Reviewerly/exHarmony huggingface dataset:
Two-layer Coded Gradient Aggregation with Straggling Communication Links

In many distributed learning setups such as federated learning, client nodes at the edge use individually collected data to compute the local gradients and send them to a central master server, and the master aggregates the received gradients and broadcasts the aggregation to all clients with which the clients can update the global model. As straggling communication links could severely affect the performance of distributed learning system, Prakash et al. proposed to utilize helper nodes and coding strategy to achieve resiliency against straggling client-to-helpers links. In this paper, we propose two coding schemes: repetition coding (RC) and MDS coding both of which enable the clients to update the global model in the presence of only helpers but without the master. Moreover, we characterize the uplink and downlink communication loads, and prove the tightness of uplink communication load. Theoretical tradeoff between uplink and downlink communication loads is established indicating that larger uplink communication load could reduce downlink communication load. Compared to Prakash's schemes which require a master to connect with helpers though noiseless links, our scheme can even reduce the communication load in the absence of master when the number of clients and helpers is relatively large compared to the number of straggling links.
Review chapter on mineral exploration section

In this part of the book noninvasive studies and methods for mineral exploration are proposed. The proposed methodologies consist in combining known techniques for recording data [deep seismic sounding, magneto acoustic emission (MAE), airborne gamma ray, drone, fault map, etc.] with mathematical or statistical techniques [empirical mode decomposition (EMD), fractal and multifractal analysis, statistical parameters] in order to extract features that make it possible to identify ore deposits. Various places in different countries have been explored, such as the Hoggar area (Algeria), Urals and Siberia (Russia), northern Chile (II and III regions), and northern Italy (Trentino Alto Adige).
Optimization of Quadratic Curve Fitting from Data Points Using Real Coded Genetic Algorithm

Curve fitting is a mathematical computation to fit a curve over an equation for generating exact fit for any type of mathematical equation such as linear equation and quadratic equation; curve fitting follows the least mean square fit methodology to achieve the optimal solution so that the error can be minimized. However, here we are attempting to solve the problem using Real Coded Genetic Algorithm to minimize the error rather than gradient search which is needed for the conventional method of curve fitting. In this paper, I am using a real coded genetic Algorithm with a large number of parameters in real coded allows creating a large set of possible permutations that can be examined by the search method for finding the solutions where there is large search space. Large search space means the length of strings is large with a large number of parameters for the variables which use multiple crossover and mutation occurs in every parameter for ensuring better and efficient outcomes.
On Model Pluralism and the Utility of Quantitative Decision Support

Risk AnalysisVolume 41, Issue 6 p. 874-877 Response On Model Pluralism and the Utility of Quantitative Decision Support Robert Lempert, Corresponding Author Robert Lempert lempert@rand.org Pardee Center on Longer Range Global Policy, RAND, Santa Monica, CA, USA Address correspondence to Robert Lempert, Pardee Center on Longer Range Global Policy, RAND, 1776 Main St, Santa Monica, CA, 90407, USA; lempert@rand.orgSearch for more papers by this authorSara Turner, Sara Turner Pardee RAND Graduate School, RAND, Santa Monica, CA, USASearch for more papers by this author Robert Lempert, Corresponding Author Robert Lempert lempert@rand.org Pardee Center on Longer Range Global Policy, RAND, Santa Monica, CA, USA Address correspondence to Robert Lempert, Pardee Center on Longer Range Global Policy, RAND, 1776 Main St, Santa Monica, CA, 90407, USA; lempert@rand.orgSearch for more papers by this authorSara Turner, Sara Turner Pardee RAND Graduate School, RAND, Santa Monica, CA, USASearch for more papers by this author First published: 25 June 2021 https://doi.org/10.1111/risa.13747Read 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 Volume41, Issue6June 2021Pages 874-877 RelatedInformation
Is graph data science the secret to accelerating machine learning?

Abstract Alicia Frame, Director of Graph Data Science at Neo4j, discusses how graph-exposed data will unearth new connections and make predictions that will help business.
Applying Convolutional Neural Network Algorithm for Classify a Tomato Species

Tomatoes are one of the economic crops that make income into the country. The price of a tomato depends on its shape. However, the tomato species classification currently consumes the workforce, which lows classification rate and accuracy. This research aims to overcome that addressed limitation by applying the Convolutional Neural Network (CNN) algorithm for classifying a tomato species. The research methodology consisted of four main components; (1) collection tomato images of 3 sample species (holland tomato, sida tomato, and beef tomato) on three sides (topside, a slanted side, and a straight side), (2) image segmentation, (3) feature extraction about compactness, perimeter, area, radius, and standard deviation of radius, and (4) a tomato species classification using CNN algorithm. The tomato image collection and its characteristics (1,800 images for all samples) develop a CNN classification method. As a result, the selected variables to classify Holland tomato are perimeter and area (95.65% accuracy). The selected variables to classify Sida tomato classify by compactness and radius (86.52% accuracy). The selected variables to classify Beef tomato are Radius and standard deviation of radius using to classify Beef tomato (95.14% accuracy).
Research on Optimal Design of Online Education Course Recommendation System Based on Hybrid Recommendation Algorithm

In recent years, online education technology has developed rapidly, and the market scale of online education platform is growing. However, with the widespread application and development of this technology, the requirements of online education users are gradually becoming stricter. Personalized course recommendation system can greatly improve the satisfaction and learning efficiency of student users, making the education platform stand out from many competitors. Based on the analysis of the advantages and disadvantages of the existing learning resource recommendation system on the online education platform, this paper proposes a new learning resource recommendation model based on hybrid recommendation algorithm, which includes course recommendation submodule based on statistics and personalized course recommendation submodule based on professional training requirements. It can not only help users to find high-quality information that they are interested in, save users' time cost, but also effectively solve the problem of cold boot. This paper provides inspiration for the improvement of online education course recommendation system.
Application of Information Teaching in Undergraduate English Education Based on the Theory of Multiple Intelligence

The theory of multiple intelligences is a theory of human development, which emphasizes that human intelligence is multiple and a dynamic process of development and function. According to this theory, people can move from various complementary intelligences with different strengths and weaknesses to weak ones, which can promote people's all-round development. At present, there are many problems in undergraduate English teaching, which make the teaching effect not ideal. In the era of rapid development of the Internet, relying on the network platform to comprehensively promote the information-based teaching reform, teachers need to pay attention to the application of multiple intelligences theory, use information technology to create diverse teaching situations, innovate diversified teaching models through platform learning resources, and establish a diversified evaluation system combining online and offline, so as to improve teaching efficiency and effectiveness, and at the same time promote the development of students' various abilities.
Web Apps for Intercultural Competences and Sustainable Development

This chapter presents the results of research performed by Colombian researchers. The research aimed at designing, developing, and evaluating the app to promote the development of intercultural competences and ICT skills in higher education teachers for the creation of virtual learning environments. The project was based on the ADDIE model of instructional design and pedagogical principles of self-training, virtual education, interculturality, and universal design for learning. The test version of the app was piloted in a sample of higher education teachers. The evaluators highlighted key aspects such as the content and dynamics of the app, authentic learning activities, use of emerging technologies, numerous possibilities of interaction with technology, intuitive interface, and presentation of strategies for self-regulated learning. End users also evaluated the app positively in terms of ease of navigation, interactivity, clarity in the presentation of information, relevant content, and promotion of respect for diversity.
Implementation of Steganography Modified Least Significant Bit using the Columnar Transposition Cipher and Caesar Cipher Algorithm in Image Insertion

Abstract Steganography technique is a technique to hide data or information into other media such as digital images, text, sound, or video. One of the simplest methods of Steganography in the concept of Steganography is the Least Significant Bit. The Least Significant Bit (LSB) method hides the message by inserting the message at the lower or rightmost bits in the cover work file as a medium to hide the message. Caesar Cipher is one method that replaces each letter in the plaintext with letters that are at odds with certain numbers in the alphabet. Columnar transposition Cipher is one method in which messages are written in rows of a specified length, then are read column by column in the order of reading based on a keyword. The message is inserted into the cover image with the target pixel determination to be inserted based on the results of the Caesar Cipher process of the Columnar Transposition Cipher index to the random value of the Multiply With Carry Generator algorithm. The system implementation uses the C# programming language. The results of this study indicate that the implementation of a combination of Caesar Cipher, Columnar Transposition Cipher, and Multiply With Carry Generator can maintain the confidentiality, integrity, and security of data.
Work-in-Progress-Design Method of a Real-Time Monitoring System for ICT Evaluation Process in Education Based on CesiumJS 3D Visualization

ICT (information and communications technology) evaluation in education is a key step in measuring the development level of education informatization. At present, the statistical data of evaluation process mainly be transmitted in the form of static chart files. How to grasp the overall evaluation status of each evaluation area in real time and understand the latest progress and bottlenecks of evaluation work is an urgent problem. This work-in-progress paper proposes a real-time monitoring system for ICT evaluation process in education based on CesiumJS 3D visualization, which can provide multi-modal 3D visualization for dynamic evaluation process data and further support knowledge mining.
SentiFusion (SF): Sentiment Analysis of Twitter Using Fusion Techniques

The Internet has become a domain for learning, idea exchange, feedback, sharing thoughts or opinions, and many more. In the current world, Twitter has been the most liable platform where people express their views and ideas about a various range of products. In this paper, different feature extraction methods are used to get more accurate sentiment analysis results. Firstly, tweet pre-processing is performed using Euclidean Vector Length Similarity and other dictionaries. The feature extraction is performed using (Jaccard Similarity and Trigrams) and (K-means and KL-divergence). Then using tf-idf and Shannon Entropy, feature fusion and weighting are done. At last, before final Sentiment Computation, Bagging is accomplished using SVM classifiers and Decision Trees.
Digital Image Forensics Using Hexadecimal Image Analysis

Digital forensics is gaining increasing momentum today thanks to rapid developments in data editing technologies. We propose and implement a novel image forensics technique that incorporates hexadecimal image analysis to detect forgery in still images. The simple and effective algorithm we developed yields promising results identifying the tool used for forgery with zero false positives. Moreover, it is comparable to other known image forgery detection algorithms with respect to runtime performance.
Student Behavior Report Management System on Somsri.IO

Student Behavior Report Management System (BRMS) is a module of Somsri.io to deal with a student behavior report book of school because the original report book is a one-way communication. Teachers only report behavior of students in classroom to parents, however parents cannot response behavior of students in home to their teachers. So behavior of learners which are monitored are not comprehensive care and difficult to change to be a good student.The Somsri.io has been created as a web application for a school management in which system consists of personnel management, student management, payroll management, and student BRMS which name is My-Day which is developed to be a two-ways communication between parents and teachers for improving behavior of students though BRMS which services on website by using Ruby on Rails, AngularJS, and GraphQL. The results from experiment found that behavior of learners easy observable and measurable by using My-Day for monitoring and changing behavior of students to be a good learner.
Features of Educational Motivation and Professional Identity of Russian and Indian Students at the Stage of Admission to the Master's School

The article is devoted to the study of the peculiarities of educational motivation and professional identity of Russian and Indian students studying in Russia and intending to continue their studies in the magistracy. The use of a comparative analysis based on the test results for two samples, a questionnaire survey, showed the predominance of internal learning motivation and a higher level of professional identity of Indian students compared to students from Russia. The factors influencing the results of measuring educational motivation are indicated. General recommendations on the formation of optimal educational motivation and professional identity of future masters are given.
Mix-groups Attention Network for Object Detection

With the development of deep learning, object detection has made great progress in the past few years, and it mainly involves the combination of object classification and localization within a scene. Effective convolution features are critical to improve detection for objects of different scales, but learning powerful features remains a challenging task due to scale variations, occlusion. In view of this fact, we introduce a lightweight Mix-groups attention network to solve these problems. Firstly, a novel group attention module is proposed, which can learn more effective part-level information. Furthermore, to enrich multi-scale representations, it is combined with channel attention to generate Mix-groups attention module, assigning weights to image features in spatial and channel dimensions. The new approach can better highlight key objects, eliminate background clutter, and directly support multi-scale feature learning. A number of experimental results show that the proposed module has achieved excellent improvement.
Hardware-Aware Model Optimization Tool for Embedded Devices

Designing deep neural network models for embedded devices is a challenging task since the models need to be lightweight, fast, and accurate. This paper proposes a hardware-aware model optimization tool (HOT) to optimize a given model in terms of latency or accuracy by replacing its existing operators with the best-performing operators for target hardware. The proposed tool finds optimal operators with high accuracy and low latency in a short searching time while keeping the existing model structure rather than finding an entirely new network architecture. The result shows that the HOT improves MobileNetV2 backbone based models by up to 13.93% in accuracy (mAP) or 37.84% in latency (ms) with cascaded pyramid network (CPN) (pose estimation) and 31.03% in mAP or 56.64% in latency (ms) for single shot multi-box detector (SSD) (object detection) on digital television.
Convergence of Q-Value in Case of Gaussian Rewards

In this paper, as a study of reinforcement learning, we converge the Q function to unbounded rewards such as Gaussian distribution. From the central limit theorem, in some real-world applications it is natural to assume that rewards follow a Gaussian distribution, but existing proofs cannot guarantee convergence of the Q-function. Furthermore, in the distribution-type reinforcement learning and Bayesian reinforcement learning that have become popular in recent years, it is better to allow the reward to have a Gaussian distribution. Therefore, in this paper, we prove the convergence of the Q-function under the condition of $$E[r(s,a)^2]<\infty $$ , which is much more relaxed than the existing research. Finally, as a bonus, a proof of the policy gradient theorem for distributed reinforcement learning is also posted. $$\dots $$
A Top-Down Approach to Attain Decentralized Multi-agents

In the modern age with the enormous amounts of data being collected, machine learning has blossomed into a necessary tool in nearly all academic and industrial fields. In many cases, machine learning deals with a static dataset, e.g., a fixed training data, for its analysis, such as in recognition of hand-written digits. Yet there is also a need to apply machine learning to streams of data, i.e., online learning. One approach is that of reinforcement learning, the setting being an agent seeking to maximize a cumulative reward signal it receives from the environment. An extension of this notion is multi-agent reinforcement learning, where now the setting is that we have many agents that seek to maximize their cumulative reward signal; the stipulation being that each agent can only observe its own local observation and communication from other agents which may have limited bandwidth, i.e., the agents must act in a decentralized manner. In this chapter, we demonstrate a method, centralized expert supervises multi-agents (CESMA), to obtain decentralized multi-agents through a top-down approach; we first obtain a solution with a centralized controller, and then decentralize this using imitation learning.
The Mobile Learning Implementation for Physics Lesson in Indonesia: A Narrative Review

Abstract Indonesia is an archipelago with a vast landscape. Equal access to education has been an important issue in improving the quality of education to support human resource provision. This study is a narrative review study based on reflections during conducting multi-year research from 2015 to 2020 funded by MOEC. During the research period, authors arranged socialization, training, mentoring, and measuring the impact of mobile learning implementation in rural and remote areas, especially in eastern Indonesia. The results of this study indicate that there is good potential for mobile learning to support equitable access to education. Governments need to consider several essential aspects so mobile learning can work well. The aspects are the readiness of infrastructure and the readiness of technology acceptance by educational decision-makers and principals. The results of this study, as a whole, can be used as a basis for developing various educational policies in the disadvantaged region.
Computational Analysis of Goal-Oriented Adaptive Strategies based on Several Error Representations

Goal-oriented error estimation and adaptivity provides a collection of techniques to control approximations of a specific quantity of interest (QoI), defined as a functional of the solution to some initial or boundary-value problem, the so-called primal problem.Using the adjoint problem, the error in the QoI can be represented in several ways in terms of the error in either the primal or the adjoint solutions, or both.In practice, computable a posteriori goal-oriented error estimates are obtained using solutions computed, for instance, on a finer mesh.For adaptive purposes, the error estimates should be decomposed into local contributions.However, the decomposition is not unique and the local refinement indicators may vary from one decomposition to another.This observation immediately implies that goal-oriented adaptive algorithms may perform differently depending upon the selected representation and decomposition.The goal of the presentation will be to compare the performance of adaptive procedures when using some of the proposed error decompositions.
Identifying Optimal Features using Whale Optimization Technique

This paper utilizes Whale Optimization Algorithmic rule (WOA), a meta-heuristic optimization algorithmic rule (nature-inspired one), to go looking for the optimum set of features or options. Feature selection techniques are crucial to understand data better, remove redundant features and also lead to increased efficiency and accuracy. Hellinger Distance (HD) function has been used for feature scaling and in Whale Optimization Algorithm RUSBoost has been used as the classifier. This algorithm reduces the size of the feature into 6 features that play the main role in the chosen dataset named the Eclipsedata1. To know how the algorithm is performing, XGBoost model has been trained using the features selected with the help of WOA. The accuracy achieved is 85.49%.
Privacy Preserving Data Sharing in Online Social Networks

Social networks pervaded human lives in mostly each aspect. The vast amount of sensitive data that users produce and exchanged on these platforms call for intensive concern about information and privacy protection. Moreover, the users’ statistical usage data collected for analysis is also subject to leakage and therefor require protection. Although there is an availability of privacy preserving methods, they are not scalable, or tend to underperform when it comes to data utility and efficiency. Thus, in this paper, we develop a novel approach for anonymizing users’ statistical data. The data is collected from the user’s behavior patterns in social networks. In particular, we collect specific points from the user’s behavior patterns rather than the entire data stream to be fed into local differential privacy (LDP). After the statistical data has been anonymized, we reconstruct the original points using nonlinear techniques. The results from this approach provide significant accuracy when compared with the straightforward anonymization approach.