Reviewerly/exHarmony huggingface dataset:
LibGuides: Lower Grades Citation Guides: Home

This LibGuide is a resource for teachers of K-4. It provides collective information on citation lessons specialized by grade level.
Basic Geophysics - Fault Plane Solutions

How earthquake ruptures are displayed as beach balls?Concept and graphic construction of focal mechanisms as beachballs
Network Structure for Personalized Face-Pose Estimation Using Incrementally Updated Face-Shape Parameters

This paper proposes a deep learning method for face-pose estimation with an incremental personalization mechanism to update the face-shape parameters. Recent advances in machine learning technology have also led to outstanding performance in applications of computer vision. However, network-based algorithms generally rely on an off-line training process that uses a large dataset, and a trained network (e.g., one for face-pose estimation) usually works in a one-shot manner, i.e., each input image is processed one by one with a static network. On the other hand, we expect a great advantage from having sequential observations, rather than just single-image observations, in many practical applications. In such cases, the dynamic use of multiple observations will contribute to improving system performance. The face-pose estimation method proposed in this paper, therefore, focuses on an incremental personalization mechanism. The method consists of two parts: a pose-estimation network and an incremental estimation of the face-shape parameters (shape-estimation network). Face poses are estimated from input images and face-shape parameters through the pose-estimation network. The shape parameters are estimated as the output of the shape-estimation network and iteratively updated in a sequence of image observations. Experimental results suggest the effectiveness of using face-shape parameters in face-posture estimation. We also describe the incremental refinement of face-shape parameters using a shape-estimation network.
Irradiance Prediction Model with Step wise Backward Linear Regression

The objective of this work is to ascertain whether reliable prediction of Global Horizontal Irradiance (GHI) is attainable using predictor variables: wind speed, wind direction, temperature, relative humidity, solar zenith angle, date of year and time. Multivariate backward stepwise linear regressions was utilized to determine significant attributes for GHI estimation. The model was then used to predict value of GHI. The performance of models was investigated using model residuals. The resulting model could explain 67% of variation in data. The model was further tested for multicollinearity and heteroscedasticity. The model gives satisfactory VIF and MAPE values and nominal heterosdasticity. R computation platform was used for modeling and computation.
Pengaruh Penerapan Model Discovery Learning Terhadap Hasil Belajar dan Retensi Belajar Peserta Didik pada Materi Ekosistem Kelas X MIA SMA Negeri 3 Makassar

Abstract: This study is quasy experimental with a pretest-posttest-retest control group design research design that aims to determine the learning outcomes and learning retention of students. The study sample was randomly selected and selected class X MIA 1 as the experimental class and X MIA 5 as the control class. The technique of collecting data through giving multiple choice tests before (pretest) and after (posttest) learning to measure learning outcomes and learning retention after learning (posttest) and two weeks after the posttest administration. The data analysis technique used is descriptive and inferential statistical analysis with anacova test. The results showed that (1) The average value of student learning outcomes learned by the discovery learning model was 12.9% in the excellent category. 45.16% were in the good category. 32.25% are in the sufficient category. 9.67% are in the less category and none is in the very less category. (2) The average learning retention of students taught by discovery learning is 70.96% in high category learning retention. 19.36% are in the moderate category, and 9.67% are in the low category. (3) The discovery learning model influences the learning outcomes and learning retention of students in the X MIA class materials at 3 Makassar Public High School. Keywords: discovery learning, learning outcomes, learning retention
Formal Development and Verification of Reusable Component in PAR Platform

Formal method is an important approach to develop high trust software systems. Coq is an interactive proof assistant for the development of mathematical theories and formally certified software. Set, Bag, List, Tree, Graph are important reusable components in PAR platform. This paper tries to formally develop ‘Set’ components which have linear structure and verify the correctness of this component mechanically with Coq. The formal development of this component involves formalization of specification, the recurrence relation of problem-solving sequence and loop invariant. Specification language Radl of PAR platform was used to describe the specification, recurrence relation and loop invariants; Software modelling language Apla was used to describe the abstract model of those components. The Dijkstra’s Weakest Precondition method is used to verify abstract model by the interactive proof tool Coq. Finally, the abstract model denoted by Apla was transformed to concrete model written by executable language; such as C++, Java, VB and C#, etc., based on the program generating systems in PAR platform.
Analysis on Interactive Teaching Strategies of Englishin Higher Vocational Colleges Based on Artificial Intelligence Technology

The application of the technology in teaching and the development of Internet + industry in education are becoming more and more mature, which promotes the English teaching in higher vocational colleges and other colleges to develop towards a more diversified, modern and intelligent direction and interactive teaching also presents a more diversified and intelligent development trend. Nowadays, intelligent APP and other artificial intelligence technologies have been widely applied to the teaching in and out of class, teaching practice, assessment and other aspects of English classes in higher vocational colleges. Various artificial intelligence English databases also provide convenience for teachers' teaching and independent learning. At the same time, artificial intelligence can change the way of teaching, learning and interaction. The teaching under the environment of artificial intelligence needs more attention to students' autonomy and subjectivity, so it needs to be based on the characteristics of students, designing and implementing personalized teaching and selecting rational intelligent interactive ways and contents to help students study more efficiently and independently. The teaching reform involves all aspects of teaching, so it is not easy to implement interactive teaching with artificial intelligence systematically and perfectly. This paperdiscusses and analyses effective strategies of interactive teaching English in higher vocational colleges based on artificial intelligence technology from the analysis of students' demand, optimizing the teaching organization form, teaching means, assessment methods and other aspects to help higher vocational teachers assist students to learn English more independently, efficiently, with high quality and personalization.
An efficiency virtual resource auction mechanism based on reserve-price strategy in cloud environments

In economic-based cloud models, resource pricing mechanism plays a key role to regulate the supply and demand for resources, provides an incentive for providers, and motivates the users to trade-off between deadline, budget, and the required level of quality of service. In this paper, we formulate the problem of resource auction as a distributed constrained programming problem; then we propose an effective and efficient auction algorithm, which use reserve-price strategy to guarantee that users pay at least a given amount determined by cloud providers. In this way, we can significantly improve the efficiency of auction process as well as provide sufficient incentive for cloud providers to share their spare resources. To evaluate the proposed auction algorithm, extensive experiments are conducted in a real-world cloud platform, and the results indicate that it can significantly increase the provider revenue as well as improve the quality-of-service (QoS) satisfactory.
Secure Outsourced Integrity Test for Cloud Storage

Cloud-based solutions allow companies to add tools to almost any existing or future infrastructure and for client data to be stored in one place. In this way, clients can generally get to the most recent and right form of information, be that as it may, checking the respectability of the information may prompt extreme overheads, which makes re-appropriating of the honesty check to outsiders an alluring alternative. This paper proposes an AES encryption method to protect the privacy of the user data and avoid data leakage during the third party handlings. We have also shown that the proposed solution does not increase the upload time and is in fact beneficial to the system’s performance
Unsupervised Topic Model Based Text Network Construction for Learning Word Embeddings

Distributed word embeddings have proven remarkably effective at capturing word level semantic and syntactic regularities in language for many natural language processing tasks. One recently proposed semi-supervised representation learning method called Predictive Text Embedding (PTE) utilizes both semantically labeled and unlabeled data in information networks to learn the embedding of text that produces state of-the-art performance when compared to other embedding methods. However, PTE uses supervised label information to construct one of the networks and many other possible ways of constructing such information networks are left untested. We present two unsupervised methods that can be used in constructing a large scale semantic information network from documents by combining topic models that have emerged as a powerful technique of finding useful structure in an unstructured text collection as it learns distributions over words. The first method uses Latent Dirichlet Allocation (LDA) to build a topic model over text, and constructs a word topic network with edge weights proportional to the word-topic probability distributions. The second method trains an unsupervised neural network to learn the word-document distribution, with a single hidden layer representing a topic distribution. The two weight matrices of the neural net are directly reinterpreted as the edge weights of heterogeneous text networks that can be used to train word embeddings to build an effective low dimensional representation that preserves the semantic closeness of words and documents for NLP tasks. We conduct extensive experiments to evaluate the effectiveness of our methods.
Lossless Color Image Watermarking Based on Lifting Scheme and GWO for Copyright Protection

Due to fast development in digital technologies, numerous gateways are available to alter easily the contents of digital multimedia without noticeable changes by human beings. These features provide breakthrough to copy, store, and transmit digital data and also it has made illegal replication/distribution of digital content. Hence, digital rights management (DRM) is essentially required to solve the issues related to data security and owner’s authentication of multimedia resources and contents. In this paper, lossless color image watermarking scheme based on lifting scheme and grey wolf optimization (GWO) for copyright protection has been proposed. To optimize the strength factor, alpha, GWO has been used in proposed work. With optimized strength factor, the insertion of color watermark has been done into the host image by changing the singular values of the host/cover image. Scrambling of watermark has also been carried out by Arnold transform to provide more security and robustness. Performance analysis of presented work has been verified both quantitatively and qualitatively by using different fidelity parameters like, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, and normalized correlation coefficient (NCC). To prove the strength, the present work has also been compared with considered image watermarking techniques.
Differentially Private Frequent Itemset Mining Against Incremental Updates

Differential privacy has recently been applied to frequent itemset mining (FIM). Most existing works focus on promoting result utility while satisfying differential privacy. However, they all focus on “one-shot” release of a static dataset, which do not adequately address the increasing need for up-to-date sensitive information. In this paper, we address the problem of differentially private FIM for dynamic datasets, and propose a scheme against infinite incremental updates which satisfies $$\epsilon $$ -differential privacy in any sliding window. To reduce the increasing perturbation error against incremental updates, we design an adaptive budget allocation scheme combining with transactional dataset change. To reduce the high sensitivity of one-shot release, we split long transactions and analyze its information loss. Then we privately compute the approximate number of frequent itemsets. Based on the above results, we design a threshold exponential mechanism to privately release frequent itemsets. Through formal privacy analysis, we show that our scheme satisfies $$\epsilon $$ -differential privacy in any sliding window. Extensive experiment results on real-world datasets illustrate that our scheme achieves high utility and efficiency.
Features of teaching a foreign language in the digital educational environment of the University

The article examines the impact of the digitalization process on the creation and use of modern electronic tools and technologies for teaching a foreign language in higher education. The article presents the main electronic tools and technologies that are used in the Financial University during the educational process of teaching a foreign language. The author notes the effectiveness of the implementation of the personality-oriented approach, which is provided by individualization and differentiation of training using the Bank of test tasks created by university teachers, and the electronic educational platform Rosetta Stone Advanced. The use of electronic learning tools and digital technologies allows to develop self-organization of the student. Changing the role of the teacher to the role of the manager of educational activities contributes to the formation of the ability to constant self-studying the student throughout his professional and social life. The effectiveness of the electronic learning tools use is proved by the high level of students’ foreign language competence and their assessment of the foreign language teachers’ pedagogical activity with the results of the survey "Students’ view on teachers".
Evolución de los Materiales Didácticos en la Enseñanza de una Lengua Extranjera: La Conversión del Profesor Analógico al Docente Digital

Through the history of teaching materials, it can be verified how the professional profile of foreign language teachers have transformed to adapt to the needs of every historical moment. In this last decades, this transformation has occurred due to the presence of technological education in the educational space. An adaptation that involves the adoption of the new digital environment, and the ability to combine traditional materials with digital materials. In this respect, the teacher is emerging as a professional capable of using digital technologies in their teaching rituals and, therefore, able to receive inside the pedagogical practices the use of teaching materials in digital support. In this way, this work aims to present the pedagogical, methodological and technological evolution in the field of didactics of foreign languages in order to reflect on the change of paradigm and guide the teacher in his role as a creator of digital materials.
THE IMPLEMENTATION OF STORYTELLING TECHNIQUE USED BY THE ENGLISH TEACHER IN TEACHING SPEAKING (A QUALITATIVE RESEARCH AT SMKN 1 BANDA ACEH)

Abstract Name : Maura Safira Student No. : 1506102020008 Study Program : English Education Department Title : The Implementation of Storytelling Technique Used by The English Teacher in Teaching Speaking (A Qualitative Research in SMKN 1 Banda Aceh) Keywords: Speaking, Storytelling Technique, English Teaching This study aims at analyzing the way the teacher develops students speaking performance by using storytelling technique and observing the steps used by the teacher in teaching English. The researcher applied qualitative study by observing classroom teaching practice and interviewing an English teacher at SMKN 1 Banda Aceh. The results of the observations and interview are consistent with the storytelling procedure proposed by Jianing (2007), which are formulated and ordered as deciding the material, dividing students into some groups, assessing the students‟ performance and providing them with some feedback at the end of the meeting. The teacher perceived that using storytelling technique in teaching speaking make students to gain more interest in the English classroom because she used storytelling techniques in teaching legend topic. Furthermore, through interview it was revealed that storytelling was believed to have increased student‟s vocabulary, confidence and a sense of pleasure in learning English.
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation

Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy. Traditional methods for face de-identification wipe out facial information entirely, making it impossible to analyze facial behavior. Recent advancements on whole-body keypoints detection also rely on facial input to estimate body keypoints. Both facial and body keypoints are critical in some medical diagnoses, and keypoints invariability after de-identification is of great importance. Here, we propose a solution using deepfake technology, the face swapping technique. While this swapping method has been criticized for invading privacy and portraiture right, it could conversely protect privacy in medical video: patients' faces could be swapped to a proper target face and become unrecognizable. However, it remained an open question that to what extent the swapping de-identification method could affect the automatic detection of body keypoints. In this study, we apply deepfake technology to Parkinson's disease examination videos to de-identify subjects, and quantitatively show that: face-swapping as a de-identification approach is reliable, and it keeps the keypoints almost invariant, significantly better than traditional methods. This study proposes a pipeline for video de-identification and keypoint preservation, clearing up some ethical restrictions for medical data sharing. This work could make open-source high quality medical video datasets more feasible and promote future medical research that benefits our society.
On non-intrusive prediction of activities and behavior

We have developed a smart home system that estimates people’s positions, activities and behavior using pressure sensors. The main difference between our research and state-of-the-art in the field is the use of a reduced number of pressure sensors to allow for a larger sensing area. An Internet of Things (IOT) platform in hand with Machine Learning (ML) algorithms are used to route signals and process them in the Cloud to predict activities and behavior and infer whether they are usual or not.
Cloud-Based Blockchaining for Enhanced Security

With the enormous growth of social technology, cloud-based access is gained more attention. The lack of security is a main problem for accessing the data. To avoid this security issues, cloud-based authentication is done with the help of blockchaining.
Digital Transformation of Springfield Police Force

This chapter looks at Digital Transformation of a police force. First its mission is described. Then, its Digital Transformation Initiative is outlined. Dimensions of this Initiative include Self-Service Police Portal; Integrated Regional/National Policing; High-Tech Connected Frontline Policing; Collaborative Community Policing; Police Real-time Online Picture; Digital Case Files; Remote Monitoring; Predictive Resourcing and Scheduling Analytics; and Deterrence and Investigation Analytics. Finally, next steps are addressed.
New culinary trends based on the most popular Instagram accounts

Besides being known for its mountains and beaches, Garut district is known for its culinary diversity. The purpose of this study was to identify Garut’s culinary products that receive the attention of citizens while being able to be used as a new icon of Garut’s typical culinary business. The @jajanangarut Instagram account, as the center of information and review of Garut culinary types and centers, was chosen as the research subject. This research primarily used qualitative content analysis. By paying attention to 9,400 posts as of June 2018, the data were then filtered into 3,624 posts in the observation period January 2018–June 2019. The results of the study found there are 10 categories of Garut culinary types, where snacks are the most popular, with 1,178 posts, followed bynoodles and meatballs.
Considering the Blackbox: An Investigation of Optimization Techniques with Completely Balanced Datasets of Packet Traffic

One of the primary goals in machine learning with neural networks is to reduce training time while simultaneously maintaining or improving accuracy. To this end a variety of optimization techniques have been developed and implemented. For the researcher focusing on particular datasets it is not always clear what works best for a given task. Dataset construction can also have a dramatic effect on results. This paper studies optimization algorithms with completely balanced packet traffic datasets and provides performance criteria for training time and accuracy. The importance in using completely balanced datasets is discussed and demonstrated. We show that though several algorithms can achieve near 99% accuracy while keeping training times to less than one minute, there is wide variation and unpredictability in some. The investigation was completed using 65K training samples, a 6. 5K validation set and four test datasets. Finally we examine varying neural network structures that might improve an algorithms performance and find that wider or deeper networks are often more of a problem than help.
Influence of Viewing Position by Changing Display Resolution and Display Size

In our previous study, we measured head movement and eye movements by changing viewing position and distance. Our results showed that mean gaze point position depends on video contents, viewing position and viewing distance. However, to the best of our knowledge, no research has yet been done on the line of sight in the case of showing a video of different resolutions and sizes at the same viewing distance. This is different from the general conditions in image quality evaluation, but we often watch videos under this condition in daily life. Therefore, we measured mean gaze point position when screen resolution and size are changed, finding that, if resolution was changed, only different viewing positions affected the position of the mean gaze point. Except in this case, there was no change in mean gaze point position and its standard deviation due to resolution or screen size.
The Princess and the Earthquakes

A fairy tale: Can the candidate fulfil the task given by the Prince?Using historic documents to estimate earthquake frequencies, typical earthquake depths in the Upper Rhine Graben, Relation of earthquake magnitude, macroseismic intensity, and hypocentral depth.