Reviewerly/exHarmony huggingface dataset:
MISO – Motion in the Science Ocean Project Based Learning Using Mobile Devices – Synthesis of Biodiesel by Homogeneous or Heterogeneous Catalysis

Six European schools from very different parts of Europe (Agrupamento de Escolas da Maia - Portugal, Anne-Frank-Schule Bargteheide – Germany, Ozel Bilkent Lisesi – Turkey,  Sandsli VGS – Norway, Institut Pau Vila – Spain and Liceul Teoretic Iancu C. Vissarion - Romenia, have developed an Erasmus+ project for Exchange of Good Practices, MISO, in order to increase student interest in science, foster the use of ICT with different applications for experiments, share experiences between teachers and students and collaboratively examined new approaches to teaching and learning of science (E-learning Tools). In recent years the automobile industry has been looking for alternatives to reduce the consumption of fossil fuels. One of the solutions can be vehicles containing a normal internal combustion engine that uses the biodiesel as combustion This research study used Project Based Learning as pedagogical methodology, through the use of mobile devices and was done by students of Chemistry of 12th grade. Students aimed to synthesize Biodiesel by transesterification of refined soybean oil with methanol by basic catalysis using laboratory materials. They studied the effect of the amount of catalyst on the synthesis of biodiesel, they evaluated the acidity index of biodiesel and they also studied the miscibility of the produts obtained with ethanol and hexane. FT-IR was used to monitor the reaction . Mobile devices were used during the project to take photos and to record videos of the experimental procedure. Different type of APPS (Magisto, Animoto, Popplet and Scapple) were used to report the results . This work showed that transesterification is a simple, easy and economically accessible method of synthesizing biodiesel. This project enabled students to deepen and apply their previous and new learning, develop their critical thinking, their creativity and communication, increase their autonomy, organizational management of work, interpersonal relations and motivation for the learning .
Video Captioning with Text-based Dynamic Attention and Step-by-Step Learning

Automatically describing video content with natural language has been attracting much attention in CV and NLP communities. Most existing methods predict one word at a time, and by feeding the last generated word back as input at the next time, while the other generated words are not fully exploited. Furthermore, traditional methods optimize the model using all the training samples in each epoch without considering their learning situations, which leads to a lot of unnecessary training and can not target the difficult samples. To address these issues, we propose a text-based dynamic attention model named TDAM, which imposes a dynamic attention mechanism on all the generated words with the motivation to improve the context semantic information and enhance the overall control of the whole sentence. Moreover, the text-based dynamic attention mechanism and the visual attention mechanism are linked together to focus on the important words. They can benefit from each other during training. Accordingly, the model is trained through two steps: "starting from scratch" and "checking for gaps". The former uses all the samples to optimize the model, while the latter only trains for samples with poor control. Experimental results on the popular datasets MSVD and MSR-VTT demonstrate that our non-ensemble model outperforms the state-of-the-art video captioning benchmarks.
Functions in Windows

Passing argument to functions is simple when you have four or fewer non-floating-point arguments. You use rcx, rdx, r8, and r9 and provide shadow space on the stack before calling the function. After the call, you re-adjust the stack for the shadow space, and everything is fine. If you have more than four arguments, things are more complicated.
The Effect of Peer Assisted Learning Strategy on the Students’ Reading Comprehension at MTs Islamiyah Medan

The objective of this study is to identify the significantly effect on the students’ achievement in reading comprehension by Peer Assisted Learning Strategy (PALS). This research is an experimental which was conducted at MTs Islamiyah Medan, Indonesia. Academic year 2018/2019. The population of this research was the first grade which distributed into two classes with total 60 students. Experimental group consisted of 30 students and control group consisted 30 students. The instruments for collecting data in this research was written test in form multiple choice test, consist of 20 questions. Pre-test and post-test were conducted in both, experimental and control group. The finding showed that the students who taught by Peer Assisted Learning Strategy (PALS) got higher score than those who taught by using teacher centered learning strategy. The result showed that there was significant effect of using Peer Assisted Learning Strategy (PALS) on the students’ reading comprehension which was proven for the result of analysis, t0 was higher than tt (15.6 > 2.00) at α 0.05, and df 58. It means the hypothesis alternative was accepted.
Methodological tool development to improve competitiveness of university international activities

The improvement of Russian higher education competitiveness is a strategic task for the country socio-economic development. The higher school trains experts who will have to solve the complex tasks faced by the country economy, and provide a technological breakthrough. At that it should be noted that the modern graduate should have skills in the global economy. The implementation of measures improving the competitiveness of Russian higher education is faced with a number of restrictions caused by the global economic development. One of the main limitations is state funding reduction for universities, which forces them to seek new sources of resources, including the external market. This leads to increased competition between universities for applicants. It should be noted that competition is not limited only to the national market, it acquires an international character. In developed economies, higher education is one of the sources of export earnings. Besides, foreign countries recruit qualified labor through international educational projects. One of the tools to improve the competitiveness of university is its international activity. On this basis, it follows that the development of methodological tools for international activity management in a higher education institution is an urgent task today.
Secure e-Health Record System Using Identity-based Encryption with Embedded Key

The existence of electronic health record in the Internet world can result in the emergence of potentially unauthorized users to access and abuse the data. Therefore, it is necessary to have a high level of security that can maintain the confidentiality of electronic health record data. In cryptography, the longer the key then, the higher the level of security achieved. However, this can lead to slow computing time. Therefore, we propose a security method with an identity-based encryption scheme that is built hybrid using elliptic curve cryptography (ECC) and elliptic curve integrated encryption system (ECIES) algorithms or can be abbreviated as IBE-ECC-ECIES. An additional feature of the proposed method is the creation of key pairs generated by the ECC algorithm and there is an identity that has been embedded in the key pair in order to increase the level of security and uniqueness of the key. This method has been compared based on analysis of performance, computation time and the level of security in the same environment. This method is another IBE hybrid scheme called IBE-ECC-AES. The results of the test showed that the proposed method was superior to 0.3 seconds compared to the comparison method in the key pair generation process. In addition, security in the proposed method can overcome sniffing and chosen-plaintext attacks.
The Sloth Who Slowed Us Down by M. Wild

Wild, Margaret. The Sloth Who Slowed Us Down. Illustrated by Vivienne To, Abrams Books for Young Readers, 2018.&#x0D; You’ll want to make a speedy trip to the nearest bookstore to pick up a copy of Margaret Wild’s The Sloth Who Slowed Us Down. Together with Vivienne To’s illustrations, this simple story about how a little sloth can be a big example could make anyone want to stop and smell the roses. Life seems to speed up every day, work needs to get done faster so we have time to quickly make dinner, quickly exercise, and then quickly move on to the next thing we feel like we need to speed through. In her newest children’s book, Wild’s descriptive prose directly mirrors Sloth as he teaches Amy’s family the importance of taking our time and enjoying living in the moment. Realistically, we are all very busy, moving from one task to the next without indulging in the little things, the happy moments and the details. Everyone from busy families to teachers to even grown-ups with grown-up jobs and responsibilities could benefit from giving this adoring story a read.&#x0D; The colourful but soft illustrations created by To provide new detail and add more expression to Wild’s story each time it’s read. Illustrations of Sloth make you want to snuggle him while you read this story and feel like a child again. Through her descriptive writing, Wild portrays Sloth’s actions quite clearly. She includes phrases, such as “Sloth had a long, leisurely bath. . .” that roll off the tongue in a way that makes you feel like you’re taking your time, but in the best sort of way. &#x0D; Highly recommended: 4 out of 4 starsReviewer: Darilyn Randall&#x0D; Darilyn Randall is a fourth-year student at the University of Alberta completing her Bachelor of Elementary Education. She is interested in teaching in a Division 1 classroom where she can incorporate children’s literacy into cross-curricular activities.
Synthetic Video Generation for Robust Hand Gesture Recognition in Augmented Reality Applications

Hand gestures are a natural means of interaction in Augmented Reality and Virtual Reality (AR/VR) applications. Recently, there has been an increased focus on removing the dependence of accurate hand gesture recognition on complex sensor setup found in expensive proprietary devices such as the Microsoft HoloLens, Daqri and Meta Glasses. Most such solutions either rely on multi-modal sensor data or deep neural networks that can benefit greatly from abundance of labelled data. Datasets are an integral part of any deep learning based research. They have been the principal reason for the substantial progress in this field, both, in terms of providing enough data for the training of these models, and, for benchmarking competing algorithms. However, it is becoming increasingly difficult to generate enough labelled data for complex tasks such as hand gesture recognition. The goal of this work is to introduce a framework capable of generating photo-realistic videos that have labelled hand bounding box and fingertip that can help in designing, training, and benchmarking models for hand-gesture recognition in AR/VR applications. We demonstrate the efficacy of our framework in generating videos with diverse backgrounds.
Diversity by Phonetics and its Application in Neural Machine Translation

We introduce a powerful approach for Neural Machine Translation (NMT), whereby, during training and testing, together with the input we provide its phonetic encoding and the variants of such an encoding. This way we obtain very significant improvements up to 4 BLEU points over the state-of-the-art large-scale system. The phonetic encoding is the first part of our contribution, with a second being a theory that aims to understand the reason for this improvement. Our hypothesis states that the phonetic encoding helps NMT because it encodes a procedure to emphasize the difference between semantically diverse sentences. We conduct an empirical geometric validation of our hypothesis in support of which we obtain overwhelming evidence. Subsequently, as our third contribution and based on our theory, we develop artificial mechanisms that leverage during learning the hypothesized (and verified) effect phonetics. We achieve significant and consistent improvements overall language pairs and datasets: French-English, German-English, and Chinese-English in medium task IWSLT'17 and French-English in large task WMT'18 Bio, with up to 4 BLEU points over the state-of-the-art. Moreover, our approaches are more robust than baselines when evaluated on unknown out-of-domain test sets with up to a 5 BLEU point increase.
Penerapan Clustering dalam Mengelompokkan Jumlah Kunjungan Wisatawan Mancanegara Dengan Metode K-Means

The Indonesian tourism sector currently contributes approximately 4% of the total economy. In 2019, the Indonesian Government wants to increase this figure to double to 8% of PDB (Produk Domestik Bruto), a target that implies that within the next 4 years, the number of visitors needs to be doubled to approximately 20 million tourists. This study discusses the Application of Clustering in Grouping the Number of Foreign Tourist Visits by Nationality and Month of Arrival by the K-Means Method. The source of this research data was collected based on data on the number of foreign tourist visits produced by the National Statistics Agency. K-Means clustering is one of the data mining techniques that gives a description of an item's cluster. The purpose of this study is to classify the number of foreign tourists in Indonesia. The results of this study are grouping the number of foreign tourist visits grouped by two clusters (high and low), high clusters of 4 countries and low clusters of 87 countries. Countries that are included in the lower clusters can be used for the Government of Indonesia in terms of improving existing facilities in tourist attractions so that visiting tourists will increase in the future.
Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis

The labels in medical diagnosis task are usually discrete and successively distributed. For example, the Diabetic Retinopathy Diagnosis (DR) involves five health risk levels: no DR (0), mild DR (1), moderate DR (2), severe DR (3) and proliferative DR (4). This labeling system is common for medical disease. Previous methods usually construct a multi-binary-classification task or propose some re-parameter schemes in the output unit. In this paper, we target on this task from the perspective of loss function. More specifically, the Wasserstein distance is utilized as an alternative, explicitly incorporating the inter-class correlations by pre-defining its ground metric. Then, the ground metric which serves as a linear, convex or concave increasing function w.r.t. the Euclidean distance in a line is explored from an optimization perspective. Meanwhile, this paper also proposes of constructing the smoothed target labels that model the inlier and outlier noises by using a unimodal-uniform mixture distribution. Different from the one-hot setting, the smoothed label endues the computation of Wasserstein distance with more challenging features. With either one-hot or smoothed target label, this paper systematically concludes the practical closed-form solution. We evaluate our method on several medical diagnosis tasks (e.g., Diabetic Retinopathy and Ultrasound Breast dataset) and achieve state-of-the-art performance.
Fast Multi Scale Face Detection ConvNet

The paper presents a face detector which can work on various scales, illumination levels, angles and occlusion levels. This detector will act as a pre-processing unit for applications such as emotion recognition, facial recognition, facial key point estimation etc. The Convolutional Neural Network, works on a Single forward pass based method and is inspired by the SSD architecture [8] . The current methods are highly computationally expensive which has an impact on their real time performance. A convent model has been created which works on a smaller input image size and uses a single forward pass to predict the faces. Various versions of the model have been created with small tweaks to the final layers. The model has been trained using a multi stage transfer learning. The model runs on an input image size of 512x512 and runs at 60fps on a NVIDIA 1060 GPU. The model is trained and Tested on the highly challenging Wider Faces Dataset [12]. The best performing version of the model has an MAP of 72% on the wider face easy validation set.
Incorporating Non-local and Task-specific Features for Instance Segmentation

This paper proposes a novel instance segmentation model, which improves the instance segmentation by considering two aspects. One is a new non-local features module to recover detailed information that is lost in the deep convolutional operations. The other is to introduce attention mechanism to generate specific features adaptive to each task. The proposed method is verified on three well-known datasets, namely Pascal VOC, Cityscapes and COCO. The experiments show that the method using the proposed modules outperforms baseline Mask R-CNN on all of the datasets without bells and whistles.
Quantum Density of the Medium and Gravitational Potentials

The quantum theory of gravity characterizes the state of quantized space-time with a new parameter: the quantum density of the medium. The new parameter is the concentration of quantons in a unit volume of space. Quanton is a quantum of space-time is the basic particle in the quantum theory of gravity. The gravitational field inside the quantized space-time is characterized by four parameters of the quantum density of the medium: ρ0, ρ1 ρ2 ρn. The quantum density of the medium is an analogue of the gravitational potentials. Each parameter of the quantum density of the medium has its own analog of the gravitational potential: φ0, φ1 φ2 φn. Previously, the theory of gravity had only one gravitational potential: the Newtonian gravitational potential φn. Because of this, we had big problems for gravitational computing. The quantum density of the medium and additional gravitational potentials allowed us to solve many problems of the theory of gravity that were previously considered unsolvable. But we have the main thing: we created the quantum theory of gravity almost a quarter century ago [1, 2]. I am surprised when I see thousands of articles with the big names of the theory of quantum gravity, and inside these articles are empty and uninteresting. In theoretical physics, we have fierce competition between scientists. And only a few of them manage to say a newer word in physics once a century. I created the theory of Superunification, the theory of quantum gravity, quantum thermodynamics, quantum energy, a quantum engine and much more. The theory of Superunification is the basis of new energy and space technologies [1-6].
An analysis of the impact of Virtual Machines allocation on the cloud - The case of Full Waveform Inversion on Amazon Web Services

In order to optimize free cash flow, today, companies are reducing the capital expenditure (CAPEX) by outsourcing their computational processes.Additionally, cloud computing offers an attractive possibility of on-demand use of up-to-date virtualmachine clusters designed by and tailored to the user needs.In this way, it avoids the problem of acquiring, maintaining and updating in-house hardware.Cloudcomputing clusters are realized by means of virtual machines that are allocated on physical servers, which are located on the cloud provider data centers.Their specific location may affect network communication performance and optimal virtual machine allocation depends on the specifics of the computational task, as well as the possibilities offered by the cloud provider.Some cloud providers allow users to choose among different allocation strategies when instantiating their set of virtual machines.In this work, we analyze the impact of different virtual machine allocation strategies on the performance and cost of executing a seismic imaging application, namely full waveform inversion (FWI), on a specific cloud provider, namely Amazon Web Services (AWS).Our results indicate that the virtual machine allocation strategy may significantly affect the performance and the cost of executing the FWI application on the cloud, which may cause the execution to be up to 3.1 times more expensive.
Clouds in the Kill Chain

Abstract Paolo Passeri, Cyber Intelligence Principle at Netskope, considers the impact of cloud technology throughout the kill chain and asks what security professionals need to know.
The Feasibility of E-Learning Media on Anecdote Text Material for X Grade Students of Senior High School Harapan 3 Delitua, Indonesia

The feasibility of anecdote text e-learning based learning media is based on the validation test of material experts and design experts. After being validated, the research products can be tested on individual students, small groups and limited groups. The following will be explained the feasibility test of e-learning based text and text learning media. Research methods in this research plan include: location and time of study, population and sample of research, research design, operational definition, research procedures, types of data, techniques and data collection instruments. The conclusion is that the use of e-learning based anecdote text learning media is more effective than non-e-learning based learning, this can be seen from the average results of the pretest and posttest and the achievement of the indicator value of students in using e-learning learning media.
HIGH-RESOLUTION GEOMETRY MEASUREMENT WITH A COLLABORATIVE ROBOT

The improvements of robust optical 3D metrology and robot systems are opening new possibilities of inline optical geometry measurements.The combination of these two facts allows measurements using the robot as manipulation system to increase the measurement area beyond a single field of view.By examining the sample from different angles using the robot to obtain high-resolution 3D datasets also from undercuts, measurements which have to date not been possible due to light/shade influences can now be achieved.This method is important on big samples with complex geometries.Special algorithm uses the position and angle of the robot tool centre point, in this case the focus point of the measuring system, for the pre-alignment of the different 3D datasets before the final stitching is performed.Further algorithm is used to improve the overall accuracy through dataset alignment.In this paper, measurement examples are presented, which examine and aim to minimize the uncertainty of this optical 3D metrology and robot combination and are tested against international standards.