Reviewerly/exHarmony huggingface dataset:
Application of Machine learning for Prediction of Solar Microgrid System

The paper shows a new and robust system implemented to predict the power generation from photovoltaic (PV) over many sets of solar power field sites in the European Union (EU). By depending on the dataset collected from these sites that have many parameters. Many of these parameters are numerical weather prediction (NWP) can use for short-term forecasting. The regression technique is utilized with features selection based on the correlation matrix. Using the proposed prediction strategy, a smaller number of these features were more informative for the prediction system and fit with continuous targets faster and accurately. Thus, improve the accuracy of the coefficient of determination CoD ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> ), chi-square test ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\chi^{2}$</tex> ), and reduce processing time.
A magnetic domain wall Mackey–Glass oscillator

We propose a time-delay oscillator using Mackey–Glass nonlinearity based on a pinned magnetic domain wall in a thin film nanostrip. Through spin transfer torques, electric currents applied along the strip cause the domain wall to deform and displace away from a geometrical pinning site, which can be converted into a nonlinear transfer function through a suitable choice of a readout. This readout serves as a delay signal, which is subsequently fed back into the applied current with amplification. With micromagnetics simulations, we study the role of the readout position, time delay, and feedback gain on the dynamics of this domain wall. In particular, we highlight regimes in which self-sustained oscillations and complex transients are possible.
Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

Development of accessible and cheap sensors as well as the possibility to transfer and process huge amounts of data offer new possibilities for many areas utilizing till now conventional approaches. Navigation of robots and autonomous vehicles is no exception in this aspect and Internet of Things (IoT), together with the means of computational intelligence, represents a new way for construction and use of robots. In this paper, the possibility to move sensors from robots to their surroundings with the help of IoT is presented and the modification of the IoT concept in the form of intelligent space as well as the concept of ubiquitous robot are shown in the paper. On an example of route tracking, we will clarify the potential of distributed networked sensors and processing their data with the use of fuzzy cognitive maps for robotic navigation. Besides, two modifications of adaptation approaches, namely particle swarm optimization and migration algorithm, are presented here. A series of simulations was performed, which are discussed and future research directions are proposed.
Inference to the Best Explanation (IBE) and Archaeology: Old Tool, New Model

The last decades have seen a renewed interest in the study of argumentation in archaeology, particularly in response to the overproduction of weak and unreliable interpretations and explanations. Concurrently, recent appeals for scientific transparency and efficiency in the management of archaeological information in digital form have stressed the necessity of explicitly showing the processes followed. A growing body of literature has identified inference to the best explanation (IBE) as the most adequate way of interpreting archaeological data, although it has quietly existed for over a century. Despite this, the investigation of IBE-based models for recording archaeological reasoning remains a largely under-researched topic. The author concludes with a novel IBE-based model for recording archaeological argumentation.
Strings and Things: A Semantic Search Engine for news quotes using Named Entity Recognition

Emerging methods for content delivery such as quote-searching and entity-searching, enable users to quickly identify novel and relevant information from unstructured texts, news articles, and media sources. These methods have widespread applications in web surveillance and crime informatics, and can help improve intention disambiguation, character evaluation, threat analysis, and bias detection. Furthermore, quote-based and entity-based searching is also an empowering information retrieval tool that can enable non-technical users to gauge the quality of public discourse, allowing for more fine-grained analysis of core sociological questions. The paper presents a prototype search engine that allows users to search a news database containing quotes using a combination of strings and things. The ingestion pipeline, which forms the backend of the service, comprises of the following modules i) a crawler that ingests data from the GDELT Global Quotation Graph ii) a named entity recognition (NER) filter that labels data on the fly iii) an indexing mechanism that serves the data to an Elasticsearch cluster and iv) a user interface that allows users to formulate queries. The paper presents the high-level configuration of the pipeline and reports basic metrics and aggregations.
Cross-Domain Retrieval in the Legal and Patent Domains: A Reproducibility Study

Domain specific search has always been a challenging information retrieval task due to several challenges such as the domain specific language, the unique task setting, as well as the lack of accessible queries and corresponding relevance judgements. In the last years, pretrained language models – such as BERT – revolutionized web and news search. Naturally, the community aims to adapt these advancements to cross-domain transfer of retrieval models for domain specific search. In the context of legal document retrieval, Shao et al. propose the BERT-PLI framework by modeling the Paragraph-Level Interactions with the language model BERT. In this paper we reproduce the original experiments, we clarify pre-processing steps and add missing scripts for framework steps, however we are not able to reproduce the evaluation results. Contrary to the original paper, we demonstrate that the domain specific paragraph-level modelling does not appear to help the performance of the BERT-PLI model compared to paragraph-level modelling with the original BERT. In addition to our legal search reproducibility study, we investigate BERT-PLI for document retrieval in the patent domain. We find that the BERT-PLI model does not yet achieve performance improvements for patent document retrieval compared to the BM25 baseline. Furthermore, we evaluate the BERT-PLI model for cross-domain retrieval between the legal and patent domain on individual components, both on a paragraph and document-level. We find that the transfer of the BERT-PLI model on the paragraph-level leads to comparable results between both domains as well as first promising results for the cross-domain transfer on the document-level. For reproducibility and transparency as well as to benefit the community we make our source code and the trained models publicly available.
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

Human-designed data augmentation strategies have been replaced by automatically learned augmentation policy in the past two years. Specifically, recent work has empirically shown that the superior performance of the automated data augmentation methods stems from increasing the diversity of augmented data [4], [5]. However, two factors regarding the diversity of augmented data are still missing: 1) the explicit definition (and thus measurement) of diversity and 2) the quantifiable relationship between diversity and its regularization effects. To bridge this gap, we propose a diversity measure called Variance Diversity and theoretically show that the regularization effect of data augmentation is promised by Variance Diversity. We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity. An unsupervised sampling-based framework, DivAug, is designed to directly maximize Variance Diversity and hence strengthen the regularization effect. Without requiring a separate search process, the performance gain from DivAug is comparable with the state-of-the-art method with better efficiency. Moreover, under the semi-supervised setting, our framework can further improve the performance of semi-supervised learning algorithms compared to RandAugment, making it highly applicable to real-world problems, where labeled data is scarce. The code is available at https://github.com/warai-0toko/DivAug.
RRT based path planning for mobile robots on a 3D surface mesh

Path planning is one of the fundamental problems in robotics. Due to the advancement in technology, the application of mobile robots has increased in recent years, not only in the field of robotics, but also in other domains such as computational biology, computer animation and aerospace. Path planning in high dimensional environments for mobile robots is known to be computationally challenging, but since the introduction of the sampling-based planning algorithms such as rapidly exploring random tree (RRT) and probabilistic roadmap (PRM), solving high dimensional path planning problems has became easier. In this paper, we present a mesh-based RRT path planning approach. Connecting RRT tree nodes on a nonplanar surface mesh requires the computation of geodesics, shortest length paths on the mesh, which can create a high computational load. The proposed method reduces the number and length of geodesics on the mesh. Simulation results show that this method finds a feasible path faster than the basic RRT on the mesh.
Mesh Saliency: An Independent Perceptual Measure or A Derivative of Image Saliency?

While mesh saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and is well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art mesh saliency methods remain poor at predicting human fixations. Cues emerging prominently from these experiments suggest that mesh saliency might associate with the saliency of 2D natural images. This paper proposes a novel deep neural network for learning mesh saliency using image saliency ground truth to 1) investigate whether mesh saliency is an independent perceptual measure or just a derivative of image saliency and 2) provide a weakly supervised method for more accurately predicting mesh saliency. Through extensive experiments, we not only demonstrate that our method outperforms the current state-of-the-art mesh saliency method by 116% and 21% in terms of linear correlation coefficient and AUC respectively, but also reveal that mesh saliency is intrinsically related with both image saliency and object categorical information. Codes are available at https://github.com/rsong/MIMO-GAN.
Examining the materiality and spatiality of design scaffolds in computational making

As researchers focus more on engaging youth with advanced technologies to build computational skills, there is a need to develop tools that support youth to become creators of digital content rather than consumers of these technologies. Scholars refer to this shift in becoming creators of technology as computational making, which enables new forms of learning and thinking at the intersection of the physical and the digital. The interdisciplinary and multidimensional nature of computational making design processes means learners must negotiate their design idea across space, time, mode, and expertise. In this paper, we draw on multiple iterations of design and implementation work with wearable and locative technologies to investigate the affordances and challenges of design scaffolds in youths’ computational making design processes. We present examples from learning with electronic textiles and locative game design to illustrate the unique problem spaces of materiality and spatiality specific to computational making. Our insights highlight a significant need for sharing scholarship and design work on implementing design scaffolds in computational making contexts toward the collective goal of developing a system of design scaffolds to effectively support learners’ thinking across the interdisciplinary, multimodal problem spaces unique to computational making.
BNPA: An R package to learn path analysis input models from a data set semi-automatically using Bayesian networks

Epidemiologists constantly search for methodologies that help them better understand how diseases work. Populations urge these improvements to combat these diseases more effectively. The literature presents several authors defending the idea that epidemiologists should be able to develop causal models. In this area, the technique of structural equation models (SEM) has stood out in scientific research. Although SEM has been widely used in several research areas, it has been little explored by epidemiologists. Despite its evolution and efficiency, SEM has a gap in terms of discovering causalities. To fill this gap, this study developed an R package called BNPA, whose methodology joins the best of Bayesian network structural learning algorithms (BNSL) from data and path analysis (PA) a SEM subarea. The BNPA was built with pre-processing functions. Its main algorithm allows creating an input model to start the PA from a data set semi-automatically generating information to analyze the PA performance. An analysis of cardiovascular disease’s main predictors was performed using the BNPA with data from the Canadian Community Health Survey (CCHS). Multiple linear regression (MR) was used as a gold standard methodology; the results of BNPA matched 85% of MR results. In conclusion, BNPA is efficient and can benefit researchers, mainly novices, by enabling them to build PA models from data. Furthermore, statisticians and PA experts will have more time to support these researchers instead of creating an initial model.
On Approximation of Concept Similarity Measure in Description Logic <i>ELH</i> With Pre-Trained Word Embedding

Data-driven and knowledge-driven methods are two mainstream techniques in the pursuit of developing artificial intelligence systems. While data-driven methods seek to develop a decision model from observations in the real world, they are difficult to provide an explanation for the results in human terms. On the other hand, knowledge-driven methods that employ symbolic reasoning based on formal semantics of a knowledge-base are thus more interpretable and explainable, while lacking an ability to deal with incomplete modeling of the structured knowledge-bases. This work aims to tackle these issues on ontology similarity by proposing a general framework that combines the strengths of both approaches for measuring semantic similarity of concepts in a description logic (DL) ontology. More specifically, a neuro-symbolic integrated framework is defined to exploit the pre-trained word embeddings with semantic definitions in an ontology to yield an explainable degree of concept similarity. To demonstrate its applicability, we develop a concrete similarity measure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {\textsf {sim}}_\epsilon $ </tex-math></inline-formula> conforming to the proposed framework and also introduce an efficient algorithm that can extract an explanation for why such a degree is indicated. The correctness is shown by analyzing theoretical properties that it guarantees to preserve and also by performing an empirical evaluation with a medical ontology SNOMED CT and a medical pre-trained embedding BioWordVec. The results show that our proposed method remains both interpretability and explainability while achieving comparable performance, relative to the state-of-the-art approaches in the data and knowledge-driven methods.
Lattice-based unidirectional infinite-use proxy re-signatures with private re-signature key

In a proxy re-signature scheme, a semi-trusted proxy can convert Alice's (also called as delegatee's) signature into Bob's (also called as delegator's) signature on the same message. However, the proxy itself cannot produce any signatures on behalf of either Alice or Bob. There exists some unidirectional one-use and multi-use (a message can be re-signed a polynomial number of times) proxy re-signature schemes. In some scenarios of big data, it is required to design unidirectional infinite-use (a message can be re-signed infinite number of times) proxy re-signature schemes. In this paper, we propose the first unidirectional infinite-use proxy re-signature scheme and identity-based unidirectional infinite-use proxy re-signature scheme with private re-signature keys based on lattice and prove that they are secure in the random oracle model.
Faster Index Reordering with Bipartite Graph Partitioning

We revisit the Bipartite Graph Partitioning approach to document reordering (Dhulipala et al., KDD 2016), and consider a range of algorithmic and heuristic refinements that lead to faster computation of index-minimizing document orderings. Our final implementation executes approximately four times faster than the reference implementation we commence with, and obtains the same, or slightly better, compression effectiveness on three large text collections.
An automated integrated speech and face imageanalysis system for the identification of human emotions

Human interactions are related to speech and facial characteristics. It was suggested that speech signals and/or images of facial expressions may reveal human emotions and that both interact for the verification of a person's identity. The present study proposes and evaluates an automated integrated speech signal and facial image analysis system for the identification of seven different human emotions (Normal (N), Happy (H), Sad (S), Disgust (D), Fear (F), Anger (A), and Surprise (Su)). Speech recordings and face images from 7,441 subjects aged 20≤age≤74 were collected, normalized and filtered. From all the above recordings 55 speech signal features and 61 different image face texture features were extracted. Statistical and model multi-classification analysis were performed to select the features able to statistically significantly distinguish between the seven aforementioned human emotions (N, H, S, D, F, A and Su). The selected features alone or a combination of these features along with age and gender of the sample investigated were used to build two learning-based classifiers; and the classifiers' accuracy was computed. For each of the above mentioned human emotions, statistical significantly different speech and face image features were identified that may be used to distinguish between the aforementioned groups (N vs H, N vs S, N vs D, N vs F, N vs A, N vs Su). Using solely the statistically significant speech and image features identified, an overall percentage of correct classification (%CC) score of 93% was achieved. A significant number of speech and face image features have been derived from continuous speech and face images. Features were identified that were able to identify between seven different emotional human states. This study poses the basis for the development of an integrated system for the identification of emotional states from automatic analysis of free speech and image face analysis. Future work will investigate the development and integration of the proposed method into a mobile device.
Improving the state-tracking ability of corona dashboards

Corona dashboards are interactive geospatial information systems used by billions of users to help them understand the evolution of the COVID-19 pandemic. I use a representational lens to explore how these systems can be made more useful. With this lens, the usefulness of these systems to convey information about the pandemic fundamentally depends on whether these systems are implemented as representation or state-tracking systems. I suggest that corona dashboards presently focus disproportionally on representing socially constructed properties (infection rates, deaths, levels of vaccination) of various things such as people, regions, or countries. They would become more useful if they additionally focused on tracking events (such as policy implementations) and changes in states (such as capacities of lockdown wards, usage of face masks). By applying a methodology for design science research involving design archaeology, I analyse the in situ implementation of Germany’s RKI COVID-19-Dashboard, develop new design principles to extend the state-tracking abilities of corona dashboards, and explore the importance, actability, and effectiveness of these design principles through an empirical case study. The contributions this paper makes are new and validated design principles for new feature implementations that can help making corona dashboards more effective and useful.
S-PoDL: A two-stage computational-efficient consensus mechanism for blockchain-enabled multi-access edge computing

More recently, there has been a growing interest in the blockchain technique for emerging multi-access edge computing (MEC) applications in relation to security and privacy, considering the benefits of using it in edge computing. In some blockchain-enabled MEC using the best-known consensus algorithm proof-of-work (PoW), the computational efforts increase dramatically with the number of transactions, since a large amount of computational tasks should be conducted by miners with the limited computing resources in edge devices. Hence, improving the computational performance as an important but challenging issue in the design of blockchain system for MEC applications, has attracted intensive attention within last years. To further improve the performance of PoW algorithm, we present a novel implementation mechanism in this paper. Here, motivated by proof-of-deep-learning (PoDL) method in which the deep learning algorithm is used to maintain blockchain, through the design of a two-stage model to achieving computational tasks in PoDL-based blockchain systems, a novel computational-efficient consensus mechanism, named separate-proof-of-deep-learning (S-PoDL), is accordingly proposed. Thus, an energy-efficient blockchain-enabled MEC could be developed with our proposed S-PoDL, which arranges miners to carry out a two-stage-based computation on the basis of accounting_queue technique, while presenting the achieved models as proofs in MEC network. The comparative experiments are conducted between S-PoDL and PoDL, and the experimental results verify the feasibility and efficiency of our consensus mechanism S-PoDL for some blockchain-enabled MEC applications in relation to PoW-based cryptocurrencies, while effectively reducing computing burden of edge devices.
Short-term power load forecasting of GWO-KELM based on Kalman filter

Short-term power load forecasting plays a significant role in power system security management. The prediction model in this paper is the grey wolf optimization algorithm to optimize kernel extreme learning machine (GWO-KELM). First, the Kalman filter is used to reduce the noise for the random noise interference existing in the power load data. Then determine the input and output of the prediction model. In this paper, the ELM model of three different kernel functions is used for comparative experiments, and the mean absolute percentage error is used as the evaluation model index. It is concluded from the experimental results that the GWO-KELM model used in this article has the advantages of high prediction accuracy and strong generalization ability, so it is practicable to apply the model to short-term electric load forecasting.