Reviewerly/exHarmony huggingface dataset:
Solar power forecasting using ordinary least square based regression algorithms

The estimation of solar power has played an important role in solar energy research. Several climatic variables are considered in forecasting solar power. This paper presents solar power forecasting using ordinary least square-based three regression algorithms i.e., linear regression, lasso regression, and multi layer perceptron regression. Climatic variables taken into consideration before and after developing hybrid features have been correlated using heat maps. The effectiveness of the predictive models has been computed through prediction error, residual plots, regression score, and mean square error. The developed pipeline has been briefly compared with existing methods. Comparative analysis between the developed solar power predictive models shows that multi-layer perceptron regression performed better than linear and lasso regression and produced a regression score up to 0.81 upon multiple runs.
Digital platform for open and equitable sharing of scholarly knowledge in India

The academic publishing model is very inequitable currently – most commercial publishers demand exorbitant prices for access to journal articles, either through subscription or article processing charges. In this digital era, when publishing costs are negligible, and the peer review process is voluntary, it is unreasonable to have such high access barriers. In this article, we have explored the idea of an accessible and equitable knowledge dissemination platform using next-generation technologies and emerging models in publication to challenge the commercial publishers’ oligopoly in knowledge dissemination. Some of the upcoming practices, open peer review systems, transparent research data practices, and finally leveraging the advancements in digital technologies in publishing can be effective in creating efficient, transparent and cost-effective publishing systems. Encouraged by these recent developments, we have explored the possibilities of launching a digital publishing platform in India. We have analysed the existing open-source technologies like Open Journal System (OJS) and compared them with proprietary models like Editorial Manager to understand the gaps in both and explored avenues to conceive a cost-effective digital publishing platform. We hope that the open-access digital publishing platform will help the Indian journals upgrade their publishing systems up to the international standard, if not better, and help researchers explore a non-commercial avenue to publish open access articles.
On the capacity and superposition of minima in neural network loss function landscapes

Abstract Minima of the loss function landscape (LFL) of a neural network are locally optimal sets of weights that extract and process information from the input data to make outcome predictions. In underparameterised networks, the capacity of the weights may be insufficient to fit all the relevant information. We demonstrate that different local minima specialise in certain aspects of the learning problem, and process the input information differently. This effect can be exploited using a meta-network in which the predictive power from multiple minima of the LFL is combined to produce a better classifier. With this approach, we can increase the area under the receiver operating characteristic curve by around <?CDATA $20\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>20</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> for a complex learning problem. We propose a theoretical basis for combining minima and show how a meta-network can be trained to select the representative that is used for classification of a specific data item. Finally, we present an analysis of symmetry-equivalent solutions to machine learning problems, which provides a systematic means to improve the efficiency of this approach.
Design of Mentally and Physically Demanding Tasks as Distractors of Rotation Gains

Redirected walking enables natural walking in virtual reality under the constraints of limited physical space by manipulating position and orientation of the user. One such manipulation method are rotation gains which decouple real and virtual head turning. When reaching a boundary of the tracking space, the user&#x0027;s orientation can be reset by applying a rotation gain and forcing the user to do a certain task that requires head rotations. To identify what kind of tasks are best suited to mask the redirection, four tasks were designed that differentiate in their amount of mentally and physically demand: a spatial memory task, a verbal memory task, a physically exhausting task and a task which requires physical skill. A first pilot study was conducted to evaluate the influence that the different task categories have on the redirection awareness. The results showed overall low redirection detection rates, with the physically exhausting task being the least noticeable, whilst the physical skill task was detected the most, and the mentally demanding tasks in between. Although no clear distinction regarding the redirection awareness between mentally and physically demanding tasks could be shown, the insights gained from this work and the study, can be used to improve future implementations of rotation tasks.
Fuzzy Cognitive Map Adaptive Algorithm

This work is aimed at the analysis of the scientific field of fuzzy cognitive maps and the development of the necessary software for the implementation of algorithms for adapting cognitive maps in order to improve the accuracy of the forecast, testing and approbation of the results. The adaptation algorithm basically consists in finding and removing the worst edges in terms of a graph or connections of a cognitive map, that is, such edges, the removal of which leads to an increase in the quality of the map.
Socially-inspired fully decentralized robot coordination

The necessity of the collision-free determination of movement trajectory can be observed among both animals and people. In a completely autonomous way, unrelated units make decisions on choosing their movement trajectory while moving in a common space. There is no explicit communication between the individual participants of the movement, and there is no central planner coordinating their activities. Nevertheless, large groups of unrelated movement participants are able to coordinate their actions, taking the trajectories of other road users into account and performing the task that is entrusted to them. These observations have inspired us to create a decentralized algorithm for coordinating the movement of mobile robots based on a model of the behavior of moving people. The need for the autonomous coordination of many objects and their management sets high requirements in terms of stability, scalability, and flexibility. The use of simple reactive controllers is a remedy that meets the limitations that are imposed on the algorithm. The solution provides methods for defining a collision-free path of movement based on the current observation of a robot's environment. The behaviors that are modeled in the controller have been inspired by natural phenomena such as respect for those who stand higher in the social hierarchy, giving priority to people leaving a room, or generally known traffic rules. This paper presents algorithms for mobile robot coordination that are inspired by such phenomena. The algorithms were tested in both real and virtual environments in several settings, such as open space, a narrow passage, and a narrow corridor, and the results are presented. The experimental results were compared with the results of the recognized reciprocal velocity obstacles (RVO) (Van den Berg et al., 2008) method. Moreover, additional video materials are available online; these show the real-life robots in action when running the proposed route-planning algorithms.
An improved augmented-reality method of inserting virtual objects into the scene with transparent objects

In augmented reality, the insertion of virtual objects into the real scene needs to meet the requirements of visual consistency. The virtual objects rendered by the augmented reality system should be consistent with the illumination of the real scene. However, for complex scenes, it is not enough to just complete the illumination estimation. When there are transparent objects in the real scene, the difference in refractive index and roughness of transparent objects will influence the effect of the virtual and real fusion. To tackle this problem, this paper proposes a new approach to jointly estimate the illumination and transparent material for inserting virtual objects into the real scene. We solve for the material parameters of objects and illumination simultaneously by nesting microfacet model and hemispherical area illumination model into inverse path tracing. Although there is no geometry model of light sources in the recovered geometry model, the proposed hemispherical area illumination model can be used to recover scene appearance. Multiple experiments on both virtual and real-world datasets verify that the proposed approach subjectively and objectively performs better than the state-of-the-art method.
Delving into the Representation Learning of Deep Hashing

Searching for the nearest neighbor is a fundamental problem in the computer vision field, and deep hashing has become one of the most representative and widely used methods, which learns to generate compact binary codes for visual data. In this paper, we first delve into the representation learning of deep hashing and surprisingly find that deep hashing could be a double-edged sword, i.e., deep hashing can accelerate the query speed and decrease the storage cost in the nearest neighbor search progress, but it greatly sacrifices the discriminability of deep representations especially with extremely short target code lengths. To solve this problem, we propose a two-step deep hashing learning framework. The first step focuses on learning deep discriminative representations with metric learning. Subsequently, the learning framework concentrates on simultaneously learning compact binary codes and preserving representations learned in the former step from being sacrificed. Extensive experiments on two general image datasets and four challenging image datasets validate the effectiveness of our proposed learning framework. Moreover, the side effect of deep hashing is successfully mitigated with our learning framework.
Local Discrimination Based on Piezoelectric Sensing in Robots Composed of Soft Matter with Different Physical Properties

The coronavirus epidemic has attracted significant attention to the applications of pet robots which can be used to treat and entertain people in their homes. However, pet robots are fabricated using hard materials and it is difficult for them to communicate with people through contact. Soft robots are expected to realize communication through contact similar to that of actual pets. Soft robots provide people with a sense of healing and security owing to their softness and can extract rich information through external stimuli by applying a machine learning framework called physical-reservoir computing. It is crucial to determine the differences between the physical properties of soft materials that affect the information extracted from a soft body to develop an intelligent soft robot. In this study, two owl-shaped soft robots with different softnesses were developed to analyze the characteristics of the signal data obtained via piezoelectric film sensors embedded in models with different physical properties. An accuracy of 94.2% and 95.9% was obtained for touched part classification using 1D CNN and logistic regression models, respectively. Additionally, the relationship between the softness of material and classification performance was investigated by comparing the distribution of part classification accuracy for different hyper-parameters of two owl models.
Investigating the Role of Artificial Intelligence in Building Smart Contact on Block-Chain

Demand of block-chain is increasing day by day. On other hand smart contract are frequently used in commercial application. Artificial intelligence mechanism is capability to making things intelligent. Proposed paper is investigation role of artificial intelligence in development of smart contract that are block chain based. Deep learning approach is the feature of Artificial intelligence that could make the smart contract running on block chain more efficient and smart. Several existing researches that are made in area of block chain and AI have been considered in present research. The issues faced in previous research are their limited scope and lack of accuracy and performance. The proposed work is supposed to provide better solution in term of accuracy and performance.
Discriminative Visual Similarity Search with Semantically Cycle-consistent Hashing Networks

Deep hashing has great potential in large-scale visual similarity search due to its preferable efficiency in storage and computation. Technically, deep hashing for visual similarity search inherits the powerful representation capability of deep neural networks, and it encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this field mainly focus on building the relationship between the visual and objective hash spaces, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic, and hashing spaces, leading to a serious semantic ignorance problem during space transformation. In this article, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHNs), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a new semantic space is conceived to strengthen the flexibility and completeness of categories in the semantic feature inference phase. At the same time, a transitive latent space is formulated to explore and uncover the shared semantic interactivity embedded in visual and semantic features. Moreover, to further ensure semantic consistency across multiple spaces, we propose to build a cyclic adversarial learning module to preserve and keep their semantic concurrence during space transformation. Notably, our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in a single-modal visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms. The source codes of this article are available at https://github.com/JalinWang/SCHN.
Naming of Libraries: Terminological Aspects

The article considers terminology used by the professional community for the analysis of names of public libraries. The concepts “onomastics”, “nomination”, “brand”, “branding”, “naming”, “brand-naming” are used in the marketing of library and information activities in Russia and abroad. The author notes that in the specialized literature in the USA, the problem of naming the library-information sphere is not covered in detail, and often gives way to general branding issues. The paper concludes that the special interest in the name of domestic libraries is a cultural phenomenon emanating from the value-historical context of Russia. The author stresses the relevance of this article because the naming becomes one of the most important elements of marketing strategy of organization. The increased research interest in this topic demonstrates the problem of terminological disunity. In this regard, generalization of library names (personalized, conceptual and toponymic) is substantiated, as well as the use of the term “naming” as the universal and most correct designation of activities for the creation and functioning of an effective name of an organization, product or service. Libraries bearing the names of famous writers and other famous representatives of culture and art have personalized names. The conceptual version of the name is associated with the reflection of the priority direction of the activity of the cultural institution, as well as with associative names, the basis of which is a special image. Toponymic names characterize the relationship with the territory where the library is located. Due to the active increase of libraries that have conceptual or toponymic names, the question arises about the correct terminological generalization of cultural institutions that do not have numbered designations. The author concludes that the terminological aspects of naming will be the subject of further discussions in the professional library community, because the well-established term “named library” is currently perceived as a cultural institution with a personalized name. The article is of interest to the Russian professional community, especially to librarians, library scientists and specialists of cultural universities implementing educational programs in the direction of LIS (“Library and Information Science activity”).
AI Chatbot for Educational Service Improvement in the Post-Pandemic Era: A Case Study Prototype for Supporting Digital Reading List

This paper describes the development of an educational artificial intelligence (AI) chatbot prototype to support teachers in developing digital reading lists for their students. The chatbot aims to teach users how to use an educational system – Talis Aspire – effectively by giving them quick answers to questions, offering demonstrations and instructions on how to complete essential tasks on Talis Aspire, advising them how to solve problems and providing solutions for common issues. We have presented the prototype, together with the approach we used to design and develop it by considering the concepts of ‘Recontextualisation’ and ‘Quality Function Deployment’. We argue that the use of chatbot technology can not only help tutors develop online education and teaching materials but may also improve the quality of educational services during and after the COVID-19 pandemic. A number of recommendations and further work suggested by domain experts have also been highlighted in this paper in order to improve our prototype further.
Development and Analysis of Educational Virtual Reality System Using Static Image

With the rapid improvement of computer hardware performance, the price of computer and its ancillary equipment is also reduced. Virtual reality technology is a method of simulating the real world through the use of computers. It can meet the needs of situational and natural learning media interaction; so, it has a huge application potential in the field of education. Life is altered by technological advancements. The advent of virtual reality technology has opened up new avenues for traditional education. Its inception allows for the advancement and innovation of traditional teaching tools. Simultaneously, as a new teaching aid, it plays an important role in piquing students’ interest in learning and improving teaching effectiveness. In terms of simulation, it creates a three-dimensional world for users to interact with and reflect changes in physical objects in real time. Participants can get a sense of direct participation and exploration of the role and change of virtual objects in their environment by engaging in realistic perceptual behaviors such as viewing, listening, touching, and touching.
Predicting mineralization and targeting exploration criteria based on machine-learning in the Serra de Jacobina quartz-pebble-metaconglomerate Au-(U) deposits, São Francisco Craton, Brazil

Defining mineral exploration criteria is a laborious, time-consuming, and generally an empirical task often biased and limited to expert knowledge. To address this problem with a different approach, we used data-driven analysis to make predictions and provide insights about gold mineralization in rocks of the Jacobina Group, São Francisco Craton. The input variables were petrophysical parameters (density, magnetic susceptibility, and electric conductivity) and lithogeochemistry data obtained by X-Ray Fluorescence assays. A machine learning model based on the Random Forests algorithm was applied to predict mineralization in drill core samples. The database used for algorithm training was balanced using the Borderline-SMOTE technique to provide approximately the same numbers of samples of the two classes in the mineral status parameter (i.e., ore and barren samples). The quality of the predictions was assessed with different datasets (i.e., training, testing, each drill core separately, and all samples) and by parameters. The average accuracies were 0.87 for cross-validation training, 0.91 for testing, and 0.86 for all samples. Also, the model allowed us to estimate and rank the importance of the input variables to the prediction. These estimates were validated by an interpretation of optical and scanning electron microscopy petrographic analysis, which was carried out to understand the relationship between minerals of different stages and gold mineralization. Thus, the techniques applied in this work are helpful to decreasing the time spent in data integration and interpretation, since mineral exploration teams can easily replicate this approach.
Skeletal Action Recognition with Local Joint Perimeter Maps Learned Using Deep Metric Embedding

Developing a highly accurate 3D action recognition framework using traditional feature maps on top of conventional neural networks has been limited by inter class discriminations of similar looking action sequences. We approach this problem through local joint Perimeter maps (LJPM) on skeletal action datasets that are learned by deep metric learning (DML) process. We propose to solve gaps in training pipelines to attain higher accuracies using a feature embedding space which is learned using the triplet loss function. To test our approach, we applied our 3D motion captured action dataset, KLHA3D-102 and two other benchmarks, HDM05 and NTU RGB D. The results obtained show that the embedding features performed better due to triplet loss which maximized the separation between multiple classes similar features. Further it the triplet loss embedding has minimal false positive effects on 3D skeletal action data recognition tasks.
Virtual Veracity: Animated Documentaries and Mixed Realities

Do we currently exist in animated realities? And how does virtualized culture shape new forms of animated documentaries? This paper analyzes non-photorealistic animated documentaries in the 21st century in relation to the virtualization of culture. I discuss animation as a leading virtual aesthetic, map out three kinds of virtual documentaries and explain animation&#x0027;s importance as documentary imagery in an era of mixed realities, i.e. the merging of the physically and digitally real. I cover animated documentaries of online game realities; the use of games as a platform to create cinematic documentaries about non-game physical realities; and the rise of VR and documentary games. These three forms of animated hybrid documentaries each use animation differently and contribute to the discourse about animation&#x0027;s use and status as a believable non-fiction aesthetic in today&#x0027;s media sphere. My main claims are that a) animation is a central visual language for virtual environments due to its dynamic nature and ability to reflect interactive user input in real-time; b) since online animated platforms function as animated realities and often converge with physical reality, today&#x0027;s mixed realities require animation as a documentary aesthetic that exceeds the capacity of photography as more traditional documentary imagery.