Reviewerly/exHarmony huggingface dataset:
LFC-SASR: Light Field Coding Using Spatial and Angular Super-Resolution

Light field imaging enables post-capture actions such as refocusing and changing view perspective by capturing both spatial and angular information. However, capturing richer information of the 3D scene results in a huge amount of data. To improve the compression efficiency of the existing light field compression methods, we investigate the impact of light field super-resolution approaches (both spatial and angular super-resolution) on the compression efficiency. To this end, firstly, we downscale light field images over (i) spatial resolution, (ii) angular resolution, and (iii) spatial-angular resolution and encode them using Versatile Video Coding (VVC). We then apply a set of light field super-resolution deep neural networks to reconstruct light field images in their full spatial-angular resolution and compare their compression efficiency. Experimental results show that encoding the low angular resolution light field image and applying angular super-resolution yield bitrate savings of 51.16% and 53.41% to maintain the same PSNR and SSIM, respectively, compared to encoding the light field image in high-resolution.
Superpixel-Based Optimization for Point Cloud Reconstruction from Light Field

As one of the most popular immersive data formats, point cloud has attracted attention due to its flexibility and simplicity. The advantage of multi-view in light field can capture rich scene information and be effectively applied to 3D point cloud reconstruction. However, existing point cloud reconstruction methods based on light field depth estimation often cause outlier points at the edges of objects, which greatly disturbs the visual effects. In this paper, we propose a superpixel-based optimization scheme for point cloud, which is reconstructed from light field. We use the superpixel-based edge detection algorithm and designed joint bilateral filter to optimize the blurred edge of the depth map, which is obtained from the EPI-based depth estimation. Then, minimal residual outlier points are removed by statistical outlier filter after point cloud generating. Experimental results show that the proposed method increases at least 25.8% in level of details (LoD) compared with several state-of-the-art methods for the real-world and synthetic light field datasets. Besides, the proposed method can restore outlier points reliably and retain the sharp features of point cloud.
Visual retrieval of digital media image features based on active noise control

In order to improve the recall and precision of image retrieval, a visual retrieval method of digital media image features based on active noise is proposed. In this paper, Canny algorithm is used to detect the edge of the image to get the feature information of the edge of the image. The RGB color space model is used to decompose the color information of the image, and the color characteristic information of the image is obtained. Extracting image features to retain the useful information contained in the image as much as possible; In order to facilitate the visual retrieval of image features, reduce the retrieval complexity and further fuse image features, FPCA and ReliefF algorithms are used to reduce the dimensionality of image features, and the active noise control method is used to sharpen the image. After processing the results, a digital media image feature visual retrieval platform is established to realize the visual retrieval of digital media image features. Experimental results show that the proposed method has a high accuracy of over 80% and a high recall rate of 95.2%.
Theme mining and quantitative evaluation of library policies

Library is an important part of the public cultural service system. Library policy is a major force that can not be ignored. As the carrier of cultural inheritance, library plays an important role in cultivating and practicing core values and promoting civilized practice in the new era. Excavating the theme of the library is the basis for determining the development and service direction of the library. The library policy is facing difficulties such as unclear construction direction and inadequate guidance. Under this background, the research on China’s library policy is carried out. With the library as an example, the topic mining and quantitative analysis of library policies in the eastern, central, and western regions of my country from 2011 to 2020 were conducted using the LDA topic model. Meanwhile, the differences between the three regions were investigated. The results demonstrate that: (1) In terms of quantity, policy texts in the eastern region account for more than half of them; (2) Regarding keywords, high-frequency words mainly include culture, openness, and information in the eastern region, numbers, resources, and reading in the central region, and students, teaching, and learning in the western region; (3) The hot topics in the eastern region involve library government affairs, library digital resources, and library service mode, that is, strengthening the construction of library digital resources and improving service level with an open attitude; (4) The hot topics in the central region contain library management, library student training, and library security, suggesting that the aspects of internal management and system security are emphasized to train students and guide the modernization of the library; (5) The hot topic in the western region includes the construction of two libraries in one station for library evaluation management and library procurement, implying to promote library reform from the bottom up and make crucial arrangements for creating a new situation for grass-roots libraries. On this basis, the policy support of government departments for library development in the future should focus on formulating regulations and rules under the Library Law, establishing a dynamic balance between supply and demand, and enhancing exchanges and cooperation between libraries in the east, middle, and west.
Effects of Difficulties Experienced on Self-Efficacy and Attributions in a Study Task With Tablet

The study examined how the facilities and difficulties experienced by learners in a search task on tablet can have an effect on the change of learners' self-efficacy about tablet use.In addition, the study investigated the nature of the causal attributions learners did about their facilities or difficulties to perform the task.Ninety-nine undergraduate student had to search information in a document and writing their answers on a tablet.Half of participants were in a highcompatibility condition (i.e.tablet's functions allowed to reach and answer more easily), while the other half was in the low-compatibility condition (i.e.document implied more difficulties and some functions were missing).The findings confirmed a decrease of self-efficacy for learners experiencing difficulties and revealed dysfunctional attribution.
Examining the Impact of Instructional Conversation on Students' Writing Through Topic Model Analysis

Instructional conversation (IC) is a small-group, conversation-based pedagogy designed to provide structured opportunities for students to deeply analyze complex problems.IC helps facilitate independent reading and writing by activating reading comprehension and reflection through collaborative discussions.A statistical topic model analysis, latent Dirichlet allocation, was used to analyze the latent structure of 3 rd and 5 th grade students' answers to short and long answer constructed response questions.Preliminary findings indicate students in IC classrooms were more likely to address multiple perspectives, address most or all parts of the prompt and display more complex reasoning abilities.Students who did not receive IC were more likely to restate the prompt, use redundant language, and not display deeper levels of thought and reasoning ability. ObjectivesThis study was designed to explore how Instructional Conversation (IC; Goldenberg, 1993) may impact students' writing abilities.We analyzed students' writing through use of latent Dirichlet allocation (LDA; Blei, Ng & Jordan, 2003).LDA detects the latent thematic structure in a corpus.In this study, LDA was used to detect the latent structure for student's answers on an essay test.Analyzed individual and group latent topic structures as well as individual grades on the essay test.The main research questions were 1.Does IC have an impact on students' essay test answers?2. Are there differences in topic structures between students who have been taught IC framework and those who have not?
Sentiment Analysis Methods: Survey and Evaluation

This paper establishes a rigorous framework for sentiment analysis applications in finance. First, we present a thorough review of the latest academic literature, including both methodological advancements and findings. Second, we categorise and analyse the different sentiment analysis techniques, from lexicon-based approaches, to machine learning, and the latest powerful Bidirectional Encoder Representations from Transformers (BERT) models. Third, we conduct an empirical evaluation of the performance of 31 different sentiment analysis methods based on a sample of 20,000 Glassdoor company reviews, using a variety of different metrics and across seven different experimental settings. We show that BERT models perform the best, followed by the machine-learning models (although here there is considerable variance between the different word embeddings and classifiers), and lastly the lexicon-based approaches. The experiments also show that all models perform best when assessing the sentiment of positive or negative statements or distinguishing between one and five star reviews, but more caution must be employed when including more ambiguous cases (two star and four star reviews) or using a multi-class sentiment division (positive, negative or neutral) rather than binary. The majority of previous analysis of sentiment from Glassdoor reviews has focused on the star ratings but our findings suggest that analysis of the sentiment of textual comments may add further nuance.
The use of weighted self-organizing maps to interrogate large seismic data sets

SUMMARY Modern microseismic monitoring systems can generate extremely large data sets with signals originating from a variety of natural and anthropogenic sources. These data sets may contain multiple signal types that require classification, analysis and interpretation: a considerable task if done manually. Machine learning techniques may be applied to these data sets to expedite and improve such analysis. In this study, we apply an unsupervised technique, the Self-Organizing Map (SOM), to high-volume data recorded by an in-mine microseismic network. This represents a good example of a large seismic data set that contains a wide range of signals, owing to the diversity of source processes occurring within the mine. The signals are quantified by extracting a number of features (temporal and spectral) from the waveforms which are provided as input data for the SOM. We develop and implement a weighted variant of the SOM in which the contributions of various different features to the training of the map are allowed to evolve. The standard and weighted SOMs are applied to the data, and the output maps compared. Both variants are able to separate source types based on the waveform characteristics, allowing for rapid, automatic classification of signals and the ability to find sources with similar waveforms. Fast classification of such signals provides practical benefit by automatically discarding waveforms associated with anthropogenic sources within the mine while seismic signals originating from genuine microseismic events, which constitute a small fraction of all signals, can be prioritized for subsequent processing and analysis. The weighted variant provides an exploratory tool through quantification of the contribution of different features to the clustering process. This helps to optimize the performance of the SOM through the identification of redundant features. Furthermore, those features that are assigned large weights are considered to be more representative of the source generation processes as they contribute more to the cluster separation process. We apply weighted SOMs to data from a mine recorded during two different time periods, corresponding to different stages of the mine development. Changes in feature importance and in the observed distribution of feature values indicate evolving source generation processes and may be used to support investigatory analysis. The weighted SOM therefore represents an effective tool to help manage and investigate large seismic data sets, providing both practical benefit and insight into underlying event mechanisms.
Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Basketball is a sport with a wide range of complicated human movements, and the ability to accurately identify these movements is critical in both competition and training. Training athletes is largely based on the subjective observations and experiences of the coaches. Artificial intelligence and big data technologies can be used to monitor athlete training. It can also assist coaches in making decisions which significantly increase the athletic ability by detecting their movements. This research work proposed a mechanism called BSTARNet which is an action recognition method for basketball sports training. The method is based on artificial neural network (ANN), and network is trained through basketball sports big data. First, this work uses the Convolution Long Short-Term Memory (ConvLSTM) unit to extract the spatiotemporal information features of basketball sports training from videos. Second, this work establishes the Attention Long Short-Term Memory (AttLSTM) unit that combines the attention mechanism with the LSTM. The unit selectively scans each location, giving more attention to the area where the action takes place. Finally, the network framework is built by improving the ordinary encoder-decoder model. After that, the spatiotemporal information contained in the video is encoded based on the Darknet network model. In the decoding stage, the AttLSTM structure is used to replace the ordinary LSTM. These units are combined to form the BSTARNet architecture. Experiments are conducted to verify the effectiveness of the proposed method applied on action recognition in basketball sports training and achieved 89.5% mAP and 95.4% accuracy.
The minimum number of discriminant equations for a symmetric game

The discriminant equations with the minimum number for a symmetric game are derived. By using a new version of the semi-tensor product method based on adjacent transpositions, the analysis of symmetric games is greatly simplified. Some new necessary and sufficient conditions for symmetric games are proposed, a basis of the symmetric game space is easily calculated, and the computing complexity is achieved. Finally, two examples are displayed to show the effectiveness of the proposed method.
Hospital Intelligent Power Operation and Maintenance Information Evaluation with the Long and Short Memory Neural Network

The invention describes a deep learning-based technique for monitoring power grid information operation and maintenance. Based on the time series data information in the power grid information operation and maintenance monitoring system, this method obtains the cleaned time series data through appropriate data preprocessing technology. This also uses the long-term and short-term memory neural network to realize the prediction function of the time series data to be detected. The reason behind is to construct the normal behavior model of the time series. Additionally, the control chart based on an exponentially weighted moving average is employed to determine whether the time series to be discovered contains any anomalous and abnormal phenomena. In the area of power grid information operation and maintenance monitoring, the method of invention is designed to counter anomaly. This phenomenon is universal in nature and has significant scientific importance for guiding treatment after the anomaly is discovered. Additionally, it guards against serious faults that the abnormality might bring about.
Design and Mixed-Reality Teleoperation of a Quadruped-Manipulator Robot for SAR Tasks

Technological development within the robotics field has made it possible to generate great advances, developing complex bio-inspired systems, such as robots with legs. The same ones have great applicability within search and rescue (SAR) tasks. This type of robot stands out for its extraordinary ability to move within unstructured environments, overcome obstacles and adapt to different terrains. However, quadruped robots have gained space in the SAR-Tasks field over the last few years. Most implementations are limited to collecting information using different sensors, such as cameras, lasers, or microphones. This article seeks to simulate, implement and carry out the teleoperation through Mixed-Reality (M-R) of a quadruped robot equipped with a manipulator with six degrees of freedom, for which the ARTU-R robot (A1 Rescue Tasks Unitree Robot) has been used. One of the main contributions of this work focuses on improving the efficiency in executing tasks of handling and transporting medical equipment in post-disaster situations, using Mixed Reality with the ARTU-R robot and a robotic manipulator. To develop this proof of concept, Matlab has been used as a computational tool for optimizing the workspace of the integrated robot. Simulations have been carried out on Gazebo in reconstructed post-disaster environments to validate the robot's functionality. At the same time, the effectiveness of the M-R system has been verified with field tests executing medical assistance tasks with the robot. The main results show a 21% increase in the efficiency of performing complex handling tasks using the proposed M-R system compared to conventional interfaces and the efficiency of using quadruped robots with manipulators for medical assistance tasks.
Learning Energy-Efficient Trotting for Legged Robots

Quadrupedal locomotion skills are challenging to develop. In recent years, Deep Reinforcement Learning (DRL) promises to automate the development of locomotion controllers and map sensory observations to low-level actions. However, legged locomotion still is a challenging task for DRL algorithms, especially when energy efficiency is taken into consideration. In this paper, we propose a DRL scheme for efficient trotting applied on Laelaps II quadruped in MuJoCo. First, an accurate model of the robot is created by revealing the necessary parameters to be imported in the simulation, while special focus is given to the quadruped’s drivetrain. Concerning, the reward function and the action space, we investigate the best way to integrate in the reward, the terms necessary to minimize the Cost of Transport (CoT) while maintaining a trotting locomotion pattern. Last, we present how our solution increased the energy efficiency for a simple task of trotting on level terrain similar to the treadmill-robot environment at the Control Systems Lab [1] of NTUA.
Digital Competence for Citizenship: Distance Learning Before and During the Covid-19 Emergency

The Covid-19 emergency is undoubtedly an exceptional situation that has forced us all to reshape and transform spaces, times and activities of all kinds. Even the school, driven by need and emergency, has re-thought its teaching practices through a re-evaluation of digital technology and organizational experimentation. An opportunity to keep relationships alive, beyond distance. The presented study analyzes the perceptions of a sample of secondary school teachers about online learning and the use of new technologies, comparing the teaching practices adopted in the pre-pandemic Covid-19 period, during the lockdown and during the return to presence. The results highlight both the critical issues that emerged during distance learning – digital divide, accessibility, workload, support for students with Special Educational Needs, the teacher-learner relationship, digital skills – and the positive aspects – flexibility, availability of tools, adaptability, innovation, student self-determination and self-regulation. This is all in line with a media education that allows the potential of digital devices to be exploited in education and to support the digital skills of teachers and pupils.
Mountain Hamlet Heritage Between Risk and Enhancement

This paper focuses on villages and hamlets on the mountain considered historical, cultural heritage. Due to their cultural and historical values, they need documentation, enhancement, and revaluation activities. The research aims to semantically and spatially describe minor historical centres such as hamlets affected by natural hazards and risk phenomena (landslide, avalanches, …). In this domain, many actors and use cases are involved in different activities such as urban and landscape planning, restoration, tourism, etc. For this reason, it is necessary to develop a replicable and sharable workflow able to harmonise and integrate different sources (spatial, structured, unstructured, historical and so on) to document these centres thoroughly. A significant case study has been selected to apply the presented methodology. It is the hamlet of Pomieri, in the municipality of Prali (Piedmont Region, Alps, Germanasca Valley, Italy). Different documents about natural hazards phenomena have been investigated parallelly to semantic and ontologies sources. This method set the basis for developing a knowledge base, including further information on such centres subject to risk and needing documentation, restoration, preservation, maintenance and planning actions.
A Multicriteria Evaluation of Blockchain-Based Agrifood Chain in the New Scenario Post-Covid 19

The agrifood supply chain is one of the largest in the world and includes all the businesses aimed at the production, processing, distribution and sale of products. The purpose of this article is to clearly explain what the blockchain technology really is, how it performs and whether it can really help our agriculture. It seems particularly useful to bring a knowledge support to institutional operators, agricultural enterprises, rural communities, activists, in the belief that it is essential to introduce in the current and future debate a critical vision that remains open to comparison and observation of the evolution of the 4.0 revolution that has just begun. In this context, blockchain technology can play an important role in the exchange of goods and services, which allows to democratize the procurement process, thus making the relationship between small farmers and large buyers more equitable. The objective of the research is to analyze which scenarios will benefit most from the implementation of blockchain in the citrus chain. The multi-criteria indices are representative of the variability in the citrus supply chain based on confirmed trends is safety, security, and traceability in the overall process for orange juice, from fruit to bottle.