Reviewerly/exHarmony huggingface dataset:
Debating Education: Is There a Role for Markets?

Educational TheoryVolume 70, Issue 1 p. 105-112 BOOK REVIEW Debating Education: Is There a Role for Markets? Reviewed by Ashley Berner, Ashley Berner Johns Hopkins UniversitySearch for more papers by this author Reviewed by Ashley Berner, Ashley Berner Johns Hopkins UniversitySearch for more papers by this author First published: 16 June 2020 https://doi.org/10.1111/edth.12407Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume70, Issue1Special Issue: Symposium: Education and RiskFebruary 2020Pages 105-112 RelatedInformation
Opponent Modelling with Local Information Variational Autoencoders.

Modelling the behaviours of other agents (opponents) is essential for understanding how agents interact and making effective decisions. Existing methods for opponent modelling commonly assume knowledge of the local observations and chosen actions of the modelled opponents, which can significantly limit their applicability. We propose a new modelling technique based on variational autoencoders which uses only the local observations of the agent under control: its observed world state, chosen actions, and received rewards. The model is jointly trained with the agent's decision policy using deep reinforcement learning techniques. We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks, showing that our method achieves significantly higher returns than a baseline method which does not use opponent modelling, and comparable performance to an ideal baseline which has full access to opponent information.
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling

Uncertainty estimation for machine learning models is of high importance in many scenarios such as constructing the confidence intervals for model predictions and detection of out-of-distribution or adversarially generated points. In this work, we show that modifying the sampling distributions for dropout layers in neural networks improves the quality of uncertainty estimation. Our main idea consists of two main steps: computing data-driven correlations between neurons and generating samples, which include maximally diverse neurons. In a series of experiments on simulated and real-world data, we demonstrate that the diversification via determinantal point processes-based sampling achieves state-of-the-art results in uncertainty estimation for regression and classification tasks. An important feature of our approach is that it does not require any modification to the models or training procedures, allowing straightforward application to any deep learning model with dropout layers.
Design of Framework for Disaster Recovery in Cloud Computing

Cloud computing is the most widely used technique which is being used by IT organizations worldwide, and it was a major disruption in IT industry and changed the landscape of the entire industry when it came into the picture as it changed the way how software was developed and distributed among their users. Cloud computing enables us to use remote resources such as network, compute, and storage over the Internet without the need to acquire physical hardware such as network cables, and servers which was the case before cloud computing came out as a concept where companies had their own on-premise data centers. Basically, cloud computing is also powered by data centers that contain a collection of several servers/systems, and systems are prone to failure which can be due to several reasons such as natural calamities, network failures, and famines which can be termed as a disaster. Disaster recovery is a technique which is used to recover the data and applications and perform a failover in such a way that the users experience the least possible downtime as well as maximum data is recovered while performing the failover. This research paper focuses on crafting an ideal solution for handling disaster recovery in cloud with minimum possible downtime for containerized microservices.
Authorship Recognition with Short-Text using Graph-based Techniques

In recent years, studies of authorship recognition has aroused great interest in graph-based analysis. Modeling the writing style of each author using a network of co-occurrence words. However, short texts can generate some changes in the topology of network that cause impact on techniques of feature extraction based on graph topology. In this work, we evaluate the robustness of global-strategy and local-strategy based on complex network measurements comparing with graph2vec a graph embedding technique based on skip-gram model. The experiment consists of evaluating how each modification in the length of text affects the accuracy of authorship recognition on both techniques using cross-validation and machine learning techniques.
Deterministic Decoding for Discrete Data in Variational Autoencoders

Variational autoencoders are prominent generative models for modeling discrete data. However, with flexible decoders, they tend to ignore the latent codes. In this paper, we study a VAE model with a deterministic decoder (DD-VAE) for sequential data that selects the highest-scoring tokens instead of sampling. Deterministic decoding solely relies on latent codes as the only way to produce diverse objects, which improves the structure of the learned manifold. To implement DD-VAE, we propose a new class of bounded support proposal distributions and derive Kullback-Leibler divergence for Gaussian and uniform priors. We also study a continuous relaxation of deterministic decoding objective function and analyze the relation of reconstruction accuracy and relaxation parameters. We demonstrate the performance of DD-VAE on multiple datasets, including molecular generation and optimization problems.
Pengaruh Model Pembelajaran Word Square Berbasis Tri Kaya Parisudha Terhadap Hasil Belajar IPA

This study aimed at investigating whether or not there was a significant effect on science learning achievement of students who were taught by using Word Square learning model based on Tri Kaya Parisudha and who were not taught by using Word Square learning model based on Tri Kaya Parisudha in the fifth grade students at SDN Gugus 6 Sutasoma Kubutambahan Sub-District in accademic year 2017/2018. This study was a quasi-experimental research. The population of this study was the 141 students of fifth grade students at SDN Gugus 6 Sutasoma Kubutambahan Sub-district. The research sample were 14 students of the fifth grade students at SD Negeri 5 Tajun and 16 students of the fifth grade students in SD Negeri 6 Tajun. The data were collected by using test method, the instrument was science learning achievement test and used in the form of multiple choice test. The obtained data were analyzed by using descriptive statistical analysis techniques and inferential statistical analysis which was t-test. The average value of the two classes, namely the experimental group was 23.64 while the control group was 16.25. The result of data analysis showed that thitung = 5.28&gt; ttable = 2.048. Based on the results of this study, it can be concluded that there was an effect of Word Square-learning model based on Tri Kaya Parisudha on students’ science learning achievement of the fifth grade students at SDN Gugus 6 Sutasoma Kubutambahan Sub-district in academic year 2017/2018.
Extrapolation Algorithm of Ship Track Smoothing Based on Spline Theory

Zhao, X. and Song, X., 2020. Extrapolation algorithm of ship track smoothing based on spline theory. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 909–912. Coconut Creek (Florida), ISSN 0749-0208.Due to the poor transition effect of traditional ship track control, the shipping pressure is too large. The research on extrapolation algorithm of ship track smoothing based on spline theory is proposed. Based on the principle of spline fitting, a three degree of freedom motion model of ship's horizontal plane is established to analyze the force on the ship in the process of motion, so the motion equation is obtained. The extrapolation algorithm is used to analyze the intersection of wave gates. Considering the actual situation and the influence parameters, the research on extrapolation algorithm of ship track smoothing based on spline theory is completed. The control experiment is designed to compare the track control effect of the proposed algorithm with that of the traditional algorithm. The results show that the combination of the spline theory and the extrapolation method results in a higher fit between the control curve and the actual route, which is obviously superior to the traditional results, and the control effect of the ship track is optimized.
A NOVEL JOB PORTAL WITH RESUME EVALUATION SYSTEM BASED ON TEXT MINING AND NLP TECHNIQUES

In this serious time, the instruction among the individuals is expanding to such an extent that the activity for them is currently diminishing. The organizations even needs the individuals who are best in their field. Around then, it turns out to be elusive the individuals who are capable enough to get recruited. The work for the organizations likewise increment to locate the right individuals who can satisfy their interest. Concerning these issues, someone may consider the procedure which could undoubtedly deal with this procedure and makes the assignment less mind boggling. Along these lines utilizing procedures including web creeping, content mining, and NLP, this examination built up a productive framework that matches work searchers with the enrollment specialists. In this task, we propose work searcher and boss login. In this we make the enrollment procedure straightforward and effective by incorporating web slithering, content mining, and normal language handling strategies. In this we make the enrollment procedure basic and productive by giving a choice of transferring study materials of the aptitude required during work post. Additionally we give programmed suggestion to the activity searcher when individual abilities are coordinated with the activity post. This is executed utilizing cooperative separating calculation. Additionally to enhance the extra room for the specialist co-op, we propose de-duplication procedure keeping away from replication of documents. For de-duplication strategy, we utilize Ҏroactive Ȓeplica Ċhecking ąpproach (PRCR). Additionally applying regular preparing strategies in both occupation searcher and manager side gives effective outcomes sparing quite a bit of time. In the business side, we use web crawler to extricate set of working responsibilities and prerequisites. In the activity searcher side, when the resume is posted, stop word separating and message division is performed. After content division, the scoring is give dependent on the training, work understanding, abilities, character attributes and recurrence of degree. At last our proposed framework gives a suggestion framework to the up and coming age in which level of instruction significant occupation prerequisites are coming.
Future of Mobile Learning During and After Global (Covid-19) Pandemic: College of Basic Education as Case

The declaration of world health organization (WHO) on March 11, 2020 that the new coronavirus (COVID-19) is a global pandemic has urged countries around the world to impose severe restrictions on their populations in a bid to stop the spread of infection.Imposing social distancing policy has consequence to stop different sectors from daily operations and education one of these sectors.This movement has consequence to accelerate the need for alternative.Mobile learning known as the ability to provide educational content on personal pocket devices, or personal digital assistants (PDAs).This style of learning is in its early stages in terms of both pedagogies and its technologies.M-learning represents a new era in learning and educational development in terms of enhancing the educational outcomes and offering mobile training for employees who are busy off campus.The opposite argument here is that this approach of learning might not be sufficient to fulfill the educational process requirements.Aim of this study is to assess from students points of view, current applications of m-learning in Kuwait's College of Basic Education and evaluate the potential of this approach.Sample of the study consist of fifty-two students (30 male and 22 female) determined by a purposive sampling method.Collected data via online questionnaire to assess the participant's perceptions and the analysis has revealed a positive attitude towards the utilization of m-learning in higher education; the average perception of was above average, with a mean of 3.54 out of 5.00.No statistical differences were found reflecting students' gender (female/ male) or their level of experience with information and communications technology (weak, above average, or advanced).As a result, the study recommends accelerating the development of courses taught and incorporating mobile learning delivery.Adopting a blended learning strategy based on m-learning can motivate the students' learning and reduce the fears of adopting mobile technology within the curriculum.
Befriending The Byzantines Through Reputation Scores.

We propose two novel stochastic gradient descent algorithms, ByGARS and ByGARS++, for distributed machine learning in the presence of Byzantine adversaries. In these algorithms, reputation score of workers are computed using an auxiliary dataset with a larger stepsize. This reputation score is then used for aggregating the gradients for stochastic gradient descent with a smaller stepsize. We show that using these reputation scores for gradient aggregation is robust to any number of Byzantine adversaries. In contrast to prior works targeting any number of adversaries, we improve the generalization performance by making use of some adversarial workers along with the benign ones. The computational complexity of ByGARS++ is the same as the usual stochastic gradient descent method with only an additional inner product computation. We establish its convergence for strongly convex loss functions and demonstrate the effectiveness of the algorithms for non-convex learning problems using MNIST and CIFAR-10 datasets.
On Extensions of Maximal Repeats in Compressed Strings

This paper provides upper bounds for several subsets of maximal repeats and maximal pairs in compressed strings and also presents a formerly unknown relationship between maximal pairs and the run-length Burrows-Wheeler transform.This relationship is used to obtain a different proof for the Burrows-Wheeler conjecture which has recently been proven by Kempa and Kociumaka in Resolution of the Burrows-Wheeler Transform Conjecture.More formally, this paper proves that the run-length Burrows-Wheeler transform of a string S with z_S LZ77-factors has at most 73(log₂ |S|)(z_S+2)² runs, and if S does not contain q-th powers, the number of arcs in the compacted directed acyclic word graph of S is bounded from above by 18q(1+log_q |S|)(z_S+2)².
Proximity Gaps for Reed–Solomon Codes

A collection of sets displays a proximity gap with respect to some property if for every set in the collection, either (i) all members are δ-close to the property in relative Hamming distance or (ii) only a tiny fraction of members are δ-close to the property. In particular, no set in the collection has roughly half of its members δ-close to the property and the others δ-far from it. We show that the collection of affine spaces displays a proximity gap with respect to Reed–Solomon (RS) codes, even over small fields, of size polynomial in the dimension of the code, and the gap applies to any δ smaller than the Johnson/Guruswami–Sudan list-decoding bound of the RS code. We also show near-optimal gap results, over fields of (at least) linear size in the RS code dimension, for δ smaller than the unique decoding radius. Concretely, if δ is smaller than half the minimal distance of an RS code V ⊂ 𝔽 q n , then every affine space is either entirely δ-close to the code or, alternatively, at most an ( n/q )-fraction of it is δ-close to the code. Finally, we discuss several applications of our proximity gap results to distributed storage, multi-party cryptographic protocols, and concretely efficient proof systems. We prove the proximity gap results by analyzing the execution of classical algebraic decoding algorithms for Reed–Solomon codes (due to Berlekamp–Welch and Guruswami–Sudan) on a formal element of an affine space. This involves working with Reed–Solomon codes whose base field is an (infinite) rational function field. Our proofs are obtained by developing an extension (to function fields) of a strategy of Arora and Sudan for analyzing low-degree tests.
Beginning Node.JS

We now have an understanding of what REST APIs are, and it’s time to understand Node.js in a larger context. This chapter covers what Node.js is. We take a brief look at Node.js and differentiate it from Node. The two are often confused or interchangeably used. We start by understanding the popularity of Node.js and how it became the go-to point for REST APIs. If you're a solution architect trying to understand which tech stack to use, this could be the chapter you want to read, considering hot topics like which stack to use for mobile app backend development. We end by looking at the possible downside of using Node.js without a framework and a very brief description of Node.js modules.
PSI-Stats: Private Set Intersection Protocols Supporting Secure Statistical Functions

Private Set Intersection (PSI) enables two parties, each holding a private set to securely compute their intersection without revealing other information. This paper considers settings of secure statistical computations over PSI, where both parties hold sets containing identifiers with one of the parties having an additional positive integer value associated with each of the identifiers in her set. The main objective is to securely compute some desired statistics of the associated values for which its corresponding identifiers occur in the intersection of the two sets. This is achieved without revealing the identifiers of the set intersection. In this paper, we present protocols which enable the secure computations of statistical functions over PSI, which we collectively termed PSI-Stats. Implementations of our constructions are also carried out based on simulated datasets as well as on actual datasets in the business use cases that we defined, in order to demonstrate practicality of our solution. PSI-Stats incurs $$5\times $$ less monetary cost compared to the current state-of-the-art circuit-based PSI approach due to Pinkas et al. (EUROCRYPT’19). Our solution is more tailored towards business applications where monetary cost is the primary consideration.
The Streaming k-Mismatch Problem: Tradeoffs Between Space and Total Time.

We revisit the $k$-mismatch problem in the streaming model on a pattern of length $m$ and a streaming text of length $n$, both over a size-$\sigma$ alphabet. The current state-of-the-art algorithm for the streaming $k$-mismatch problem, by Clifford et al. [SODA 2019], uses $\tilde O(k)$ space and $\tilde O\big(\sqrt k\big)$ worst-case time per character. The space complexity is known to be (unconditionally) optimal, and the worst-case time per character matches a conditional lower bound. However, there is a gap between the total time cost of the algorithm, which is $\tilde O(n\sqrt k)$, and the fastest known offline algorithm, which costs $\tilde O\big(n + \min\big(\frac{nk}{\sqrt m},\sigma n\big)\big)$ time. Moreover, it is not known whether improvements over the $\tilde O(n\sqrt k)$ total time are possible when using more than $O(k)$ space. We address these gaps by designing a randomized streaming algorithm for the $k$-mismatch problem that, given an integer parameter $k\le s \le m$, uses $\tilde O(s)$ space and costs $\tilde O\big(n+\min\big(\frac {nk^2}m,\frac{nk}{\sqrt s},\frac{\sigma nm}s\big)\big)$ total time. For $s=m$, the total runtime becomes $\tilde O\big(n + \min\big(\frac{nk}{\sqrt m},\sigma n\big)\big)$, which matches the time cost of the fastest offline algorithm. Moreover, the worst-case time cost per character is still $\tilde O\big(\sqrt k\big)$.
Auxiliary navigation system based on Baidu Map JavaScript API for high clearance sprayers

In order to improve the precision of the sprayer field operation and reduce the overlapping rate as well as the missing rate during the spray process, an auxiliary navigation system based on Baidu map JavaScript API (application programming interface) was designed to prevent overlapping spray and missing spray. Technologies such as serial communication, GPS (Global Positioning System) positioning, Baidu map API, path planning and geographic information system were integrated in this system. Accordingly, the functions of vehicle positioning, path planning, job navigation, doodle in operated areas, real-time data collection and automatic processing in spray process could be achieved. In order to verify the reliability and stability of the designed navigation system, an experimental platform was developed for auxiliary navigation system based on the self-developed 3WPG-3000 high clearance self-propelled sprayer. In addition, an RTK GPS and a computer were also integrated in this system. Moreover, experiments of AB line navigation and free line navigation were carried out. The results indicated that the overlapping rate of the AB line navigation and the free line navigation as well as the missing rate of them were 2.54%, 0.26% and 4.43%, 0.88% respectively, which meet the needs of sprayer field operation.
RETRACTED: Performance of news clustering using ant colony optimization

Abstract Humans are basically curious creatures. We always look for all the latest information through books or news. News usually contains tags or categories to make it easier to find similar news. It can be done manually by a human or with a machine to make it easier. Data mining techniques such as clustering and classification can be used to help the categorization process. Clustering is a technique that can be used to grouped news based on their similarity. There is some algorithm that can be used to clustering data. One of them is Ant Colony Optimization (ACO). ACO is a metaheuristic algorithm that mimics how ants bring food to the colony. ACO also can be used to solve the clustering problem. In this paper, we will use the ACO version for clustering to cluster news and find the performance of the said method. Then we compare the result with ground truth.