Reviewerly/exHarmony huggingface dataset:
360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales

Gaze estimation involves predicting where the person is looking at within an image or video. Technically, the gaze information can be inferred from two different magnification levels: face orientation and eye orientation. The inference is not always feasible for gaze estimation in the wild, given the lack of clear eye patches in conditions like extreme left/right gazes or occlusions. In this work, we design a model that mimics humans' ability to estimate the gaze by aggregating from focused looks, each at a different magnification level of the face area. The model avoids the need to extract clear eye patches and at the same time addresses another important issue of face-scale variation for gaze estimation in the wild. We further extend the model to handle the challenging task of 360-degree gaze estimation by encoding the backward gazes in the polar representation along with a robust averaging scheme. Experiment results on the ETH-XGaze dataset, which does not contain scale-varying faces, demonstrate the model's effectiveness to assimilate information from multiple scales. For other benchmark datasets with many scale-varying faces (Gaze360 and RT-GENE), the proposed model achieves state-of-the-art performance for gaze estimation when using either images or videos. Our code and pretrained models can be accessed at https://github.com/ashesh-0/MultiZoomGaze.
Emulating Trusted Platform Module 2.0 on Raspberry Pi 2

A computer hijacked by a malware may pretend that it is normal as usual and retrieve secrets from storage of itself and other victim computers.By adopting trusted computing technology a computer's former health status cannot be forged.Computers can thus detect the change of health status of a hijacked computer and prevent the leakage of the secrets.As Trusted Computing Group (TCG) proposed Trusted Platform Module (TPM) specification, IBM implemented software TPM (sTPM) and utilities for engineer who wants to learn the operating principle of TPM.Meanwhile, the blooming of tiny size, but powerful, computers, e.g.Raspberry Pi 2 (Rpi2), attract ones to develop some dedicated applications on the computers.In this article, we report the verified steps for installing new sTPM version on RPi2.After the installation, we also test the functionality and evaluate the performance of the sTPM with some major TPM Commands.The real behaviour of and the traffic between the host computer and the emulated TPM can thus be learned easily.
LibGuides: SUNY Online: Home

Designed to assist SUNY Online coaches in their work with SUNY Online students using the SUNY Empire State College Online Library.
تأثیر استراتیجیة ( فکر – زاوج – شارک ) المدعمة باستخدام الهاتف النقال على تعلم دورانى سباحتى الزحف على البطن وعلى الظهر للمبتدئین

يThe aim of this research is to identify the effect of the strategy (think - mate - share) supported by the use of a mobile phone on learning the rotation of the two swimming pools, crawling on the stomach and on the back for beginners. By the random method on two groups with (10) learners for each group, one of the most important results was that the educational program using the strategy (think - pair - share) supported by a mobile phone was more effective than the used method (the command method) in improving the level of learning for the two swimming pools, crawling on the stomach and on The back is recommended by the researcher to use the educational program using the strategy (think - pair - share) supported by the mobile phone in teaching the two swimming circles, crawling on the stomach and on the back for beginners
Robust Deep Learning Ensemble against Deception

Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the training data. How to protect a machine learning model against deception of both types of destructive inputs remains an open challenge. This paper presents XEnsemble, a diversity ensemble verification methodology for enhancing the adversarial robustness of DNN models against deception caused by either adversarial examples or out-of-distribution inputs. XEnsemble by design has three unique capabilities. First, XEnsemble builds diverse input denoising verifiers by leveraging different data cleaning techniques. Second, XEnsemble develops a disagreement-diversity ensemble learning methodology for guarding the output of the prediction model against deception. Third, XEnsemble provides a suite of algorithms to combine input verification and output verification to protect the DNN prediction models from both adversarial examples and out of distribution inputs. Evaluated using eleven popular adversarial attacks and two representative out-of-distribution datasets, we show that XEnsemble achieves a high defense success rate against adversarial examples and a high detection success rate against out-of-distribution data inputs, and outperforms existing representative defense methods with respect to robustness and defensibility.
Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intents. Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection.
예비 역사교사의 교육적 경험 연구 -‘고등학교 한국사 교과서 쓰기’를 중심으로-

Although a position of textbook is lowered recently, textbook’s influences are still significant in Korea’s public education system. Even if studies on teachers and students who are consumers of such textbooks are intermittently performed, their interests in educational experience are still insufficient beyond analysis of textbook.Under this circumstance, this study has focuses on preservice secondary history teachers who have been consumers for past textbooks and would be those for future textbooks. Through results yielded by a theme of experiencing ‘Writing textbook along with 17 preservice secondary history teachers, this study not only assessed preservice secondary teachers’ views on history textbook and existing textbook but also reviewed what characteristics exist on such results.This study showed implications that preliminary history teachers are able to enhance their understanding and sensitivity of educational writing via educational experience of ‘Writing Korean history textbook’ and at the same time their analysis for results from writing textbook is able to accompany understanding of consumers as to discussion of Korea history textbook.
RANCANG BANGUN SISTEM INFORMASI BADAN USAHA MILIK DESA ( BUMDES ) BERBASIS WEB MOBILE

The information system of village-owned enterprises is still done manually, and in delivering information to the people in the village of Sidokayao, Abung Tinggi Subdistrict, North Lampung Regency, so that some people do not know about Sidokayo Village, there are bumdes activities, therefore, an application must be designed using internet facilities that can used by the public in accessing information and can save time, thus in Sidokayo village a website application is needed.With the existence of a mobile web-based village-owned enterprise information system it is hoped that it can facilitate access to information and improve services in marketing businesses in the Sidokayo village managed by BUMDes.
The role of quantum correlations in entangled two-photon absorption

The theoretical description of entangled photon absorption in complex multilevel systems undergoing dissipative dynamics is reviewed. The impact of quantum correlations on the efficiency of the absorption process and the excited state distributions is discussed.
Exploring the Relationship among the Brain Knowledge, Certainty of Knowledge, Learning pathway, and Growth Mindset in Korean Undergraduates

본 연구는 국내 대학생의 뇌과학 지식 수준과 지식에 대한 확신성을 조사하고, 특히 잘못된 뇌과학정보의 주된 학습 경로에 따라 지식의 정확도와 확신성이 어떻게 달라지는지 밝히고자 하였다. 또한, 성장신념과 뇌과학 지식 수준의 관계를 살펴보았다. 대학생 312명을 대상으로 설문을 시행하였고, 연구결과는 다음과 같다. 첫째, 많은 대학생이 잘못된 뇌과학 지식을 갖고 있었으며 이에 대해 높은 확신성을 보였다. 특히 뇌신경계신화 중 오답률이 가장 높았던 것은 VAK 학습유형 원리였다(91.3%). 둘째, 학생들은 뇌과학 정보를 주로 방송 및 인터넷을 통해 학습하였으며 잘못된 뇌과학지식의 주요 학습 경로는 방송 및 인터넷, 책, 주변 사람들 순으로 나타났다. 셋째, 뇌과학 지식 수준이 성장신념을 예측하지는 못했지만, 신경계신화 수준은 노력에 관한 믿음을 예측했다. 본 연구결과는 학습자가 정확한 뇌과학 지식을 얻을 수 있도록 학습자들에게 올바른 학습 경로를 제시하고 체계적인 교육을 제공해야 할 필요가 있음을 시사한다.With a sample of 312 undergraduate students, the present study aimed to identify the level of brain knowledge, the major learning pathway, the certainty of knowledge, and how the certainty of inaccurate knowledge varied as a function of students’ learning pathway. In addition, we examined the relationship between the level of brain knowledge and the growth mindset (i.e., the belief that intelligence is malleable). Results showed that, first, many students demonstrated inaccurate understanding about the brain and high level of certainty toward their false beliefs about the brain. Among the neuromyth items, the VAK learning style myth showed the highest rate of incorrect answers (i.e., 91.3%). Second, students reported they acquired brain knowledge mainly from TV and the internet, books, and the people around them. Inaccurate knowledge about the brain was also obtained mainly from TV and the internet, books, and the surrounding people. Third, the level of brain knowledge did not predict students’ growth mindset, but the level of neuromyths significantly predicted students’ effort beliefs. Our findings suggest that students should acquire their brain knowledge via credible learning pathways so that they can obtain accurate knowledge about the brain.
Graph-to-Sequence Neural Machine Translation

Neural machine translation (NMT) usually works in a seq2seq learning way by viewing either source or target sentence as a linear sequence of words, which can be regarded as a special case of graph, taking words in the sequence as nodes and relationships between words as edges. In the light of the current NMT models more or less capture graph information among the sequence in a latent way, we present a graph-to-sequence model facilitating explicit graph information capturing. In detail, we propose a graph-based SAN-based NMT model called Graph-Transformer by capturing information of subgraphs of different orders in every layers. Subgraphs are put into different groups according to their orders, and every group of subgraphs respectively reflect different levels of dependency between words. For fusing subgraph representations, we empirically explore three methods which weight different groups of subgraphs of different orders. Results of experiments on WMT14 English-German and IWSLT14 German-English show that our method can effectively boost the Transformer with an improvement of 1.1 BLEU points on WMT14 English-German dataset and 1.0 BLEU points on IWSLT14 German-English dataset.
The third age of augmented, so-called artificial intelligence

The observation made by Herbert Simon, 1978 Nobel Prize in economics, that AI is far removed from human intelligence navigating in an uncertain world, remains more correct than ever in 2020. First, AI lacks methodological rigor on the constitution and processing of data. Worse: after having privileged the symbolic intelligence model, which has found its limits, it now takes as its model the intuitive intelligence of children under 6, what is more in a very crude and cartoonish way. Shouldn’t we rather try to go towards a third age of AI, increasing our capacities rather than mimicking them, and relying on all the springs of intelligence?
Knowledge Graphs for Multilingual Language Translation and Generation

The Natural Language Processing (NLP) community has recently seen outstanding progress, catalysed by the release of different Neural Network (NN) architectures. Neural-based approaches have proven effective by significantly increasing the output quality of a large number of automated solutions for NLP tasks (Belinkov and Glass, 2019). Despite these notable advancements, dealing with entities still poses a difficult challenge as they are rarely seen in training data. Entities can be classified into two groups, i.e., proper nouns and common nouns. Proper nouns are also known as Named Entities (NE) and correspond to the name of people, organizations, or locations, e.g., John, WHO, or Canada. Common nouns describe classes of objects, e.g., spoon or cancer. Both types of entities can be found in a Knowledge Graph (KG). Recent work has successfully exploited the contribution of KGs in NLP tasks, such as Natural Language Inference (NLI) (KM et al.,2018) and Question Answering (QA) (Sorokin and Gurevych, 2018). Only a few works had exploited the benefits of KGs in Neural Machine Translation (NMT) when the work presented herein began. Additionally, few works had studied the contribution of KGs to Natural Language Generation (NLG) tasks. Moreover, the multilinguality also remained an open research area in these respective tasks (Young et al., 2018). In this thesis, we focus on the use of KGs for machine translation and the generation of texts to deal with the problems caused by entities and consequently enhance the quality of automatically generated texts.
Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics

We demonstrate how a target model's generalization gap leads directly to an effective deterministic black box membership inference attack (MIA). This provides an upper bound on how secure a model can be to MIA based on a simple metric. Moreover, this attack is shown to be optimal in the expected sense given access to only certain likely obtainable metrics regarding the network's training and performance. Experimentally, this attack is shown to be comparable in accuracy to state-of-art MIAs in many cases.
Ebraismo: vecchie e nuove diaspore

Every discussion of Jewish Diaspora is different if placed before or after the Age of Emancipation. Judaism is defined by three fundamental words. Torah (Law), People, Land. Erez Israel (Land of Israel) is a Holy Land if People obey the Law. People can obey the Law also outside the Land of Israel. The Jewish civilization owes much to the European culture, including the birth of Zionism. Today the most important problem of the Diaspora is constituted by its relationships with the State of Israel
Trials & Tribulations: Effective Management of Electronic Resource Trials

Presentation for the 2019 Electronic Resources and Libraries conference. This presentation discusses best practices, the results of a survey of librarians on how trials are currently utilized, and responses from vendors about how trials are viewed
Personalized Learning as A Method of Moral and Intellectual Development in The Humanitarization of the Post-Industrial Society

The article presents the post-industrial society problem in the context of personal self-existence of the individual. A spiritually oriented humanitarian paradigm in the intellectual and moral development of the individual in the framework of solving the post-industrial society problems is identified. Personalized learning is considered as a special learning type focused on the personal and intellectual development of the individual on the way from egocentrism to altruism. Intellectual development in the framework of metacognitive learning strategies is presented. The spiritual and moral development of students is defined as personal reflection development. The article presents the empirical study data on the moral orientation of students' personality in educational organizations.
فعالية استخدام لعبة سلسلة القصص كالوسيلة التعليمية لترقية مهارة الكتابة بمدرسة سونن كالي جاغا الثانوية الإسلامية سورابايا

The majority of Eighth grade students at Sunan Kali Jaga Islamic High School Surabaya are weak in our Islamic scholarship. They have difficulty in composing sentences in accordance with the correct rules of writing Arabic. This study aims to determine how effective the use of the game "Fairy Tale Chains" to improve the maharah of students of the eighth grade of Sunan Kali Jaga Islamic High School Surabaya. The research method used in this study is a quantitative method using data collection methods consists of methods of observation، tests، interviews، and documents by taking a sample of 40 eighth grade students of Sunan Kali Jaga Islamic High School Surabaya. From this research it can be seen that there is an increase in our students' happiness after applying the game "Fairy Tale Chains". with the analysis obtained with the t-test: t_0 the result is 16.29 and tt is 1.699 because t_0 is greater than tt then the null hypothesis (Ho) is rejected and the alternative hypothesis (Ha) is acceptable.
HarBest: An e-Commerce Agricultural Product Delivery Service Analyzed and Designed Using Progressive UI Framework and MySQL

This study was conducted with the aim of analyzing and designing an e-commerce platform for agricultural products delivery service.The proponents named this system application as "HarBest" which means best harvest.This application is designed to be accessible to its target clienteles anytime provided that internet connectivity is available.The researchers used progressive UI framework as the front-end while for the back-end, MySQL application was utilized.Progressive UI framework, as used in this study, includes a server-side scripting language called PHP version 5 as the front-end; CSS3 and Bootstrap 4 for styling purposes; and HTML5 for forms creation.PHP 5 was used since it is compatible to most of the servers available in the web.HarBest is consists of modules which are designed to improve the marketing strategy of the farmers which in turn will result to customers' satisfaction.It also provides a security module which has five access levels.These access levels are for the system administrator, farmers, customers, drivers and guests.
Input Hessian Regularization of Neural Networks

Regularizing the input gradient has shown to be effective in promoting the robustness of neural networks. The regularization of the input's Hessian is therefore a natural next step. A key challenge here is the computational complexity. Computing the Hessian of inputs is computationally infeasible. In this paper we propose an efficient algorithm to train deep neural networks with Hessian operator-norm regularization. We analyze the approach theoretically and prove that the Hessian operator norm relates to the ability of a neural network to withstand an adversarial attack. We give a preliminary experimental evaluation on the MNIST and FMNIST datasets, which demonstrates that the new regularizer can, indeed, be feasible and, furthermore, that it increases the robustness of neural networks over input gradient regularization.
ANALYZING THE METHOD GAME-BASED LEARNING

This article discusses the benefits of using game-based learning. The purpose of the article: to provide a method that would make it easier for students to understand and accept the new topics at school and to remember the learned materials for a long time.Increasing the interest and activity of schoolchildren is an important part of the learning process. Motivating students is one of the biggest challenges for teachers. It is quite possible that the use of traditional teaching methods alone will cause indifference in students. Therefore, it is better to use active learning methods so that the lesson is not boring. In this regard, the use of game-based learning technology can increase the motivation of students to learn.Based on the research materials, it can be concluded that the game-based learning method facilitates students' learning and motivates students, allows them to work with classmates, i.e. increases the ability to work in groups, helps to overcome indifference to mathematics, creates a fun learning environment.
Extending Learning Beyond the Planetarium with the Dome+ Model

Planetarium visits limit the amount of time and agency people have to explore content relevant to their interest and identities as compared to other informal learning environments.We developed the Dome+ model to guide extending engagement beyond planetariums based on the design principles of identity, interest, and choice.We do this through web resources, social media, and hands-on activities centered on a planetarium show.We present research into the development and implementation of the Dome+ model as it applies to the Big Astronomy Project.This is a multi-institutional effort to share the stories of the people and places that make astronomy happen.The work presented focuses on the implementation of the model during production of the planetarium show.We interviewed team members key in the production of the show and find themes that differ based on project role.We also discuss practical challenges in implementing the Dome+ model.
Type-augmented Relation Prediction in Knowledge Graphs

Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entities. Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones. Most of the existing work is proposed by maximizing the likelihood of observed instance-level triples. Not much attention, however, is paid to the ontological information, such as type information of entities and relations. In this work, we propose a type-augmented relation prediction (TaRP) method, where we apply both the type information and instance-level information for relation prediction. In particular, type information and instance-level information are encoded as prior probabilities and likelihoods of relations respectively, and are combined by following Bayes' rule. Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on four benchmark datasets: FB15K, FB15K-237, YAGO26K-906, and DB111K-174. In addition, we show that TaRP achieves significantly improved data efficiency. More importantly, the type information extracted from a specific dataset can generalize well to other datasets through the proposed TaRP model.
Improve black-box sequential anomaly detector relevancy with limited user feedback

Anomaly detectors are often designed to catch statistical anomalies. End-users typically do not have interest in all of the detected outliers, but only those relevant to their application. Given an existing black-box sequential anomaly detector, this paper proposes a method to improve its user relevancy using a small number of human feedback. As our first contribution, the method is agnostic to the detector: it only assumes access to its anomaly scores, without requirement on any additional information inside it. Inspired by a fact that anomalies are of different types, our approach identifies these types and utilizes user feedback to assign relevancy to types. This relevancy score, as our second contribution, is used to adjust the subsequent anomaly selection process. Empirical results on synthetic and real-world datasets show that our approach yields significant improvements on precision and recall over a range of anomaly detectors.