosmetics brands. In addition to the great diversity, Product1M enjoys several appealing characteristics including fine-grained categories, complex combinations, and fuzzy correspondence that well mimic the real-world scenes. Moreover, we propose a novel model named Cross-modal contrAstive Product Transformer for instance-level prodUct REtrieval (CAPTURE), that excels in capturing the potential synergy between multi-modal inputs via a hybrid-stream transformer in a self-supervised manner.CAPTURE generates discriminative instance features via masked multi-modal learning as well as cross-modal contrastive pretraining and it outperforms several SOTA cross-modal baselines. Extensive ablation studies well demonstrate the effectiveness and the generalization capacity of our model. Dataset and codes are available at https: //github.com/zhanxlin/Product1M.
유아 다문화가정 자녀 (한)국어 교육에 대한 개선점 연구 - 유아교사의 인식을 중심으로

This paper studies Korean education and the educational environments concerning children from multicultural families based upon the comparative analysis of the two surveys targeting early childhood teachers carried out in 2011 and 2019. We divided the surveys into two different parts, ‘status of Korean language education’ and ‘noteworthy points for Korean language education’ for exquisite analysis. In the ‘status of Korean language education,’ the 2019 survey compared to that of 2011 shows that negative responses have increased with regard to the number of times of special Korean lessons for children from multicultural families, and language teaching environments are still in a poor situation. Besides, the degree of concern of the parents of multicultural families for their children has decreased, which seems to be the effect of the economic polarization of multicultural families. In the ‘noteworthy points for Korean language education,’ responses for the necessity of intensified multicultural education for early childhood teachers have slightly increased. It is because the level of multicultural education seems to be still staying fundamental. This result also seems to be partly related to the stricter evaluation criteria of the teachers against children from multicultural families as the number of enrolling children from those families is increasing every year. It is strongly suggested that the specialized Korean teaching program should be developed and separate teaching hours be secured based upon our analysis.
Tutorials on Testing Neural Networks

Deep learning achieves remarkable performance on pattern recognition, but can be vulnerable to defects of some important properties such as robustness and security. This tutorial is based on a stream of research conducted since the summer of 2018 at a few UK universities, including the University of Liverpool, University of Oxford, Queen's University Belfast, University of Lancaster, University of Loughborough, and University of Exeter. The research aims to adapt software engineering methods, in particular software testing methods, to work with machine learning models. Software testing techniques have been successful in identifying software bugs, and helping software developers in validating the software they design and implement. It is for this reason that a few software testing techniques -- such as the MC/DC coverage metric -- have been mandated in industrial standards for safety critical systems, including the ISO26262 for automotive systems and the RTCA DO-178B/C for avionics systems. However, these techniques cannot be directly applied to machine learning models, because the latter are drastically different from traditional software, and their design follows a completely different development life-cycle. As the outcome of this thread of research, the team has developed a series of methods that adapt the software testing techniques to work with a few classes of machine learning models. The latter notably include convolutional neural networks, recurrent neural networks, and random forest. The tools developed from this research are now collected, and publicly released, in a GitHub repository: \url{https://github.com/TrustAI/DeepConcolic}, with the BSD 3-Clause licence. This tutorial is to go through the major functionalities of the tools with a few running examples, to exhibit how the developed techniques work, what the results are, and how to interpret them.
IMPLEMENTASI MANAJEMEN BERBASIS SEKOLAH OLEH KEPALA SEKOLAH DITINJAU DARI MANAJEMEN KURIKULUM DAN MANAJEMEN KEUANGAN [IMPLEMENTATION OF SCHOOL BASED MANAGEMENT BY PRINCIPAL IN TERMS OF CURRICULUM MANAGEMENT AND FINANCIAL MANAGEMENT]

&lt;div&gt;&lt;p class="1eAbstract-text"&gt;The principal has an important role in every stage of curriculum management and financial management, in this case it includes planning, implementing and monitoring evaluations. However, what about the role of the MBS model school principals in private schools in both curriculum management and financial management, and what factors influence the role of the principal in the Tangerang XYZ Kindergarten private school. The research method used is qualitative-case study. Data were obtained through semi-structured interviews, focus group discussions, observation studies, and documentation studies. While testing the validity of the data is used triangulation method. Based on the findings of the study it was concluded that there were restrictions on the authority of the principal, which had an impact on the role of the principal at each stage of management, especially curriculum management and financial management, with three factors influencing, namely (1) the dominance of the family foundation, (2) the status of the principal PNS and (3) lacking the attitude of the principal as a leader.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;/div&gt;
Muon study hints at new physics

A measurement of the muon’s magnetic moment is at odds with the Standard Model – potentially hinting at new forces or particles – as Edwin Cartlidge reports
The Pyro game: a slow intelligent fire

In the Firefighter problem, a fire breaks out at a vertex of a graph and at each subsequent time step, the firefighter chooses a vertex to protect and then the fire spreads from each burned vertex to every unprotected neighbour. The problem can be thought of as a simplified model for the spread of gossip or disease in a network. We introduce a new two-player variation called the Pyro game, in which at each step, the fire spreads from one burned vertex to all unprotected neighbours of that vertex. The fire is no longer automated and aims to maximize the number of burned vertices. We show, that unlike the Firefighter problem, one firefighter can contain a fire on the Cartesian grid in the Pyro game. We also study both the Pyro Game and the Firefighter Problem on the infinite strong grid and the complexity of the Pyro game.
Media Literacy in Increasing Reading Interest

Survey of national library in 2015 shows that 90% of Indonesian society prefer watching television rather than reading. This preference is also popular among kids and teens in Kemlagi village, Mojokerto. The appearance of shopisticated technology is influencing reading interest among kids and teens, they are so enganged with their gadget rather than playing or reading. In fact, in using technology, people do not need to read, but a habit and observation. This statement can be proven by seeing a child who able to operate a mobile phone. Besides, smartphone facilitate children to find a motioning picture on Youtube, they can operate a phone or certain application without reading a book to know how an application works. In addition parents are supporting their kids by letting them using their phone rather than reading. This statement can be seen by a number of parents who buying their kids a phone rather than a book. Low interest in reading is influenced by inadequate facilities and illiterate society in that village. Therefore, community service of PIKAT UPN “Veteran” East Java has been enganging a partnership with Empowerment-Development and Family Welfare (PT-PKK) Association of Kemlag, Kemlagi, Mojokerto Regency to hold a media literacy socialization and establish a rural library as an effort in increasing reading interest. Outcomes of this activities are the avaibility of rural library facilities in Kemlag, Kemlagi, Mojokerto Regency, eradicating illiteracy strategy, and published scholarly article in proceeding.
Perceptually Guided End-to-End Text-to-Speech With MOS Prediction

Although recent end-to-end text-to-speech (TTS) systems have achieved high-quality speech synthesis, there are still several factors that degrade the quality of synthesized speech, including lack of training data or information loss during knowledge distillation. To address the problem, we propose a novel way to train a TTS model under the supervision of perceptual loss, which measures the distance between the maximum speech quality score and the predicted one. We first pre-train a mean opinion score (MOS) prediction model and then train a TTS model in the direction of maximizing the MOS of synthesized speech predicted by the pre-trained MOS prediction model. Through this method, we can improve the quality of synthesized speech universally (i.e., regardless of the network architecture or the cause of the speech quality degradation) and efficiently (i.e., without increasing the inference time or the model complexity). The evaluation results for MOS and phone error rate demonstrate that our proposed approach improves previous models in terms of both naturalness and intelligibility.
Rothberg Returns: DNA Sequencing Legend Incites Protein Sequencing Revolution

Genetic Engineering & Biotechnology NewsVol. 41, No. 8 GEN EdgeRothberg Returns: DNA Sequencing Legend Incites Protein Sequencing RevolutionHis new company, Quantum-Si, has merged with a SPAC and is leveraging chip-based technology to digitize proteomicsAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:6 Aug 2021https://doi.org/10.1089/gen.41.08.02AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 8Aug 2021 Information© 2021 by GEN PublishingTo cite this article:Alex Philippidis.Rothberg Returns: DNA Sequencing Legend Incites Protein Sequencing Revolution.Genetic Engineering & Biotechnology News.Aug 2021.10, 12.http://doi.org/10.1089/gen.41.08.02Published in Volume: 41 Issue 8: August 6, 2021PDF download
Bolstering Biometric Data

Sometimes surveyed individuals claim to care greatly about privacy but their actions rarely align with the concern expressed. This disconnect is referred to as the privacy paradox. The privacy paradox is explained by reference to individuals finding it difficult to assign a specific value to their information or identify their information as their own. COVID-19 has accelerated the use of biometric data in technology to provide contactless solutions and implement contact tracing to address the unprecedented public health challenge. Biometric data is inherently identifiable as it uses our physical or behavioural human characteristics such as our faces, eyes, gait, or fingerprints. As individuals these biometric features are us, we can't easily obtain new features. Given COVID-19 is encouraging technology companies and governments to use biometric data, will this be the catalyst for individuals to align their actions to their concerns, and if they do they face the same ‘take it or leave it’ position we have experienced pre-COVID? Technology organisations that have historically benefited from individuals sharing their personal information in exchange for convenience may be faced with a backlash, as too many governments could use contact tracing to develop surveillance. Privacy laws have been written by legislators, applied by companies and administered by regulators. As the information being collected moves from information about us to information that is fundamentally the essence of our being, do the laws need to be updated to empower individuals to manage and control their biometric data?