Reviewerly/exHarmony huggingface dataset:
Student Representation of Mass Media as Tool for Forming Public Opinion

The relevance of this article is to study the role of mass media and the extent of their impact on public consciousness, since almost every home has a TV, which is the most accessible way to communicate with the outside world and a means of obtaining information. The strengthening of modern mass media role, especially the Internet and television, in modern society is obvious, since information TV programs are aimed primarily at rapid, regular information about events in domestic and international life; in this regard, the most acute question is about the media as a tool for forming public opinion. The purpose of the study is to analyze students’ perceptions of the mass media as a tool for forming public opinion and students’ attitude to technologies for forming public opinion regarding the production of competitive import-substituting goods and services using the mass media. Research methods: as a research method, we used content analysis of TV shows, which allows us to identify in news: what events were broadcasted, what they said, how and in what context; as well as the interview method, which allows us to identify students’ perception of the media as a tool for forming public opinion and determine the degree of impact of messages contained in the media on public opinion according to students. Results of the research: the article defines the students’ perception of the mass media as a tool for forming public opinion. Verbal forms of presentation of information in television programs, which raised the topic of the production of competitive import-substituting goods and services, their application in various sectors of the economy are defined the rhetoric of news programs on the topics studied is described. It is revealed that these mass media use various information technologies: archetypes, images of folklore, designed to create a positive image of the production process of competitive import-substituting goods and services, the Russian product, and vice versa, a negative image of Europe and the United States. It is revealed that in news programs, the introduction of Western sanctions against Russia is often presented as a positive factor, although the negative impact is also presented quite significantly. It is shown that, according to the students, after the introduction of the food embargo, import substitution programs were introduced for goods produced within the country, which led to new opportunities and development of the potential of Russian citizens. It is determined that students see the role of television as ideological rather than informational. Students allocate the following tools for influencing public consciousness: emotional impact, suggestion, transfer of the private into the General, the use of rumors, the method of using frightening moments, silencing facts.
Center-of-Gravity Variation-Driven Spherical UAV System and Its Control Law

Most of the spherical unmanned aerial vehicles (SUAVs) use control surfaces, which are functions of aileron and an elevator, to generate control torque. The work proposes a new conceptual design of an SUAV system controlled through center-of-gravity (CG) variations with its path-tracking control law designed for the system. Compared to the one using control surfaces, the concept suggested is beneficial in the aspect of the expandability of building lighter and smaller SUAVs, especially. A CG variation principle by actuating a pendulum type of a moving part is considered as a methodology for both translational and rotational motion control of an SUAV. Since variations of the moment-of-inertia (MOI) elements which resulted from the motion of the moving part affect the performance of the suggested method, the variations of MOI analysis are performed for all angular ranges of the moving part. As a result, certain angular ranges for the moving part to prevent the degradation of the path-tracking performance by the effect of the MOI changes are found. By considering the findings, numerical studies are performed for hovering, ascent, descent, and horizontal tracking missions. The applicability of the proposed SUAV system and the corresponding controller to achieve the path-tracking missions is demonstrated through the numerical simulation.
Bayesian decomposition of multi-modal dynamical systems for reinforcement learning

In this paper, we present a model-based reinforcement learning system where the transition model is treated in a Bayesian manner. The approach naturally lends itself to exploit expert knowledge by introducing priors to impose structure on the underlying learning task. The additional information introduced to the system means that we can learn from small amounts of data, recover an interpretable model and, importantly, provide predictions with an associated uncertainty. To show the benefits of the approach, we use a challenging data set where the dynamics of the underlying system exhibit both operational phase shifts and heteroscedastic noise. Comparing our model to NFQ and BNN+LV, we show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation

Domain adaptation (DA) is the topical problem of adapting models from labelled source datasets so that they perform well on target datasets where only unlabelled or partially labelled data is available. Many methods have been proposed to address this problem through different ways to minimise the domain shift between source and target datasets. In this paper we take an orthogonal perspective and propose a framework to further enhance performance by meta-learning the initial conditions of existing DA algorithms. This is challenging compared to the more widely considered setting of few-shot meta-learning, due to the length of the computation graph involved. Therefore we propose an online shortest-path meta-learning framework that is both computationally tractable and practically effective for improving DA performance. We present variants for both multi-source unsupervised domain adaptation (MSDA), and semi-supervised domain adaptation (SSDA). Importantly, our approach is agnostic to the base adaptation algorithm, and can be applied to improve many techniques. Experimentally, we demonstrate improvements on classic (DANN) and recent (MCD and MME) techniques for MSDA and SSDA, and ultimately achieve state of the art results on several DA benchmarks including the largest scale DomainNet.
Robot navigation and target capturing using nature-inspired approaches in a dynamic environment

Path Planning and target searching in a three-dimensional environment is a challenging task in the field of robotics. It is an optimization problem as the path from source to destination has to be optimal. This paper aims to generate a collision-free trajectory in a dynamic environment. The path planning problem has sought to be of extreme importance in the military, search and rescue missions and in life-saving tasks. During its operation, the unmanned air vehicle operates in a hostile environment, and faster replanning is needed to reach the target as optimally as possible. This paper presents a novel approach of hierarchical planning using multiresolution abstract levels for faster replanning. Economic constraints like path length, total path planning time and the number of turns are taken into consideration that mandate the use of cost functions. Experimental results show that the hierarchical version of GSO gives better performance compared to the BBO, IWO and their hierarchical versions.
ON THE LINK BETWEEN FUTURE STUDIES AND NECESSITY OF INCLUDING CORROSION IN A “DESIRED FUTURE” SCENARIO: PRESENTING A MODEL

In this paper a model is developed to address how a bad, man-made incident such as corrosion as an element for moving from a possible future to a desired future must be handled within a Participatory Action Learning scheme. The possible mechanisms (including corrosion knowledge management as a managerial tool) are also addressed and discussed.
Performance prediction based on neural architecture features

Neural Architecture Search (NAS) usually requires to train quantities of candidate neural networks on a dataset for choosing a high-performance network architecture and optimising hyperparameters, which is very time consuming and computationally expensive. In order to resolve the issue, the authors try to use a performance prediction method to predict the model performance with little or even no training steps. They assume that the performance is determined once the architecture and other hyperparameters are chosen. So they first extract the sequence features of the chain-structured neural architecture by introducing the N-grams model to process architecture textual description. Subsequently, based on the extracted neural architecture features, they use the appropriate regression model to predict validation accuracies for a modelling learning curve. Through a series of experimental comparisons, they verify the effectiveness of the authors’ proposed performance prediction method and the effective acceleration of the NAS process.
Enriching Natural Monument with User-Generated Mobile Augmented Reality Mashup

This paper proposes a mobile augmented reality mashup for cultural heritage sites such as natural monuments.Several benefits of mobile augmented reality solutions are ideal for preserving and protecting cultural heritage sites.By presenting mobile augmented reality mashup scenarios and mobile mashup framework, we introduce how user-generated multimedia contents can be added.We present two scenarios of Mashup Viewer and Mashup Maker.In Mashup Viewer mode, visitors can create new AR contents using mashup tools for memo, Twitter, images and statistical graphs.In Mashup Maker mode, other visitors also can view the user-generated multimedia AR contents using QR codes as access points.To show feasibility of our approach in mobile platforms, we compare several detection algorithms on PC and mobile platform and report on deployment of our approach in a natural monument museum.With our proposed mashup tools, visitors to the cultural heritage sites can enjoy default AR contents provided by the site administrators and also participate as active content producers and consumers.
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition

Unknown examples that are unseen during training often appear in real-world computer vision tasks, and an intelligent self-learning system should be able to differentiate between known and unknown examples. Open set recognition, which addresses this problem, has been studied for approximately a decade. However, conventional open set recognition methods based on deep neural networks (DNNs) lack a foundation for post recognition score analysis. In this paper, we propose a DNN structure in which multiple one-vs-rest sigmoid networks follow a convolutional neural network feature extractor. A one-vs-rest network, which is composed of rectified linear unit activation functions for the hidden layers and a single sigmoid target class output node, can maximize the ability to learn information from nonmatch examples. Furthermore, the network yields a sophisticated nonlinear features-to-output mapping that is explainable in the feature space. By introducing extreme value theory-based calibration techniques, the nonlinear and explainable mapping provides a well-grounded class membership probability models. Our experiments show that one-vs-rest networks can provide more informative hidden representations for unknown examples than the commonly used SoftMax layer. In addition, the proposed probability model outperformed the state-of-the art methods in open set classification scenarios.
Hybrid Semantic Recommender System for Chemical Compounds

Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking (BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.
Effects of culture on graphical password image selection and design

Purpose The purpose of this study is to examine the effects of culture on the cross-cultural design of the recognition-based graphical password (RBG-P) interface as inferred from Chinese and Saudi subjects’ image selections. Design/methodology/approach The authors use a between-group design adopted using two groups of participants from China and the Kingdom of Saudi Arabia to measure the differences caused by the effects of cultures on graphical password image selections. Three hypotheses have been tested in a four-week long study carried out using two questionnaires and an RBG-P webtool designed for images selection. Findings The results have indicated that participants are equally biased not only toward their own culture but also depending on their opinions about other cultures. In addition, when creating the password, it has been observed that culture not only influenced the image selection to create the password but also have an effect on the sequence of the images forming the password. Research limitations/implications Appropriately used image selection differences can be used appropriately in cross-cultural designs that will lead to better development of culturally adaptive interfaces that will boost the security posture of RBG-P authentication. Practical implications Some RBG-P interfaces that are produced outside the designer’s culture may suffer the effects of cultural differences. Hence, to incorporate culture in the interface, authentication systems within applications should be flexible by designing images that fit the culture in which the software will be used. To this end, access control interface testing should also be carried out in the environmental and cultural context in which it is will be used. Originality/value This paper provides useful information for international developers who develop cross-cultural usable secure designs. In such environments, the cross-culturally designs may have significant effects on the acceptability and adoption adaptation of the interface to multi-cultural settings.
Industrial Applications of Blockchain to IoT Data

BlockchainBlockchain has been long touted as the perfect technological complement to IoTInternet of Things (IoT) systems. To understand why there has been such enthusiasm for the synergies between these two seemingly unrelated technology systems, we first examine some of the largest challenges facing the IoTInternet of Things (IoT) space, divided into several broad categories: technological, commercial, and social.
A Summary of the Application of Artificial Intelligence in Music Education

With the development of modern science and technology, the application of artificial intelligence in music education is more and more extensive, which is beneficial to the development of music education in China.The application of artificial intelligence in music education has broken the traditional music education model, especially the application of computer music system and high intelligent music software in music education, which has greatly improved the quality of music teaching and expanded the music teaching model.
The Use of Blockchain Technology in Agriculture

Objective : This paper explores the use of blockchain technology in agriculture and agricultural products. Research Design &amp; Methods : The article is based on a critical analysis of the literature with a view to understanding the current state of use of blockchain technology in agriculture. It was assumed that blockchain technology is used in the agricultural sector to promote food security, prevent food fraud and verify the origin and authenticity of agricultural products and agricultural inputs. Findings : Blockchain technology improves traceability and transparency, allowing parties within the agricultural value chain to identify faulty or suboptimal processes as well as bad actors. This ensures that ideal conditions are pursued from farm to market. The ability to trace the origin of food products is essential when food safety breaks down. The early identification of the origin of contamination will enable food companies to swing into action quickly to prevent illness and thus save lives. Such a timely response will also help limit food wastage and will save money by containing financial fallout. Implications / Recommendations : Blockchain technology has strong potential for success within the agricultural sector. It can be used to ensure food safety by enabling the source of agricultural products, as well as the source of their potential contamination, to be traced, and the authenticity of farming inputs to be verified. Blockchain can also be employed in the process of disbursing subsidies to farmers to ensure that they benefit from subsidy programmes. Finally, blockchain technology will offer farmers better prices and better payment methods and solve challenges in land title sales and purchase registration. Contribution : Blockchain is a new technology to the agricultural sector, and enormous challenges remain. There is still no established system to regulate blockchain transactions. Nevertheless, the application of blockchain in agriculture holds promising rewards.
Analysis and Research on the Combination of Virtual Reality Technology (VR) and College Sports Training

Abstract The combination of virtual reality technology (VR) and college sports training makes college sports training continue to innovate with the help of emerging technologies and receive good results. Virtual reality technology combines traditional technology advantages in various fields with sensor technology, three-dimensional animation technology, and network communication technology. Through research and innovation, through virtual reality technology, computer communication technology is used to achieve an effective fit between sports training and virtual reality technology. It is used to improve the technical level and training quality of college athletes, give full play to their technological advantages, and contribute to the reserve of reserve talents for competitive sports in China.
Show Us the Way: Learning to Manage Dialog from Demonstrations

We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge. Our proposed dialog system adopts a pipeline architecture, with distinct components for Natural Language Understanding, Dialog State Tracking, Dialog Management and Natural Language Generation. At the core of our system is a reinforcement learning algorithm which uses Deep Q-learning from Demonstrations to learn a dialog policy with the help of expert examples. We find that demonstrations are essential to training an accurate dialog policy where both state and action spaces are large. Evaluation of our Dialog Management component shows that our approach is effective - beating supervised and reinforcement learning baselines.
A Comparison of Deep Reinforcement Learning and Deep learning for Complex Image Analysis

The image analysis is an important and predominant task for classifying the different parts of the image.The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis.Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis.The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons.The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology.The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information.In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models

Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation. In this work, by contrast, we hypothesize that these encoder outputs can be compressed to shorten the sequence delivered for decoding. We take Transformer as the testbed and introduce a layer of stochastic gates in-between the encoder and the decoder. The gates are regularized using the expected value of the sparsity-inducing L0penalty, resulting in completely masking-out a subset of encoder outputs. In other words, via joint training, the L0DROP layer forces Transformer to route information through a subset of its encoder states. We investigate the effects of this sparsification on two machine translation and two summarization tasks. Experiments show that, depending on the task, around 40-70% of source encodings can be pruned without significantly compromising quality. The decrease of the output length endows L0DROP with the potential of improving decoding efficiency, where it yields a speedup of up to 1.65x on document summarization tasks against the standard Transformer. We analyze the L0DROP behaviour and observe that it exhibits systematic preferences for pruning certain word types, e.g., function words and punctuation get pruned most. Inspired by these observations, we explore the feasibility of specifying rule-based patterns that mask out encoder outputs based on information such as part-of-speech tags, word frequency and word position.
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications. However, they are also notorious for being slow in inference, which makes them difficult to deploy in real-time applications. We propose a simple but effective method, DeeBERT, to accelerate BERT inference. Our approach allows samples to exit earlier without passing through the entire model. Experiments show that DeeBERT is able to save up to ~40% inference time with minimal degradation in model quality. Further analyses show different behaviors in the BERT transformer layers and also reveal their redundancy. Our work provides new ideas to efficiently apply deep transformer-based models to downstream tasks. Code is available at https://github.com/castorini/DeeBERT.