Reviewerly/exHarmony huggingface dataset:
Generative adversarial network for video anomaly detection

Video anomaly detection has gained wide popularity in the current intelligent surveillance systems. Most works have struggled with tasks such as detecting and localizing object tasks in crowded scenes, especially at the pixel level. Generative adversarial network (GAN) is an effective approach for image generation and data augmentation. In this chapter, we comprehensively explore GAN on its structure and procedures of anomaly detection research trends and introduce our proposed deep spatiotemporal translation network (DSTN), a novel unsupervised anomaly detection and localization method. As an unsupervised manner, we train only the frames of normal events to obtain their motion information. The unknown events are detected as abnormalities during testing since the model has only prior knowledge of normal events. Our experimental results outperform other state-of-the-art works respecting the frame level, pixel level, and time complexity. Besides, the performance of GAN is discussed along with its advantages and limitations for video anomaly detection.
Leveraging Audio Gestalt to Predict Media Memorability.

Memorability determines what evanesces into emptiness, and what worms its way into the deepest furrows of our minds. It is the key to curating more meaningful media content as we wade through daily digital torrents. The Predicting Media Memorability task in MediaEval 2020 aims to address the question of media memorability by setting the task of automatically predicting video memorability. Our approach is a multimodal deep learning-based late fusion that combines visual, semantic, and auditory features. We used audio gestalt to estimate the influence of the audio modality on overall video memorability, and accordingly inform which combination of features would best predict a given video's memorability scores.
PENGARUH MODEL PEMBELAJARAN SQ4R(SURVEY, READ, REFLECT, RECITE, REVIEW) TERHADAP HASIL BELAJAR EKONOMI SISWA KELAS X PENGARUH MODEL PEMBELAJARAN SQ4R(SURVEY, READ, REFLECT, RECITE, REVIEW) TERHADAP HASIL BELAJAR EKONOMI SISWA KELAS X

The problems that exist in economic learning in schools are the use of learning methods that are not yet appropriate so that students are easily bored during the economic learning process. The purpose of this study is to find out whether there is influence of the learning model Survey, Question, Read, Reflect, Recite, Review (SQ4R) on economic learning outcomes in class X Bandar Lampung 17 Public High School Academic Year 2018/2019. In this study the authors used the experimental method that is using the learning model Survey, Question, Read, Reflect, Recite, Review (SQ4R). The population in this study were students of class X IPS in the even semester of Bandar Lampung High School 17 which numbered 89 students. Where the sample consisted of 2 classes, namely X IPS 1, which amounted to 29 as the experimental class and X IPS with a total of 29 students as the control class. Based on the results of testing the hypothesis conducted by the author, it can be thit = 3.37 by looking at the test criteria with a level of 5% obtained tdaf = 2.00 so thit> tdaf so H0 is rejected, meaning Ha is accepted or there is the influence of the application of SQ4R learning method (Survey , Question, Read, Reflect, Recite, Review) on the economic learning outcomes of students of class X Even Semester 17 Bandar Lampung High School in Academic Year 2018/2019.
Geo-Phy-What?!

Abstract Summary GEO-PHY-WHAT?! is a place where adults and children can learn about geophysics, experiment with it and discover what you can use it for.
Soft skills as one of the components of the professional competence of a modern designer

This article provides a soft skills detailed analysis. This group of skills, and the paper will prove that, represents one of the modern designer’s professional skills main components. The «soft skills» concept essence and structural components are revealed. The following categories of soft skills were studied: key emotional qualities of a person, skills of managing their own emotions, skills of interaction with the outside world and in society, as well as skills formed by individual characteristics of human thinking. The non-technical skills importance in the designer study is determined, their formation process is also studied. Next, the content of «soft skills», required for the formation of undergraduate students, who master the direction of training 54.03.01 «Design», is determined. In this article, in addition, the competencies provided for by the Federal State Educational Standard for Higher Education, which are directly related to the process of forming such skills in students of the «Design» direction are examined. Within the framework of this article, the analysis of the modern, dynamically developing system of the higher education is carried out. On its basis, the necessity of introducing such structural and organizational elements into the training process of future bachelors, which will most likely contribute to the soft skills formation is proved. The role of the higher school teacher in the process of the designer’s soft skills forming is also defined. The soft skills of the future design bachelor are considered by the author not only as a set of knowledge, skills and abilities, but also the sum of personal qualities based on tolerant, humanistic, ethical principles. According to the author, the human personality of the future designer finds the most complete expression at the level of professional and social culture. The article proves the necessity of applying a system-value approach in the process of preparing bachelor’s degrees in design. In turn, one of the most significant components of this approach is soft skills.
Semantic Search Pipeline: From Query Expansion to Concept Forging

When searching a database for a topic (e.g. Covid-19), there may not exist a precise match, especially if the topic is novel. Furthermore, the topic may surface in the data under different guises (`Covid-19,' `coronavirus,' `pandemic', etc.). The results of a keyword search are limited by the querier's imagination and familiarity with the data. Such searches have high precision, but low recall. In order to increase the recall of searches, we present the Semantic Search Pipeline, a novel approach to document retrieval that uses distributional semantic models and locality sensitive hashing to expand queries and efficiently identify other relevant documents that may not contain the obvious query terms. We evaluate the pipeline using a dataset curated from financial customer service call centers, resulting in an increase in recall of 32% over a simple keyword baseline, with a negligible drop in precision. Furthermore, we present the notion of concept forging, a process of tracing a topic or concept through time and through its various surface realizations. Applied to Covid-19, the search pipeline retrieves a set of documents that allow us to uncover the short- and long-term effects of Covid-19 on the lives of the people and businesses impacted by it. Although Covid-19 is a timely test case, our search pipeline is general in nature and can be easily applied to any range of topics.
Teaching Principles of Interactive Sound

This chapter takes a practice-based approach, investigating the techniques used to teach Level 5 (second year of the UK University system) music students across two degrees and providing a framework for interactive audio analysis from a practitioner’s perspective. The chapter is divided into two sections: the first section outlines the practical and creative challenges for composers, sound designers, and musicologists when approaching interactive audio. These challenges are related to current theories of game design and ludomusicology. The second half provides practice-based solutions to teaching and analyzing interactive audio from the composer and sound designer’s perspective.
THE IMPACT OF SMART PHONES ON ENGLISH LANGUAGE LEARNING: PERCEPTIONS OF EFL UNDERGRADUATES STUDENTS IN STKIP PGRI TULUNGAGUNG

Researchers across the world are examining the educational value and effectiveness of integrating the latest electronic gadgets with teaching-learning activities in the classroom. In spite of the availability of latest electronic gadgets like iPods, tablets, and smartwatches, researchers are more interested in the educational value of the Smart phones for the teaching-learning of English. In a technologically advanced country like Indonesia, the third and fourth generation (3G, 4G) smart phones devices are available at affordable prices, and people of all streams find it necessary to own a smart phone for connecting with their friends and relatives. Moreover, it has become a common trend among undergraduate students to carry a smart phone to the classroom as well. The present study examines the impact of smart phones on studentsâ?? English language learning. A field study was conducted on fifty-two undergraduate male students majoring in English language and literature at STKIP PGRI Tulungagung. The methodology of data collection included a self-report for students and a teacher questionnaire. Findings of the study are significant for EFL teachers and researchers for introducing innovative methods and resourceful materials for the English classroom.
On Securing Electronic Healthcare Records Using Hyperledger Fabric Across the Network Edge

Over the last few years, there is a growing need for researchers to study various accessible methods to develop Electronic Health Records in order to help physicians access patient data while maintaining privacy of the health data. However, there lie several challenges due to the accessibility of various Internet of Things sensors and wearables that collect user’s physiological and mental health data. Therefore, it is crucial to secure such data in the healthcare ecosystem so that it is not retrievable by third parties. In this vein, we have used the concepts of blockchain to provide patient control of their own data and the liberty to choose the specific medical caregivers they want to share their data with. We have implemented a large-scale information architecture to access EHRs based on hyperledger fabric model. Setting distributed ledger of the blockchain node can be used for setting up central network of the edge computing that could be visualized as the vectorized decentralized edge computing. We have framed data privacy and accessibility issues in healthcare and proposed an integrated blockchain-based architecture. This chapter describes an approach to address limitations such as permissioned blockchain that uses off-chain storage of the data assets, and this is accessed through a standard browser and mobile app. There are three main components to the proposed system: a blockchain-based access control module, off-chain data storage and a role-centered web user interface. The implementation in the hyperledger framework is described as an example use of patient-centered health data management.
Automatic adjustment method of sports equipment parameters based on dynamic data drive

In order to overcome the problems of low sensitivity, large adjustment deviation and low adjustment efficiency existing in the traditional parameter adjustment method of sports fitness equipment, this paper proposes a new automatic parameter adjustment method of squatting fitness equipment based on dynamic data drive. This method analyses the basic elements of sports fitness equipment, and obtains the relevant parameters of squatting fitness equipment. A multi-objective optimisation model is established to optimise the support probability and utilisation rate of spare parts. According to the optimisation results, Kalman filter method is used to fuse the equipment parameters, and the method of random gradient descent and dynamic data-driven is used to adjust the fused parameters. The experimental results show that it has obvious advantages in equipment sensitivity, parameter adjustment deviation and adjustment efficiency. After adjustment, the highest sensitivity of equipment is close to 0.9.
Prediction problems inspired by animal learning

We present three problems modeled after animal learning experiments designedto test online state construction or representation learning algorithms. Ourtest problems require the learning system to construct compact summaries oftheir past interaction with the world in order to predict the future, updatingonline and incrementally on each time step without an explicit training-testingsplit. The majority of recent work in Deep Reinforcement Learning focuses oneither fully observable tasks, or games where stacking a handful of recentframes is sufficient for good performance. Current benchmarks used forevaluating memory and recurrent learning make use of 3D visual environments(e.g., DeepMind Lab) which require billions of training samples, complex agentarchitectures, and cloud-scale compute. These domains are thus not well suitedfor rapid prototyping, hyper-parameter study, or extensive replication study.In this paper, we contribute a set of test problems and benchmark results tofill this gap. Our test problems are designed to be the simplest instantiationand test of learning capabilities which animals readily exhibit, including (1)trace conditioning (remembering a cue in order to predict another far in thefuture), (2) patterning (a particular combination of cues predict another), (3)and combinations of both with additional non-relevant distracting signals. Weprovide baselines for each problem including heuristics from the early days ofneural network learning and simple ideas inspired by computational models ofanimal learning. Our results highlight the difficulty of our test problems foronline recurrent learning systems and how the agent's performance oftenexhibits substantial sensitivity to the choice of key problem and agentparameters.
Efforts To Improve History Learning Activity And Results Through Quantum Teaching Model With Melody Stick Media

This study aims to improve the activeness and learning outcomes of students through the application of the Quantum Teaching learning model with the Melody Stick media.This re­search is a Classroom Action Research (CAR). The study was conducted in two cycles consisting. The research subjects were class XI IPS 4 of SMA Negeri 1 Karanganyar, with a total of 36 students. The results showed that: the application of the Quantum Teaching learning model with the Melody Stick media could increase the activeness of students. This is evidenced by the in­crease in the average achievement indicator of activeness from 60.12% in the pre-cycle, to 70.71% in the first cycle, and 83.27% in the second cycle. the application of the Quantum Teaching learn­ing model with the Melody Stick media can improve the learning outcomes of students. This is evidenced by the increase in the percentage of students completing history learning, namely 41.67% in the pre-cycle, increasing to 63.89% in the first cycle, and 83.33% in the Based on the results of the study it can be concluded that the application of the Quantum Teaching learning model with the Melody Stick media can increase the activeness and learning outcomes
Facilitating in-situ shared use of IoT actuators in public spaces

Public spaces, where we gather, commune, and take a rest, are the essential parts of a modern urban landscape, enriching citizen's everyday life [3]. How we share these spaces are considered an indicator of the quality of life. Public spaces thus have a responsibility to provide comfort and satisfaction to any visitors. However, in most times, the operations of the spaces are managed in rather an exclusive manner.
Leading for Development? (II)

Chapters 8 complements Chapter 7 by assessing the developmental implications of leadership changes in Africa through an extensive empirical analysis. It examines the impact of leaders’ duration in power and of the diverse modes of leadership transfers on the provision of social welfare, state consolidation and control of corruption. It is postulated that electoral competition and alternation in office – even when they fall short of genuine democracy – help African citizens improve the accountability of their leaders, at least to some extent. The risk of being removed from power generates incentives to provide public goods for incumbents who want to maximize their reelection chances. At the same time, elections help opposition parties monitor the behavior of rulers and expose wrongdoings and maladministration. We advance several specific hypotheses on the effect that leaders can have on social progress as well as on a better and less corrupt functioning of state apparatuses. We test these hypotheses empirically using a time-series and cross-sectional research design that includes all the 49 countries of the sub-Saharan region between 1960 and 2018. Much of the evidence confirms our underlying argument about African development, political leaders, and the modes in which they rotate in office matter
PDE-based time-dependent model for image restoration with forward-backward diffusivity

In the image processing community, image denoising and deblurring, data compression, edge detection, etc. are the most challenging and basic hurdles. We address a new nonlinear anisotropic diffusion model for image denoising and deblurring. To judge our model, we compared the existing model to obtain good results. To discretize for both nonlinear anisotropic diffusion models, we have used an explicit scheme and numerical experimental results are discussed.
Subset-Saturated Transition Cost Partitioning

Cost partitioning admissibly combines the information from multiple heuristics for optimal state-space search. One of the strongest cost partitioning algorithms is saturated cost partitioning. It considers the heuristics in sequence and assigns to each heuristic the minimal fraction of the remaining costs that are needed for preserving all heuristic estimates. Saturated cost partitioning has recently been generalized in two directions: first, by allowing to use different costs for the transitions induced by the same operator, and second, by preserving the heuristic estimates for only a subset of states. In this work, we unify these two generalizations and show that the resulting subset-saturated transition cost partitioning algorithm usually yields stronger heuristics than the two generalizations by themselves.
On the Influence of Machine Translation on Language Origin Obfuscation

In the last decade, machine translation has become a popular means to deal with multilingual digital content. By providing higher quality translations, obfuscating the source language of a text becomes more attractive. In this paper, we analyze the ability to detect the source language from the translated output of two widely used commercial machine translation systems by utilizing machine-learning algorithms with basic textual features like n-grams. Evaluations show that the source language can be reconstructed with high accuracy for documents that contain a sufficient amount of translated text. In addition, we analyze how the document size influences the performance of the prediction, as well as how limiting the set of possible source languages improves the classification accuracy.
Performance Analysis of NoSQL Databases with Large Volumes of Open Educational Data

Non-Relational Databases, also known as NoSQL (Not Only Structured Query Language), emerged in the face of new requirements of Web 2.0 computer applications.Relational databases, although consolidated as a data storage and manipulation model for decades, began to face performance limitations when dealing with large volumes of data.NoSQL databases have flexible data structure, and when associated with distributed computing provide a good scalability, being indicated in the Big Data scenario.In this context, this work evaluates the performance of three NoSQL databases, in order to verify their performance in large volumes of educational data.The experiments were performed with school census data, available in the repository of the Ansio Teixeira National Institute for Educational Studies and Research (INEP) in Brazil.For this case of study, the following databases were adopted: DynamoDB (whose data model is key-value oriented), MongoDB (whose data model is document-oriented), and Cassandra (whose data model is column-oriented).Therefore, among the investigated databases, MongoDB was more efficient, presenting lower processing times in the operations of inserts/loads, queries, updates, and removals of basic educational data.
Loop Estimator for Discounted Values in Markov Reward Processes

At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process. We propose a simple and efficient estimator called loop estimator that exploits the regenerative structure of Markov reward processes without explicitly estimating a full model. Our method enjoys a space complexity of O(1) when estimating the value of a single positive recurrent state s unlike TD with O(S) or model-based methods with O(S^2). Moreover, the regenerative structure enables us to show, without relying on the generative model approach, that the estimator has an instance-dependent convergence rate of O~(\sqrt{\tau_s/T}) over steps T on a single sample path, where \tau_s is the maximal expected hitting time to state s. In preliminary numerical experiments, the loop estimator outperforms model-free methods, such as TD(k), and is competitive with the model-based estimator.
Estimating Visual Saliency for Omnidirectional HDR Images

A unified decomposition-and-integration-based framework is presented herein for the visual saliency estimation of omnidirectional high dynamic range (HDR) images, which allows straightforward reuse of existing saliency estimation method for typical images with narrow field-of-view and low dynamic range (LDR). First, the proposed method decomposes a given omnidirectional HDR image into multiple partially overlapping LDR images with quasi-uniform spatial resolution and without polar singularities, both spatially and in intensity using a spherical overset grid and a tone-mapping-based synthesis of imaginary multiexposure images. For each decomposed image, a standard saliency estimation method is then applied for typical images. Finally, the saliency map of each decomposed image is optimally integrated from the coordinate system of the overset grid and LDR back to the representation of the coordinate system and HDR of the original image. The proposed method is applied to actual omnidirectional HDR images and its effectiveness is demonstrated.
A Note About Claw Function with a Small Range.

In the claw detection problem we are given two functions $f:D\rightarrow R$ and $g:D\rightarrow R$ ($|D|=n$, $|R|=k$), and we have to determine if there is exist $x,y\in D$ such that $f(x)=g(y)$. We show that the quantum query complexity of this problem is between $\Omega\left(n^{1/2}k^{1/6}\right)$ and $O\left(n^{1/2+\varepsilon}k^{1/4}\right)$ when $2\leq k<n$.
Locks, Latches, and Mutexes

In this chapter, we’ll take a detailed look at how Oracle locks both data (e.g., rows in tables) and shared data structures (such as those found in the SGA). We’ll investigate the granularity to which Oracle locks data and what that means to you, the developer. When appropriate, I’ll contrast Oracle’s locking scheme with other popular implementations, mostly to dispel the myth that row-level locking adds overhead; in reality, it adds overhead only if the implementation adds overhead. In the next chapter, we’ll continue this discussion and investigate Oracle’s multiversioning techniques and how locking strategies interact with them.