Reviewerly/exHarmony huggingface dataset:
Transparency and Involvement of the Energy-Related Industry in a Data Sharing Platform

The integration of renewable energy sources, the decentralization of the energy system, and the increasing digitization of energy-related processes require the integration of a wide range of energy-related data. In this context, a data sharing platform can serve as a hub for exchanging energy-related data and developing innovative solutions to improve the efficiency and sustainability of the energy system. However, especially because of the involvement of the energy-related industry in such a platform poses several challenges related to data protection, intellectual property, and business interests. This paper presents a framework for ensuring transparency and involvement of the energy-related industry in a data sharing platform, based on the FAIR data principles and a co-creation approach involving industry partners.
A FAIR Future for Engineering Sciences

The emergence of FAIR data management (FDM) is being witnessed in more and more disciplines, including the engineering sciences. However, until recently, little academic credit has been given for the work that sound FDM practices in research publications require. Moreover, there has been a lack of space where the engineering sciences community could discuss and share experiences, ideas and advice about this topic. In academia, a suitable platform for such information exchange are journals. In this publication, the concept behind ing.grid, the newly established open access journal for FDM in engineering sciences, is presented, illuminating how these challenges can be addressed by providing a platform for the publication of manuscripts, research data, and software as well as by incorporating open peer review.
Horizontal Attention Based Generation Module for Unsupervised Domain Adaptive Stereo Matching

The emergence of convolutional neural networks (CNNs) has led to significant advancements in various computer vision tasks. Among them, stereo matching is one of the most popular research areas that enables the reconstruction of 3D information, which is difficult to obtain with only a monocular camera. However, CNNs have their limitations, particularly their susceptibility to domain shift. The CNN-based stereo matching networks suffered from performance degradation under domain changes. Moreover, obtaining a significant amount of real-world ground truth data is laborious and costly when compared to acquiring synthetic data. In this letter, we propose an end-to-end framework that utilizes image-to-image translation to overcome the domain gap in stereo matching. Specifically, we suggest a horizontal attentive generation (HAG) module that incorporates the epipolar constraints when generating target-stylized left-right views. By employing a horizontal attention mechanism during generation, our method can address the issues related to small receptive field by aggregating more information of each view without using the entire feature map. Therefore, our network can maintain consistencies between each view during image generation, making it more robust for different datasets.
A Word Embedding Model for Fault Localization using Bug and Software Change Repositories

Software developed and then deployed in a real world environment is inevitable to exhibit some undesirable behavior. Therefore, developers need to provide maintenance facilities to enable the bugs causing the undesirable behavior to be fixed. However, prior to fixing the bug, the suspicious part of the code needs to be identified. For this purpose, they usually perform fault localization. This can be done manually as well as automatically. Several techniques exist in the literature for fault localization. However, most of them are static based techniques because they do not depend on a specific programming language along with the possibility to work on underdeveloped software and some other benefits. These techniques are largely based on lexical matching of terms which leads to mismatch of terms, large precision value because of limited vocabulary of a programming language and some techniques consider the semantics but it is computationally expensive to localize faults through this. In this paper we have proposed a fault localization technique which is based on the machine learning concept of word embedding. Our proposed approach aims at looking at the relatedness between the bug terms and source code artifact. We mined the bug repositories and software change repositories to train the word embedding model on the mined repositories data. On the arrival of a new bug, the cluster of the bugs from the model is searched and the files from the software change repositories are retrieved which are used for fixing those bugs. We have compared the results of our approach with the latest technique proposed in year 2018 Pointwise Mutual Information (PMI) and Normalized Google Distance (NGD) which consider the context and also with existing lexical techniques Vector Space Model (VSM) and the semantic based method Latent Semantic Indexing (LSI). We have used the benchmark dataset “MoreBugs” which has been widely used in this domain. The results show that our approach outperforms other techniques.
A self-supervised learning of semantic feature consistency for image clustering

Contrastive learning is a powerful technique for learning feature representations without manual annotation. The K-nearest neighbor (KNN) method is commonly used to construct positive sample pairs to calculate the contrastive loss. However, it is challenging to distinguish positive sample pairs, reducing clustering performance. We propose a novel Deep Contrastive Clustering method based on a GrapH convolutional network called GHDCC. It uses an instance-level contrastive loss with mean square error (MSE) regularization and a cluster-level contrastive loss to incorporate semantic features and perform cluster assignments. The method utilizes a graph convolutional network (GCN) to improve the semantic consistency of features and linear interpolation data augmentation to improve the representation ability of the model. To minimize the occurrence of false positive sample pairs, we select only samples whose similarity exceeds a predefined threshold to construct the adjacency matrix. The experimental results on six public datasets demonstrate that the GHDCC significantly outperforms contrastive clustering (CC, 500) by a large margin except on CIFAR-10. The GHDCC performs well compared to other deep contrastive clustering methods and achieves the highest clustering accuracy of 0.913 on ImageNet-10.
Hyper-parameter tuning for support vector machine using an improved cat swarm optimization algorithm

Support vector machine (SVM) is a supervised machine learning algorithm for classification and regression problems. SVM performs better when combined with other classifiers or optimized with an optimization algorithm. The SVM parameters such as kernel and penalty have good performance on the classification accuracy. Recently, a lot of evolutionary optimization algorithms were used for optimizing the SVM. In this paper, an Improved Cat Swarm Optimization (ICSO) was proposed for optimizing the parameters of SVM with the aim of enhancing its performance. CSOs have the problem of a low convergence rate and are easily trapped in local optima. To address this problem, a new parameter was added to the velocity for the tracing mode and the Opposition-Based Learning (OBL) technique was used to modify the CSO algorithm (ICSO-SVM). A new parameter was introduced to guide the cats’ positions to the local and global best positions in the velocity tracing mode of the CSO algorithm. The proposed algorithm was verified using 15 datasets from the University of California Irvine (UCI) data repository and also six different performance metrics were used. The experimental results clearly indicate that the proposed method performs better than the other state-of-the-art methods.
Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning

Real-time detection of satellite remote sensing images is one of the key technologies in the field of remote sensing, which requires not only high-efficiency algorithms, but also low-power and high-performance hardware deployment platforms. At present, the image processing hardware acceleration platform mainly uses an image processing unit (GPU), but the GPU has the problem of large power consumption, and it is difficult to apply to micro-nano satellites and other devices with limited volume, weight, computing power, and power consumption. At the same time, the deep learning algorithm model has the problem of too many parameters, and it is difficult to directly deploy it on embedded devices. In order to solve the above problems, we propose a YOLOv4-MobileNetv3 field programmable gate array (FPGA) deployment scheme based on channel layer pruning. Experiments show that the acceleration strategy proposed by us can reduce the number of model parameters by 91.11%, and on the aerial remote sensing dataset DIOR, the average accuracy of the design scheme in this paper reaches 82.61%, the FPS reaches 48.14, and the average power consumption is 7.2 W, which is 317.88% FPS higher than the CPU and reduces the power consumption by 81.91%. Compared to the GPU, it reduces power consumption by 91.85% and improves FPS by 8.50%. Compared with CPUs and GPUs, our proposed lightweight algorithm model is more energy-efficient and more real-time, and is suitable for application in spaceborne remote sensing image processing systems.
Morlille kan ikke flyve

This paper deals with natural language (NL) semantics an the computer. It presents two major theoretical vicwpoints in linguistic semantics: representational and denotational. In linguistic theory, these two issues are mostly understood as mutually exclusive, as can be seen from Chomskyan and Montague Grammar. The paper discusses representational semantics in connection with the autonomous syntax approach of mainstream transformational grammar, denotational semantics in connection with the model-theoretic approach of Montague grammar. In the next step, I consider the implementation of model-theoretic NL comprehension systems on the computer. In the present context, they are viewed principally as a means to make experimental studies in the formalizations of NL. It is shown that the correct handling of referential NL terms in such systems can be achicved only if these systems combine methods and techniques from both types of semantics. Finally, the cognitive meaning of the concept of computer understanding is touched upon.
Research Data Management for Experiments in Solid-State Physics: Concepts

FAIRmat develops concepts for paving the way to enable FAIR research data in solid-state physics. For selected theoretical data in this field, the NOMAD portal has developed mature concepts and technological solutions for storing data according to the FAIR principles. Extending this approach to experimental data is challenging due to their diversity and missing standards. In this paper we present our comprehensive approach to establish FAIR data in the field of experimental solid-state physics despite its heterogeneity. The concept includes elaboration of standards, community building and methods that facilitate the community’s transition to FAIR standards.
The concept of implementing distributed registry technology in the activities of wholesale distribution centers

The paper considers the conceptual aspects of the application of distributed ledger technology elements (blockchain) and their derivative smart contract for the construction of automated transactions on the example of individual business tasks of large wholesale distribution centers. In the course of the study, the elements and properties of the smart contract characteristic of the subject area under study were determined, as well as the conditions, types of business operations and the corresponding data allowing them to consider the business object state with its subsequent implementation in the smart contract transaction.
The assessment model of online vocal music teaching quality under the optimized DL model

In recent years, art disciplines have flourished in response to the requirements of the times, and online vocal music courses have been offered by major colleges and universities. The quality of education in colleges and universities is a key indicator of the quality of a school's talents, and improving its quality is the most important part of modernizing education. In this study, a new education index system is constructed to address the problems of traditional online vocal teaching quality evaluation methods, and a vocal teaching quality evaluation model based on an adaptive variant Genetic Algorithm improved Back Propagation neural network is proposed. The model is combined with the established new index system and used in the evaluation of vocal music teaching quality. Comparing the performance between the model and the model constructed by the traditional algorithm, the experimental results showed that the convergence speed of the optimized model was improved by 79.32%. The model adaptation reached convergence in the 19th time, and the adaptation value was stabilized at 1.34, which can be seen that the improved model has a stronger adaptive ability and a faster convergence speed. In summary, the results predicted by the model basically coincide with the trend of the actual evaluation results, and the existence of the error is small, indicating that the model can achieve a more comprehensive and scientific evaluation of education quality.
SSFuzzyART: A Semi-Supervised Fuzzy ART through seeding initialization and a clustered data generation algorithm to deeply study clustering solutions

Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labeled data is available. Indeed, some labeled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labeled data. This paper introduces a semi-supervised variant of the FuzzyART clustering algorithm (SSFuzzyART). The proposed solution uses the available labeled data to initialize clusters centers. In another hand, to deeply evaluate the characteristics of the proposed algorithm, a clustered binary data generation algorithm with controlled partitioning is also introduced in this paper. Indeed, the controlled generated clusters allows studying the characteristics of the proposed SSFuzzyART. Furthermore, a set of experiments is carried out on some available benchmarks. SSFuzzyART demonstrated better clustering prediction results than its classic counterpart.
Data use policies on state COVID-19 dashboards in the United States: Key characteristics, topical focus, and identifiable gaps

In this paper, we report the findings of an investigation into the data use policies published on the COVID-19 dashboards developed by the 50 state governments of the United States as well as the government of District of Columbia. Specifically, we examined the key attributes of the dashboard data notes, such as data source, update frequency, and data suppression disclaimers. We also studied the terms and phrases used, as well as topic themes of the data policy texts. Using a data policy analysis model, our results revealed a series of gaps and inconsistencies in the policy statements. Connecting these gaps and inconsistencies with potential problems that could violate individual Open Data Principles (ODP) and the FAIR principles, we made recommendations to help resolve these missing areas and fix the inconsistencies, so that open government data can be managed and used to further the very core of open data practice. Further research that we plan to carry out includes confirmation and validation of our analysis model and our approach of linking the examination and assessment of open data policy with ODP and the FAIR principles.
Poly-ABE: A traceable and revocable fully hidden policy CP-ABE scheme for integrated demand response in multi-energy systems

Demand response is a crucial measure in multi-energy systems (MESs) that encourages energy service providers (ESPs) to engage with energy users (EUs). In the energy internet, EUs can match their energy source requirements through integrated demand response (IDR). However, such permissionless retrieval of energy data compromises the privacy of ESPs and exposes the energy consumption data of EUs. To address this, we propose a traceable, revocable fully hidden policy CP-ABE scheme called Poly-ABE for a data-driven IDR model in MESs, which allows registered EUs to request data and ESPs to authorize data decryption. Poly-ABE features fine-grained access policies using a matrix, coupled with hidden vector encryption to fully hide the policies. Matrix policies offer flexible and complex authorization rules with lower cost. Moreover, Poly-ABE constructs a decryption permission binary tree (DPBT) to identify the attribute permissions of EUs. If malicious behavior is detected, the DPBT is used to determine a minimum set of tree nodes to pinpoint malicious nodes. We rigorously demonstrate that Poly-ABE is selectively secure against selective access policy and chosen plaintext attacks. The experimental results substantiate the feasibility of Poly-ABE, highlighting its pre-authentication, traceability, revocation, and update capabilities.
A privacy preserving graph neural networks framework by protecting user’s attributes

Graph neural networks (GNNs) can learn the node representations to capture both node features and graph topology information through the message passing mechanism. However, since the information collected by GNNs is often used without authorization or maliciously attacked by hackers, which may result in leakage of users' private information. To this end, we propose a privacy preserving GNNs framework, which not only protects the attribute privacy but also performs well in various downstream tasks. Specifically, when the users communicate with the third party, Paillier homomorphic encryption (HE) is used to encrypt users' sensitive attribute information to prevent privacy leakage. Considering that the third party may be untrustworthy, differential privacy (DP) with Laplace mechanism is carried out to add noise to sensitive attribute information before transmission, so that the real attribute information is not accessible to the third party. Subsequently, the third party trains the GNNs model by using both the privacy preserving attribute information and public network topology information. Extensive experimental results show that, compared with the state-of-the-art methods, the privacy preserving GNNs still achieves satisfactory performance regarding different downstream tasks, such as node classification and link prediction while protecting the sensitive attributes of individuals.
An embedded feature selection approach for depression classification using short text sequences

Depression has become a serious mental health issue worldwide, particularly due to the rise of the Global Pandemic. Identifying depression of an individual from short texts shared in social media is a challenging task. The present work aims to select the optimal feature subset for classifying short texts for depression detection. By performing feature selection, it is possible to eliminate redundant and noisy features in high-dimensional datasets with small sample sizes. This can prevent the ”curse of dimensionality” and enhance the effectiveness of classification algorithms. However, current feature selection methods often focus on optimizing classification or clustering performance, while neglecting the stability of the selected features. This can lead to unstable results and make it challenging to identify meaningful and interpretable features. This paper introduces a novel embedded feature selection approach named Statistical Relevance Class Frequency based on Whale Optimization Algorithm (SRCF-WOA) for selecting feature subsets from short texts in social media. The proposed methodology extracts both the unigram features and composite features to capture the semantic and structural information. χ2.rcf (Chi-squared relevance class frequency) filter approach is applied to rank the extracted features to signify the importance of the features. WOA is adapted to retrieve the optimal subset of features with low-dimensional space using its high exploration and high exploitation capability. In the evaluation process, four benchmark short text datasets and two classifiers are used. The comparison shows that the proposed embedded feature selection method outperforms other algorithms in terms of accuracy and Fβ scores(β=0.5,1, and 2). The sensitivity analysis is carried out to check the robustness and stability of the proposed method. The findings indicate that the SRCF-WOA surpasses other methods on the majority of datasets, achieving the maximum classification accuracy while utilizing the minimal features. The statistical importance of these findings is further supported by the Analysis of Variance (ANOVA) F-test. Moreover, the proposed method strikes the optimal balance between classification accuracy and feature stability.
Sentimental Contrastive Learning for event representation

Event representation learning is crucial for numerous event-driven tasks, as the quality of event representations greatly influences the performance of these tasks. However, many existing event representation methods exhibit a heavy reliance on semantic features, often neglecting the wealth of information available in other dimensions of events. Consequently, these methods struggle to capture subtle distinctions between events. Incorporating sentimental information can be particularly useful when modeling event data, as leveraging such information can yield superior event representations. To effectively integrate sentimental information, we propose a novel event representation learning framework, namely Sentimental Contrastive Learning (SCL). Specifically, we firstly utilize BERT as the backbone network for pre-training and obtain the initial event representations. Subsequently, we employ instance-level and cluster-level contrastive learning to fine-tune the original event representations. We introduce two distinct contrastive losses respectively for instance-level and cluster-level contrastive learning, each aiming to incorporate sentimental information from different perspectives. To evaluate the effectiveness of our proposed model, we select the event similarity evaluation task and conduct experiments on three representative datasets. Extensive experimental results demonstrate obvious performance improvement achieved by our approach over many other models.