Reviewerly/exHarmony huggingface dataset:
Perception-Oriented Stereo Image Super-Resolution

Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of super-resolved stereo images. To improve the perceptual performance, this paper proposes the first perception-oriented stereo image super-resolution approach by exploiting the feedback, provided by the evaluation on the perceptual quality of StereoSR results. To provide accurate guidance for the StereoSR model, we develop the first special stereo image super-resolution quality assessment (StereoSRQA) model, and further construct a StereoSRQA database. Extensive experiments demonstrate that our StereoSR approach significantly improves the perceptual quality and enhances the reliability of stereo images for disparity estimation.
CobotAR: Interaction with Robots using Omnidirectionally Projected Image and DNN-based Gesture Recognition

Several technological solutions supported the creation of interfaces for Augmented Reality (AR) multi-user collaboration in the last years. However, these technologies require the use of wearable devices. We present CobotAR -a new AR technology to achieve the Human-Robot Interaction (HRI) by gesture recognition based on Deep Neural Network (DNN) - without an extra wearable device for the user. The system allows users to have a more intuitive experience with robotic applications using just their hands. The CobotAR system assumes the AR spatial display created by a mobile projector mounted on a 6 DoF robot. The proposed technology suggests a novel way of interaction with machines to achieve safe, intuitive, and immersive control mediated by a robotic projection system and DNN-based algorithm. We conducted the experiment with several parameters assessment during this research, which allows the users to define the positives and negatives of the new approach. The mental demand of CobotAR system is twice less than Wireless Gamepad and by 16% less than Teach Pendant.
Study of Pre-trained Language Models for Named Entity Recognition in Clinical Trial Eligibility Criteria from Multiple Corpora

Named entity recognition (NER) is fundamental to the computerization of clinical trial eligibility criteria text. In this study, we fine-tuned pretrained contextual language models to support the NER task on clinical trial eligibility criteria. We systematically explored four pre-trained contextual embedding models for biomedical domain (i.e., BioBERT, BlueBERT, PubMedBERT, and SciBERT) and two models for the open domain (BERT and SpanBERT), for NER tasks using three existing clinical trial eligibility criteria corpora. Our evaluation results showed domain-specific transformer models achieved better performance than the general transformer models, with the best performance obtained by the PubMedBERT model (F1-scores of 0.715, 0.836, and 0.622 for the three corpora respectively. This study demonstrated the efficiency of domain-specific transformer-based language models for NER in clinical trial eligibility criteria.
Missing Data Imputation for Solar Yield Prediction using Temporal Multi-Modal Variational Auto-Encoder

The accurate and robust prediction of short-term solar power generation is significant for the management of modern smart grids, where solar power has become a major energy source due to its green and economical nature. However, the solar yield prediction can be difficult to conduct in the real world where hardware and network issues can make the sensors unreachable. Such data missing problem is so prevalent that it degrades the performance of deployed prediction models and even fails the model execution. In this paper, we propose a novel temporal multi-modal variational auto-encoder (TMMVAE) model, to enhance the robustness of short-term solar power yield prediction with missing data. It can impute the missing values in time-series sensor data, and reconstruct them by consolidating multi-modality data, which then facilitates more accurate solar power yield prediction. TMMVAE can be deployed efficiently with an end-to-end framework. The framework is verified at our real-world testbed on campus. The results of extensive experiments show that our proposed framework can significantly improve the imputation accuracy when the inference data is severely corrupted, and can hence dramatically improve the robustness of short-term solar energy yield forecasting.
Safest Route Detection via Danger Index Calculation and K-Means Clustering

The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one.The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results.Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results.we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users.The successful implementation would hopefully aid us to curb the ever-increasing crime rates; as it aims to provide the user with a beforehand knowledge of the route they are about to take.A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer.Thus, addressing a social problem which needs to be eradicated from our modern era.
A progressive development of a visual analysis interface of climate-related VGI

Abstract This paper describes the progressive development of three approaches of successively increasing analytic functionality for visually exploring and analysing climate-related volunteered geographic information. The information is collected in the CitizenSensing project within which urban citizens voluntarily submit reports of site-specific extreme weather conditions, their impacts, and recommendations for best-practice adaptation measures. The work has pursued an iterative development process where the limitations of one approach have become the trigger for the subsequent ones. The proposed visual exploration approaches are: an initial map application providing a low-level data overview, a visual analysis prototype comprising three visual dashboards for more in-depth exploration, and a final custom-made visual analysis interface, the CitizenSensing Visual Analysis Interface (CS-VAI), which enables the flexible multifaceted exploration of the climate-related geographic information in focus. The approaches developed in this work are assessed with volunteered data collected in two of the CitizenSensing project’s campaigns held in the city of Norrköping, Sweden.
Aspect-Driven User Preference and News Representation Learning for News Recommendation

Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs), ensuring smooth interactions and improving the driving experience. Listening to news is a popular method of relaxing during a journey; as a result, travelers require automatic recommendations of preferred news programs. Most existing news recommender systems usually learn topic-level representations of users and news for recommendations while neglecting to learn more informative aspect-level features, resulting in limited recommendation performance. To bridge this significant gap, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preferences and news representation learning. In ANRS, a news aspect-level encoder and a user aspect-level encoder are devised to learn the fine-grained aspect-level representations of users’ preferences and news characteristics respectively. These representations are subsequently fed into a click predictor to predict the probability of a given user clicking on the candidate news item. Extensive experiments demonstrate the superiority of our method over state-of-the-art baseline methods.
English literature students' perspectives on digital resources in a Spanish university

Digital resources have been more widely used in the university classroom since the Spring semester of 2020, but the reality is that Covid-19 simply accelerated an already leading tendency in education. The pedagogical potential of teaching with digital resources, editions and collections remains largely unexplored in the Spanish university context, especially in relation to the study of literary and historical texts. This article reports on a case study at the University of Alicante. An online questionnaire was dispensed to 134 English Studies undergraduates at Alicante in order to elicit the students' responses to electronic resources, their knowledge, uses and attitudes towards them. We also attempted to gauge their engagement with the library services at Alicante and beyond. The results indicate a statistically significant preference for electronic over print resources. Nevertheless, they also suggest the students' low degree of awareness of digital resources and their inability to discriminate between digital resources and general Internet sources. A lack of familiarity with the full potential that libraries offer is also reported, which stresses the need for further training in what seems to be a neglected but crucial area of their studies.
Knowledge Structure of the Application of High-Performance Computing: A Co-Word Analysis

As high-performance computing (HPC) plays a key role in the Fourth Industrial Revolution, the application of HPC in various industries is becoming increasingly important. Several studies have reviewed the research trends of HPC but considered only the functional aspects, causing limitations when discussing the application. Thus, this study aims to identify the knowledge structure of the application of HPC, enabling practical and policy support in various industrial fields. Co-word analysis is mainly used to establish the knowledge structure. We first collected 28,941 published papers related to HPC applications and built a co-word network that used author keywords. We performed centrality analysis and cluster analysis of the co-word network; as a result, we derived the major keywords and 18 areas of HPC applications. To validate the knowledge structure, we conducted a case study to find opportunities for HPC research plans in the research community. As a result, we discovered 17 new research topics and presented their research priorities by conducting expert interviews and Analytic Hierarchy Process. The findings of this study contribute to an understanding of the application of HPC, to exploring promising research fields for technological and social development, and to supporting research plans for successful technology commercialization.
A Study on Key Components of Problem-Based Learning through Literature Review

With problem-solving capacity continuously gathering prominence in professional field, Korean universities are deviating from traditional rote-teaching to a teaching method which ensures development in problem-solving ability among students. As part of several new teaching methods, problem-based learning (PBL) has emerged as an effective means for education. For successful implementation of PBL within university curricula, pinpointing components required for PBL program is necessary, and yet essential components of PBL have not been specifically identified according to studies. Thus, this study conducted a literature review to identify key components which are necessary for effective application of PBL in university courses.
Scaling Neural Program Synthesis with Distribution-Based Search

We consider the problem of automatically constructing computer programs from input-output examples. We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search. Within this framework, we introduce two new search algorithms: Heap Search, an enumerative method, and SQRT Sampling, a probabilistic method. We prove certain optimality guarantees for both methods, show how they integrate with probabilistic and neural techniques, and demonstrate how they can operate at scale across parallel compute environments. Collectively these findings offer theoretical and applied studies of search algorithms for program synthesis that integrate with recent developments in machine-learned program synthesizers.
ARCADIS: Asynchronous Remote Control-Flow Attestation of Distributed IoT Services

Remote attestation (RA) is a security mechanism that verifies the trustworthiness of remote IoT devices. Traditional RA protocols aim to detect the presence of malicious code in the static memory of a device. In the IoT domain, RA research is currently following two main directions: Dynamic RA and Swarm RA. Dynamic RA schemes intend to detect runtime attacks that hijack the control-flow execution of a running program without injecting new malicious code into the memory. On the other hand, swarm RA protocols focus on attesting efficiently and effectively a large number of IoT devices. However, existing RA protocols do not perform dynamic attestation in asynchronous IoT networks. This paper proposes a RA protocol for Asynchronous Remote Control-Flow Attestation of Distributed IoT Services (ARCADIS). This protocol extends the state-of-the-art by detecting IoT devices that have (directly or indirectly) been maliciously influenced by runtime attacks on asynchronous distributed IoT services. The protocol has been simulated for Wismote sensors in the Contiki emulator. The conducted experiments confirm the feasibility of ARCADIS and demonstrate its practicality for small IoT networks.
Application of Traditional Cultural Symbols in Art Design under the Background of Artificial Intelligence

In order to solve the declining influence of traditional cultural symbols, the research on traditional cultural symbols has become more meaningful. This article aims to study the application of traditional cultural symbols in art design under the background of artificial intelligence. In this paper, a fractal model with self-combined nonlinear function changes is constructed. By combining nonlinear transformations and multiparameter adjustments, various types of fractal models can be automatically rendered. The convolutional neural network algorithm is used to extract the characteristics of the style picture, and it is compared with the trained picture many times to avoid the problem of excessive tendency of the image with improper weight. The improved L-BFGS algorithm is also used to optimize the loss of the traditional L-BFGS, which improves the quality of the generated pictures and reduces the noise of the chessboard. The experimental results in this paper show that the improved L-BFGS algorithm has the least loss and the shortest time in the time used for more than 500 s. Compared with the traditional AdaGrad method, its loss is reduced by about 62%; compared with the traditional AdaDelta method, its loss is reduced by 46%. Its loss is reduced by about 8% compared with the newly optimized Adam method, which is a great improvement.
Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.
Novel Cooperative Automatic Modulation Classification Using Unmanned Aerial Vehicles

Automatic modulation classification (AMC) has been intriguing many researchers as it has many civil and military applications. Recently, cooperative AMC (CAMC) using a dynamic or ad hoc sensor network becomes appealing and challenging. As the unmanned aerial vehicles (UAVs) can facilitate three-dimensional communication/sensor network, we propose a novel CAMC approach based on a dynamic (ad hoc) UAV network. In our proposed new CAMC approach, the local classification decisions, which are made by spatially distributed nodes (UAVs) using our previously proposed graph-based modulation classifier, are gathered to reach an overall decision by a new weighted voting mechanism pertinent to individual received signal qualities. Note that the fusion center does not have to be a fixed UAV and it can be dynamically reassigned to any UAV within the same network in each sensing interval. The corresponding weights to individual UAVs are to be determined according to their cumulative states and the temporal discount factor. As a result, our proposed new CAMC approach can be fully distributed as no control center (or hub) is necessary. Besides, our new CAMC scheme can accommodate realistic ad hoc network variations to allow the existing UAVs to depart and/or the new UAVs to join in any sensing interval. Monte Carlo simulation results demonstrate that our proposed new CAMC scheme is quite robust and outperforms the existing CAMC method.
Image retrieval using Nash equilibrium and Kalai-Smorodinsky solution

In this paper, we propose a new formulation of Nash games for solving a general multi-objectives optimization problems. The objective of this approach is to split the optimization variables, allowing us to determine numerically the strategies between two players. The first player minimizes his function cost using the variables of the first table P and the second player, using the second table Q. The original contribution of this work concerns the construction of the two tables of allocations that lead to a Nash equilibrium on the Pareto front. The second proposition of this paper is to find a Nash Equilibrium solution, which coincides with the Kalai--Smorodinsky solution. Two algorithms that calculate P, Q and their associated Nash equilibrium, by using some extension of the normal boundary intersection approach, are tried out successfully. Then, we propose a search engine to look for similar images of a given image based on multiple image representations using Color, Texture and Shape Features.
Agriculture 5.0 in India

The welfare of farmers has been the agenda for elections in world’s largest democracy since 1947. Political parties made fortunes by exploiting the fragile economic status of farmers and making promises of farmer dare in every election, just to repeat them in next election, but with nothing changing for farmers. Urban workers earn eight times more than agricultural worker in India. Technology can bring prosperity to Indian farmers and reduce poverty. Blockchain is a distributed ledger of transactions and digital events shared among the participating parties. Blockchain technology and financial technology (fintech) can provide cost effective accessibility of finance, insurance, smart contracts, e-payments, weather updates, land registrations, supply chain, and future trading solutions. An enhanced income for farmers offers a multiplier effect by boosting the demand for products, services, and investment. Better supply chains will reduce supply shocks and control inflation. This chapter reviews the synergy of existing information and communication technology (ICT) infrastructure in India, stable and technologically well informed central government, predominantly young demographic profiles and an emerging start-up culture for transition to Agriculture 5.0 by developing blockchain and fintech solutions for the agriculture sector. Blockchain is relatively unexplored technology in India. Blockchain is hard to implement due to complexity and lacks regulatory clarity. Political will and vision can make blockchain and economic prosperity a reality for Indian farmers. A care model is proposed to enhance the adoption of technology in India for Agriculture 5.0.
The Relationships among Quality of Online Education, Learning Immersion, Learning Satisfaction, and Academic Achievement in Cooking-Practice Subject

The purpose of this study was to identify the problems of online education perceived by learners in a time when non-face-to-face online lectures are conducted due to the spread of COVID-19, and suggest measures to improve educational outcomes. Therefore, the structural influence of cooking practice on the quality of online lessons, learning immersion, learning satisfaction, and the achievement felt by learners was verified. The sample of the study was a college student majoring in cooking, and a total of 509 responses were used for analysis. The results of the study are as follows: First, it was analyzed that there was a significant influence on learning immersion in the order of content, interaction, and educational environment among the quality of online classes. Second, among the quality of online classes, class content and educational environment showed positive (+) influence on learning satisfaction. Third, learning immersion and learning satisfaction showed a positive influence on academic achievement. Fourth, in the direct effect of online class quality on academic achievement, it was analyzed that only class content had a positive (+) effect. Based on the results of this study, the provision of learning materials with detailed explanations was suggested in the online cooking practice class rather than providing a simple cooking video. In addition, it was suggested that as a way to improve academic achievement of online education, it is necessary to provide content that allows learners to immerse themselves in class, as well as class content and educational environment, and consider learning satisfaction.