Reviewerly/exHarmony huggingface dataset:
The Problem Setting

We first introduce the domain of this book: low resource social media text. The domain encompasses some of the most used languages in the world and a wide variety of tasks and applications. We explore the socio-technical conditions that lead to such text, and how it influences expression online. Examples and statistics are provided from various social media data sets and recent research. We then cover attempts to bridge the resource gap between world languages like English and low-resource languages. Special attention is given to the various data acquisition strategies employed by researchers. This chapter will help NLP practitioners understand the importance of analyzing the low-resource components of corpora from various societies and how ignoring them can skew results, how to go about addressing these, and a broad set of examples and statistics to reinforce the importance of low-resource social media text mining.
Augmenting Training Data for Low-Resource Neural Machine Translation via Bilingual Word Embeddings and BERT Language Modelling

Neural machine translation (NMT) is often described as ‘data hungry’ as it typically requires large amounts of parallel data in order to build a good-quality machine translation (MT) system. However, most of the world's language-pairs are low-resource or extremely low-resource. This situation becomes even worse if a specialised domain is taken into consideration for translation. In this paper, we present a novel data augmentation method which makes use of bilingual word embeddings (BWEs) learned from monolingual corpora and bidirectional encoder representations from transformer (BERT) language models (LMs). We augment a parallel training corpus by introducing new words (i.e. out-of-vocabulary (OOV) items) and increasing the presence of rare words on both sides of the original parallel training corpus. Our experiments on the simulated low-resource German–English and French–English translation tasks show that the proposed data augmentation strategy can significantly improve state-of-the-art NMT systems and outperform the state-of-the-art data augmentation approach for low-resource NMT.
Human education in the historical space virtual world of a computer game

This paper is devoted to computer games as a means of educating people. The paper emphasizes that computer games are firmly embedded in the everyday culture of modern society, while they carry a number of threats. However, in addition to the negative aspects, computer games have some positive features that are reasonable to implement within the framework of educational and educational programs. In a sense, a computer game is compared to a book, the content of which a person gets used to and experiences it. Certainly, we recognize that you can't put an equal sign between a computer game and a book, so this comparison is conditional. Thanks to visualization, a computer game can transmit information in a form that is not available to text or speech. It seems that if you do not study computer games in a pedagogical sense, their strengths will remain unclaimed and the whole essence of these games will be reduced to entertainment. English version of the article on pp. 664-670 is available at URL: https://panor.ru/articles/character-building-and-education-in-the-historical-context-of-the-virtual-world-of-computer-games/70041.html
FoLaR: Foggy Latent Representations for Reinforcement Learning with Partial Observability

We propose a novel methodology for improving the rate and consistency of reinforcement learning in partially observable (foggy) environments, under the broader umbrella of robust latent representations. The present work addresses partially observable environments, which violate the canonical Markov assumptions. We propose adaptations for any on-policy model-free deep reinforcement learning algorithm, in order to improve training in partially observable situations: (i) recurrent layers for including information from previous observations, (ii) predicting the step reward and the next latent representation as auxiliary outputs from the same latent space as used for inferring the action, and (iii) modification of the loss function to penalise errors in the two auxiliary outputs, in addition to the reward-based gradients used for policy training. We show that the proposed changes substantially improve learning in several environments over vanilla Proximal Policy Optimisation (PPO) and other baselines in literature, especially in known challenging environments with hard exploration.
Research on Satellite Cable Laying and Assembly Guidance Technology Based on Augmented Reality

Spacecraft satellite has the characteristics of large number of cables, many branches and complex layout. The traditional cable-laying task adopts the combination of viewing the two-dimensional electronic assembly process and browsing 3D model, which shows some problems, such as poor understanding, inaccurate assembly accuracy, low efficiency and insufficient assembly-guidance. For this purpose, the application of augmented reality (AR) in the Aerospace Satellite cable-laying assembly was studied, using the SLAM positioning and tracking technology and ORB algorithm to solve the problem of multiple artificial markers and limited field of vision in the satellite cable-laying; The virtual real occlusion processing method based on contour tracking was used to solve the real occlusion relationship between virtual cable and satellite structure; Based on the deployment of Hololens, the AR visual cable laying guidance system was constructed. Through the immersive 3D display and visual process information, the cable-laying assembly-guidance can be realized. Finally, it is applied to a certain type of satellite for application verification, the results show that augmented reality technology has a higher advantage than the traditional way in satellite cable-laying, and can effectively improve the efficiency and assembly quality in the satellite cable-laying assembly.
UAV Path Planning with No-Fly-Zone Constraints by Convex Optimization

This paper presents a convex optimization methodology for an unmanned aerial vehicle (UAV) path planning problem with no-fly-zone (NFZ) constraints. The great challenge is to cast the original problem into a second-order cone programming (SOCP) frame. Since the original dynamic model of the UAV is nonlinear, a special transformation is applied to linearize the dynamic model. To make the problem tractable, this paper employs lossless convexification and relaxation. Then the L1 penalty method is introduced to ensure the feasibility of the transformed problem. The feasibility and effectiveness of the designed successive SOCP algorithm are demonstrated by numerical simulations.
Service Replacement Based on Service Cluster Based on Information Technology

With the rapid growth of service resources, how to quickly and accurately find the replaceable service is a key problem when the service fails. To solve this problem, this paper proposes a research method of service replacement based on service cluster. Some similar web Services are put in the same set. Through the optimization of function similarity and service function, the candidate set of service replacement is generated. On the basis of the candidate set, Word2Vec is used to calculate the word embedding of each cloud service. By calculating the cosine value of the word vector of two cloud services, the similarity of the service is obtained, the cloud service with high similarity is replaced with the invalid service. Experimental results show that the method is correct and effective, and improves the efficiency of service replacement to a certain extent.
Deep Reinforcement Exemplar Learning for Annotation Refinement

Due to the inter-observer variation, the ground truth of lesion areas in pathological images is generated by majority-voting of annotations provided by different pathologists. Such a process is extremely laborious, since each pathologist needs to spend hours or even days for pixel-wise annotations. In this paper, we propose a reinforcement learning framework to automatically refine the set of annotations provided by a single pathologist based on several exemplars of ground truth. Particularly, we treat each pixel as an agent with a shared pixel-level action space. The multi-agent model observes several paired single-pathologist annotations and ground truth, and tries to customize the strategy to narrow down the gap between them with episodes of exploring. Furthermore, we integrate a discriminator to the multi-agent framework to evaluate the quality of annotation refinement. A quality reward is yielded by the discriminator to update the policy of agents. Experimental results on the publicly available Gleason 2019 dataset demonstrate the effectiveness of our reinforcement learning framework—the segmentation network trained with our refined single-pathologist annotations achieves a comparable accuracy to the one using majority-voting-based ground truth.
Image mosaic based on improved SPHP mesh optimization method

In order to solve the problems of ghosting, deformation and skew matching in large parallax image mosaic, an improved SPHP method is proposed. The steps are as follows: 1. In the feature extraction stage, feature points are extracted by ASIFT algorithm; 2. In the feature matching stage, feature points are roughly matched by KNN algorithm, and feature points are purified by RANSAC algorithm; 3. In the image alignment stage, the spatial transformation model and similar transformation model are calculated by SPHP algorithm to correct the shape and effect of stitching; 4. In the image fusion stage, the linear weighted fusion algorithm is used to fuse the overlapping area. Compare with the traditional autostitch algorithm and SPHP algorithm, the experimental results show that the quality of the improves SPHP algorithm is significantly improved, and the normalized mutual information is improved by 1.4% compared with the SPHP algorithm.
Knowledge Graph Post-Processing for QA Systems

Constructed large-scale Chinese knowledge graph may have many problems when used. In the process of implementing a knowledge-based QA system, the paper finds that the knowledge graph has many defects reducing the accuracy of the QA system. These defects are 1) synonymous attributes and relationships, 2) ambiguous attributes, 3) lacking data needed for application, 4) numeric attributes are not uniform. Wrong results caused by these defects may be hard to avoid by existing algorithms. Paper solves the troubles from the perspective of the knowledge graph, not the system. Research first puts forward the viewpoint of application-driven knowledge graph post-processing and proposes a framework to post-process KGs. The framework is based on the feedback mechanism of the QA system. Our framework has four modules, including 1) merge synonymous attributes based on iteration, 2) divide one ambiguous attribute into multiple attribute s based on Dublin Core™ Metadata Element Set, 3) use knowledge graph completion technology to add critical data, 4) standardize non-standard numerical attributes based on the template. The post-processing of the knowledge graph is to modify the accuracy of the QA system and reduce the difficulties of implementation
Energy Saving Scheduling Model of Virtual Machine in Data Center based on Multi-Objective Optimization

When a user creates a virtual machine, the cloud computing platform selects an appropriate physical host to create a virtual machine based on the virtual machine scheduling strategy according to the virtual machine configuration and other information in the user request, and allocates the required CPU, memory and other resources for it. Aiming at the problem of virtual machine migration, this paper establishes the framework of virtual machine migration. This paper studies the four main problems in the process of virtual machine migration, and gives the corresponding strategies. The purpose of this study is to improve resource utilization, balance physical machine load and minimize migration times.
Intelligent Control System for Cigarette Processing Based on Deep Learning

research-article Intelligent Control System for Cigarette Processing Based on Deep Learning Share on Authors: Shunpeng Pang Ocean University of China, China Ocean University of China, ChinaView Profile , Junhua Jia Ocean University of China, China Ocean University of China, ChinaView Profile , Baoqi Guo Qingdao New Star Software Consulting Co., Ltd, China Qingdao New Star Software Consulting Co., Ltd, ChinaView Profile , Xiangqian Ding Ocean University of China, China Ocean University of China, ChinaView Profile , Shusong Yu Ocean University of China, China Ocean University of China, ChinaView Profile Authors Info & Claims ACM TURC 2021: ACM Turing Award Celebration Conference - China ( ACM TURC 2021)July 2021 Pages 39–43https://doi.org/10.1145/3472634.3472643Online:02 October 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Информационно-кибернетический подход к проектированию педагогического эксперимента

The stages of a pedagogical experiment on teaching information security of future economists are presented from a cybernetic informational perspective. The experimental results were statistically processed, evaluated using the nonparametric Mann-Whitney test and the φ *criterion - Fisher's angular transformation.
A Survey on Metaheuristics-Based Task Scheduling

Cloud computing (CC) is a pool of services which includes services in means of infrastructure, memory, applications, platform, storage and many more. Cloud provides service as per requests, at a given time numerous users might be working to get the resources provided by the service provider. The main issue here for the service provider is how to handle these requests, so they have to do it in such a way that the quality promised in not compromised. For this purpose, we need to choose such a scheduling mechanism that could efficiently perform the task of mapping resources from that populous pool of resource. Keeping in account various metrics such as execution time and makespan that effect the parameter of task scheduling like response time, load balancing, completion time, etc. In this paper, we will be having a systematic review of various task scheduling algorithm choosing different metrics to work on.
Variational Encoding and Decoding for Hybrid Supervision of Registration Network

Various progresses have been made in improving the accuracy of deep learning-based registration. However, there are still some limitations with current methods because of 1) difficulty of acquiring supervised data, 2) challenge of optimizing image similarity and enforcing deformation regularization, and 3) small number of training samples in such an ill-posed problem. It is believed that prior knowledge about the variability of a population could be incorporated to guide the network training to overcome these limitations. In this paper, we propose a group variational decoding-based training strategy to incorporate statistical priors of deformations for network supervision. Specifically, a variational auto-encoder is employed to learn the manifold for reconstructing deformations from a group of valid samples by projecting deformations into a low dimension latent space. Valid transformations can be simulated to serve as the ground-truth for supervised learning of registration. By working alternatively with the conventional unsupervised training, our registration network can better adapt to shape variability and yield accurate and consistent deformations. Experiments on 3D brain magnetic resonance (MR) images show that our proposed method performs better in terms of registration accuracy, consistency, and topological correctness.
Markov-Chain-Based Agents for k-Armed Bandit Problem

In this paper, we present our findings on applying Markov chain generative model to model actions of an agent in Markov decision process framework. We outlined a problem of current solutions to reinforcement learning problems that utilize the agent-environment framework. This problem arises from the necessity of performing analysis of each environment state (for example for q-value estimation in q-learning and deep q-learning methods), which can be computationally heavy. We propose a simple method of ‘skipping’ intermediate state analysis for which optimal actions are determined from analysis of some previous state and modeled by a Markov chain. We observed a problem of this approach that limits agent’s exploratory behavior by setting Markov chain’s probabilities close to either 0 or 1. It was shown that the proposed solution by $$L^1$$ -normalization of transition probabilities can successfully handle this problem. We tested our approach on a simple environment of k-armed bandit problem and showed that it outperforms commonly used gradient bandit algorithm.
Reputation at Stake! A Trust Layer over Decentralized Ledger for Multiparty Computation and Reputation-Fair Lottery

This work introduces, to the best of our knowledge, the first stake based reputation and trust layer to proof of stake (PoS) system. Namely, we show that the delegation framework, introduced by Karakostas et al. (SCN’20) to provide a delegation framework, can be extended and repurposed to construct a trust layer over a PoS consensus protocol in addition to its original application. Furthermore, we show a concrete reputation system satisfying the positive results of (1) Asharov et al. (Asiacrypt’13), allowing the secure execution of multiparty protocols such as GMW (STOC’ 87) and Damgard and Ishai (Crypto’05), and (2) Kleinrock et al. (Indocrypt’20), a Reputation-fair Lottery, thus, also, a Proof of Reputation system. More concretely, our devised layer is used to construct a concrete reputation system based on arbitrary stake distribution. In this layer groups of users can freely and dynamically “assign their respective trust” to members of a set of trustees, i.e. participants that offered themselves as receivers of such assignment. Furthermore, our work offers the advantage of providing a clear stake based criteria, verifiable in the ledger, and, therefore, naturally resistant to sybil attack, that the set of trustees indeed yields an honest majority. This setting provides a better situation than a simple assumption of honest majority, since it involves stake in a decentralized ledger, and the public verifiability of the reputation score via verification of the stake distribution.
A high speed method for features extraction in face recognition systems

Face recognition is one of the most important and practical issues in image processing, and descriptors are used in order to extract image features. These descriptors are used in the face recognition scheme in terms of speed, processing time, and the extracted feature vector dimensions. The method presented in this paper uses local difference binary descriptor and local quantized patterns, which is used in the local difference binary descriptor from the features of average intensity of the pixels, horizontal and vertical gradients and in the local quantized pattern, from the Fourier transform. Because the face recognition scheme has a large database, it is necessary to classify the extracted features using the support vector machine classifier and the nearest neighbor k. The ORL database was used to evaluate the proposed method, which achieved the average recognition rate 97 percentage and total processing time has been at least 80% faster. The evaluation results show the proposed method's proper performance in rapid face recognition scheme and real-time face recognition schemes.
Mobile Technologies in Learning the Chinese Language for Students of Non-Linguistic Courses

The methodological potential of mobile technologies in teaching Chinese language to students of non-linguistic areas of training is considered. The author defines the term “mobile learning,” offers a list of mobile technologies that can be used in teaching Chinese language, and develops a nomenclature of speech and language skills formed by students of non-linguistic training areas based on mobile technologies. The author demonstrated the advantages of using mobile phones in learning Chinese language as a second language. The areas of mobile language-based learning discussed in this research are vocabulary, listening, grammar, phonetics, and reading comprehension.
A solution to minimise the success of phishing attempts using the effects of human behaviour and emotions on falling into a phishing scam

Phishing is a social engineering scam that can cause data loss, reputational damages, identity theft, money loss, and many other damages to people and organisations. Multiple studies showed the effects of human behaviour, such as risk-taking and decision making, on Internet users' security behaviour. Researchers also investigated how email users' behaviour can influence the success of a phishing attempt. Moreover, the number of phishing attempts has been increased rapidly since the beginning of the COVID-19 outbreak. Several studies demonstrated the effects of the COVID-19 pandemic on human behaviour, impacting phishing attempts' success.Organisations can use the results of these studies to find potential high-risk users by measuring the users' behaviour and emotions, which are associated with falling into a phishing scam. In this study, we have developed a solution and guideline using previous studies to identify risky users (i.e., those at risk of clicking on phishing links). The solution will then suggest or assigns proper mitigation actions for those users. The system contains measurement (psychological scales), scoring (machine learning), and mitigation modules that can become more mature and accurate over time. Furthermore, specific situations, such as the pandemic, is also considered in the solution- that is, when a situation like the COVID-19 pandemic happens, the solution will consider the impacted human emotions in finding the high-risk users and might suggest other types of mitigations. We have used regression models for the machine learning module.The proposed solution will help organisations focus more on high-risk users and reduce cyber risks. This solution, however, should be used in combination with technical anti-phishing systems and cybersecurity awareness training campaigns to achieve better results.
IMPROVING VIDEO CONFERENCE SECURITY WITH ARTIFICIAL INTELLIGENCE

As virtual workspaces become more sophisticated, the challenge of protecting an organization's sensitive data from accidental or intentional disclosure and compliance is becoming more challenging. With AI-powered security solutions specifically designed to monitor collaboration platforms, organizations can identify and respond to risks in real time to protect their data and that of their employees.