Reviewerly/exHarmony huggingface dataset:
Validation of Digital Learning Media to Improve the Basic Literacy Skills of Low-Grade Elementary School Students

This research was motivated by the low literacy ability of students caused by less interesting learning media. This study aims to create and know the validation of digital learning media to improve students' basic literacy skills in integrated thematic learning in low-grade elementary schools. research starts from the Preliminary Research stage, Prototyping Stage, Assessment Stage. The instruments used in this study were interviews and validation sheets for research samples were students, data collection techniques were carried out using questionnaires and interviews. Based on the problems found in elementary schools, learning media is designed using digital learning media. The following results were obtained after being validated by five validators: content feasibility, presentation feasibility, linguistic feasibility, and graphic feasibility each fall into the excellent category. If the validity value is averaged, then the digital learning media developed is in the range of very valid categories.
Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination

With the development of information extraction technology, a variety of entity-relation extraction paradigms have been formed. However, approaches guided by these existing paradigms suffer from insufficient information fusion and too coarse extraction granularity, leading to difficulties extracting all triples in a sentence. Moreover, the joint entity-relation extraction model cannot easily adapt to the relation extraction task. Therefore, we need to design more fine-grained and flexible extraction methods. In this paper, we propose a new extraction paradigm based on existing paradigms. Then, based on it, we propose SSPC, a method for Span-based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination. SSPC first decomposes the task into three sub-tasks, namely S,R Extraction, R,O Extraction and S,R,O Classification and then uses prompt tuning to fully integrate entity and relation information in each part. This fine-grained extraction framework makes the model easier to adapt to other similar tasks. We conduct experiments on joint entity-relation extraction and relation extraction, respectively. The experimental results show that our model outperforms previous methods and achieves state-of-the-art results on ADE, TACRED, and TACREV.
Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature Recommendation

Tremendous academic articles face serious information overload problems while supporting literature searches. Finding a research article in a relevant domain that meets researchers’ requirements is challenging. Hence, different paper recommendation models have been proposed to address this issue. However, these models lack a more comprehensive analysis of the connections between the literature, the domain knowledge provided, and the hotspot information expressed in the literature. Previous models make it impossible to locate the appropriate documents for domain literature. Additionally, these models encounter problems such as cold start papers and data sparsity. To overcome these problems, this paper presents a recommendation model termed PRHN. Inputs of the model are the hotspot information network and the domain knowledge graph, which both were developed during the preceding research phase. After the query terms are extracted and the associated heterogeneous literature networks are formed, they are aggregated in a uniform hidden space. Similarity with the candidate set is determined to transform the search problem into a TOP N recommendation problem. Compared to state-of-the-art models, results generated by PRHN on public available datasets show improvement in HR and NDCG. Concretely, results on the metallurgical literature dataset are more conspicuous, with more remarkable improvement in HR and NGCC by approximately 4.5% and 4.2%.
Machine Learning-Based GPR with LBFGS Kernel Parameters Selection for Optimal Throughput Mining in 5G Wireless Networks

Considering the ever-growing demand for an efficient method of deductive mining and extrapolative analysis of large-scale dimensional datasets, it is very critical to explore advanced machine learning models and algorithms that can reliably meet the demands of modern cellular networks, satisfying computational efficiency and high precision requirements. One non-parametric supervised machine learning model that finds useful applications in cellular networks is the Gaussian process regression (GPR). The GPR model holds a key controlling kernel function whose hyperparameters can be tuned to enhance its supervised predictive learning and adaptive modeling capabilities. In this paper, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) with kernel parameters selection (KPS) algorithm is employed to tune the GPR model kernel hyperparameters rather than using the standard Bayesian optimization (BOP), which is computationally expensive and does not guarantee substantive precision accuracy in the extrapolative analysis of a large-scale dimensional dataset. In particular, the hybrid GPR–LBFGS is exploited for adaptive optimal extrapolative learning and estimation of throughput data obtained from an operational 5G new radio network. The extrapolative learning accuracy of the proposed GPR–LBFGS with the KPS algorithm was analyzed and compared using standard performance metrics such as the mean absolute error, mean percentage error, root mean square error and correlation coefficient. Generally, results revealed that the GPR model combined with the LBFGS kernel hyperparameter selection is superior to the Bayesian hyperparameter selection method. Specifically, at a 25 m distance, the proposed GPR–LBFGS with the KPS method attained 0.16 MAE accuracy in throughput data prediction. In contrast, the other methods attained 46.06 and 53.68 MAE accuracies. Similarly, at 50 m, 75 m, 100 m, and 160 m measurement distances, the proposed method attained 0.24, 0.18, 0.25, and 0.11 MAE accuracies, respectively, in throughput data prediction, while the two standard methods attained 47.46, 49.93, 29.80, 53.92 and 47.61, 52.54, 53.43, 54.97, respectively. Overall, the GPR–LBFGS with the KPS method would find valuable applications in 5G and beyond 5 G wireless communication systems.
An augmented reality-based virtual chemistry laboratory to support educational and research activities of 11th grade students

The organization of high school students' research and learning activities in chemistry requires a significant amount of experimental work. The performance of experimental work in chemistry at school faces a number of problems, both purely material and organizational, to which the need for distance learning has recently been added. This article describes the state of use of augmented reality technologies in modern chemistry education and the possibilities of using augmented reality technologies to support students' learning and research activities in chemistry. To solve the research tasks, cloud-based augmented reality development tools (A-Frame and AR.js) were used. The developed tool is a virtual chemistry laboratory using augmented reality technologies: individual markers correspond to reagents, and a pairwise combination of markers triggers a video recording of chemical interaction between the corresponding pair of reagents. The article describes the development of augmented reality software to support eleventh graders' learning and research activities in chemistry in the form of an augmented reality-based virtual chemistry laboratory and its implementation in the teaching process.
Red Flag/Blue Flag visualization of a common CNN for text classification

A shallow convolutional neural network (CNN), TextCNN, has become nearly ubiquitous for classification among clinical and medical text. This research presents a novel eXplainable-AI (X-AI) software, Red Flag/Blue Flag (RFBF), designed for binary classification with TextCNN. RFBF visualizes each convolutional filter's discriminative capability. This is a more informative approach than direct assessment of logit contribution, features that overfit to train set nuances on smaller datasets may indiscriminately activate large logits on validation samples from both classes. RFBF enables model diagnosis, term feature verification, and overfit prevention. We present 3 use cases of (1) filter consistency assessment; (2) predictive performance improvement; and (3) estimation of information leakage between train and holdout sets. The use cases derive from experiments on TextCNN for binary prediction of surgical misadventure outcomes from physician-authored operative notes. Due to TextCNN's prevalence, this X-AI can benefit clinical text research, and hence improve patient outcomes.
UCDM: A User-Centric Data Model in Power Blockchain

Background: As innovative information technology, blockchain has combined the advantages of decentralization, immutability, data provenance, and contract operation automatically, which can be used to solve the issues of single point failure, high trading cost, low effectiveness, and data potential risk in power trading. However, in the traditional power blockchain, the design of functional components in blockchain, such as the data structure of the block, does not take the actual features of power into account, thus leading to a performance bottleneck in practical application. Motivated by business characteristics of power trading, a user-centric data model UCDM in consortium blockchain is proposed to achieve efficient data storage and quick data retrieval. Methods: The proposed UCDM is designed by considering the requirements of transaction retrieval and analysis, thus supporting the requirements of concurrent data requests and mass data storage. The ID of each user will independently form its own chain over the blockchain. Results: Compared with the traditional data model, the extensive experimental results demonstrate that the proposed UCDM has shorter processing delay, higher throughput, and shorter response latency, thus having practical value. Conclusion: UCDM is an effective solution to the transaction retrieval and analysis in power blockchain. Furthermore, the participant of the blockchain network has a unique identity over the world, which ensures high security during trading.
eIDAS Interoperability and Cross-Border Compliance Issues

The eIDAS Regulation provides a common foundation for secure electronic interaction between citizens, businesses, and public authorities. We investigated and identified interoperability and cross-border compliance issues in this paper. We have identified the following weaknesses: Organizational independence, remote access to banking services, remote video identification, use of electronic signatures in public administration, commercial access to the eIDAS network, biometric authentication mechanisms, and, finally, some technical issues with the mechanisms used to provide security and authentication in eIDAS nodes.
Application of Fractional Differential Model in Image Enhancement of Strong Reflection Surface

Combined with advanced fractional differential mask operation, this paper used a fractional differential to normalize the 5 × 5 mask and conducted experiments to select fractional v = 0.7 to determine the equation. The position of the center of the light band was obtained by the gray centroid method, and the center of the light band was enhanced by the fractional differential algorithm. Three samples of hard disk substrate, roller, and printed circuit board were selected. The traditional processing was compared to the fractional differential algorithm in this paper, and several advanced algorithms were compared with the algorithm in this paper. Experimental data showed that fractional differential enhancement can effectively improve the accuracy of extracting the center of light fringes. It can be found that the average error of extracting the center by fractional differential processing was relatively small, and the image outline was clearer.
Weak Interactions, Strong Bonds: Live electronics as a complex system

This article examines works for live, interactive electronics from the perspective of complex dynamic systems, placing the human–computer interaction within a wider set of relationships. From this perspective, composing equates to constructing a complex system with the performer(s) and the computer as key players within a wider network of interdependence. Using the author’s own compositions as examples, this article investigates the utility of a system view on interactive, live electronics.
Saliency Detection Based on Low-Level and High-Level Features via Manifold-Space Ranking

Saliency detection as an active research direction in image understanding and analysis has been studied extensively. In this paper, to improve the accuracy of saliency detection, we propose an efficient unsupervised salient object detection method. The first step of our method is that we extract local low-level features of each superpixel after segmenting the image into different scale parts, which helps to locate the approximate locations of salient objects. Then, we use convolutional neural networks to extract high-level, semantically rich features as complementary features of each superpixel, and low-level features, as well as high-level features of each superpixel, are incorporated into a new feature vector to measure the distance between different superpixels. The last step is that we use a manifold space-ranking method to calculate the saliency of each superpixel. Extensive experiments over four challenging datasets indicate that the proposed method surpasses state-of-the-art methods and is closer to the ground truth.
Investigating the capability of Sentinel-2 and Worldview-3 VNIR satellite data to detect mineralized zones at an igneous intrusion in the Koutala islet (Lavreotiki, Greece) using laboratory mineralogical analysis, reflectance spectroscopy and spectral indices

During the last decades, the rapid progress of remote sensing data processing for their utilization in detecting locations of possible sites linked to hydrothermal alteration and ores has gained increasing attention due to significant time and cost savings. In this study, we present the findings of a joint remote sensing and laboratory examination of a mineralization zone induced by a granitoid intrusion onshore the islet of “Koutala”, in Lavreotiki, central Greece. Our objective is to investigate the potential of Sentinel-2 and WorldView-3 VNIR satellite data to detect and map oxidized ore zones and alteration minerals that are detected from laboratory analysis of samples and could be linked to Fe-Mn mineralization. Two approaches are followed, namely reflectance spectroscopy and spectral indices. In reflectance spectroscopy, the spectral signatures of all minerals detected in the study area are retrieved from spectral libraries. The signatures are resampled to Sentinel-2 and to WorldView-3 VNIR spectral bands. Continuum-removal is then applied and the diagnostic absorption features of each mineral are detected for each spectral band configuration. The dataset with the best spectral configuration for mineral detection is then used for the production of mineral maps using the corresponding satellite image. The second approach involves the calculation of spectral indices, namely ferric, ferrous iron and hydroxyl-bearing alteration, on reflectance spectra. The ferric iron index is applied to both satellite datasets while the two other indices require the use of SWIR bands and therefore, they can be only calculated on Sentinel-2 data. All results show that laboratory and satellite data analyses results are consistent and complementary. WorldView-3 VNIR data seem to be sensitive only to the ferric and manganese phase. Sentinel-2 data seem to be capable to detect and map all alteration minerals that are potentially linked to Fe-Mn ore, including both ferric and ferrous phases. The mineral absorption and spectral indices maps show that in the investigated area, there is significant mineralization related to the granitoid intrusion. Hydrothermal alteration is observed on the entire surface of the islet but it seems to be stronger at the eastern part of the islet where the granitoid/schist contact is located. It is the first time that (i) minerals linked to a potential Fe-Mn ore are detected on the islet and (b) the corresponding alteration mineral maps are produced from satellite data, revealing their spatial distribution and providing indirect estimations of the degree of their presence.
Micro-Expression Spotting Based on a Short-Duration Prior and Multi-Stage Feature Extraction

When micro-expressions are mixed with normal or macro-expressions, it becomes increasingly challenging to spot them in long videos. Aiming at the specific time prior of micro-expressions (MEs), an ME spotting network called AEM-Net (adaptive enhanced ME detection network) is proposed. This paper is an extension of the conference paper presented at the Chinese Conference on Biometric Recognition (CCBR). The network improves spotting performance in the following five aspects. Firstly, a multi-stage channel feature extraction module is constructed to extract the features at different depths. Then, an attention spatial-temporal module is leveraged to obtain salient and discriminative micro-expression segments while suppressing the generation of excessively long or short suggestions. Thirdly, a ME-NMS (non-maximum suppression) network is developed to reduce redundancy and decision errors. Fourthly, a multi-scale feature fusion module is introduced to fuse up-sampling features of high-level maps and fine-grained information, which obtains meaningful information on feature distribution and contributes to a good representation of MEs. Finally, two spotting mechanisms named anchor-based and anchor free were integrated to get final spotting. Extensive experiments were conducted on prevalent CAS(ME)2 and the SAMM-Long ME databases to evaluate the spotting performance. The results show that the AEM-Net achieves competitive performance, outperforming other state-of-the-art methods.
Glocal Retriever: Glocal Image Retrieval Using the Combination of Global and Local Descriptors

Development of deep learning has led to progress in computer vision, including metric learning tasks such as image retrieval, through convolutional neural networks. In image retrieval, the metric distance (i.e., the similarity) between the images needs to be computed and then compared to return similar images. Global descriptors are good at extracting holistic features of an image, such as the overall shape of the main object and the silhouette. On the other hand, the local features extract the detailed features which the model uses to help classify similar images together. This paper proposes a descriptor mixer which takes advantage of both local and global descriptors (group of features combined into one) as well as different types of global descriptors for an effect of a lighter version of an ensemble of models (i.e., fewer parameters and smaller model size than those of actual ensemble of networks). As a result, the model’s performance improved about 1.36% (recall @ 32) when the combination of the descriptors were used. We empirically found out that the combination of GeM and MAC achieved the highest performance.
TrEKBQA:Traversing Knowledge Graph Embedding for Multi-hop Knowledge Base Question Answering

Abstract Recent research apply KG embedding to multi-hop Knowledge Base Question Answering(KBQA) to predict missing links, however, it is often affected by the skewed distribution of nodes in the knowledge graph, resulting in poor generalization of the model. Therefore, we propose a method TrEKBQA based on traversing the knowledge graph embedding space for multi-hop KBQA, which performs path traversal in the KG embedding space instead of KG itself for link prediction to complete the knowledge graph, thus improving the accuracy of multi-hop KBQA.TrEKBQA model complex relationships using correlations between individual links and longer paths connecting the same pair of entities to traverse the KG embedding space to mitigate the effects of biased distribution of nodes and improve the performance of link prediction. In the pre-processing process, TrEKBQA uses the PRN algorithm to extract subgraphs related to the problem entity to reduce the number of target entities. Through experiments on multiple benchmark datasets, we demonstrate the effectiveness of TrEKBQA on KBQA tasks.
The Role of DevOps in Sustainable Enterprise Development

This chapter considers the problem of sustainable enterprise development from the perspective of the organization of software development. The goal is to provide a comprehensive analysis of the characteristics of the DevOps approach and present how it enables organization of continuous software development. By utilizing these DevOps principles, an enterprise can quickly adapt to ever-changing business requirements while fostering an environment capable of sustainable growth. This growth is primarily enabled by a shift in culture, where teams are encouraged to be functionally diverse which allows them to consider the whole lifecycle of an activity, rather than one single aspect. A case study of a university research and development center is presented to demonstrate the peculiarities arising from the DevOps approach. Conclusions and recommendations for future works are drawn from the literature analysis and own experiences.
Energy Production Analysis of Rooftop PV Systems Equipped with Module-Level Power Electronics under Partial Shading Conditions Based on Mixed-Effects Model

The rooftop photovoltaic (PV) system that uses a power optimization device at the module level (MLPE) has been theoretically proven to have an advantage over other types in case of reducing the effect of partial shading. Unfortunately, there is still a lack of studies about the energy production of such a system in real working conditions with the impact of partial shading conditions (PSC). In this study, we evaluated the electrical energy production of the PV systems which use two typical configurations of power optimization at the PV panel level, a DC optimizer and a microinverter, using their real datasets working under PSC. Firstly, we compared the energy utilization ratio of the monthly energy production of these systems to the reference ones generated from PVWatt software to evaluate the effect of PSC on energy production. Secondly, we conducted a linear decline model to estimate the annual degradation rate of PV systems during a 6-year period to evaluate the effect of PSC on the PV’s degradation rate. In order to perform these evaluations, we utilized a mixed-effects model, a practical approach for studying time series data. The findings showed that the energy utilization ratio of PVs with MLPE was reduced by about 14.7% (95% confidence interval: −27.3% to −2.0%) under PSC, compared to that under nonshading conditions (NSC). Another finding was that the PSC did not significantly impact the PV’s annual energy degradation rate, which was about −50 (Wh/kW) per year. Our finding could therefore be used by homeowners to help make their decision, as a recommendation to select the gained energy production under PSC or the cost of a rooftop PV system using MLPE for their investment. Our finding also suggested that in the area where partial shading rarely happened, the rooftop PV system using a string or centralized inverter configuration was a more appropriate option than MLPE. Finally, our study provides an understanding about the ability of MLPE to reduce the effect of PSC in real working conditions.