Reviewerly/exHarmony huggingface dataset:
A Model Driven Framework for Standardizing Requirement Elicitation by Quantifying Software Quality Factor

The quality monitoring of a software is ensured in every activity of software development lifecycle. Software Quality Requirements are defined in terms of Software Quality Factors and they are gathered to ensure that the software produced meets the user defined quality standards; framework activities are also performed to ensure the incorporation of quality into the developed product. However, these quality factors are qualitative in nature and are thus hard to understand, elicit and record; it also makes the analysing process of the factors hard due to natural language constraints. In order to overcome this, standards and templates are proposed by researchers and devised by organization for eliciting quality factors and then models and procedures are defined to convert the qualitative quality factors into quantitative measures. However, no standardized procedure, tool or model exist that can be applied to this process of recording qualitative software quality factors in quantifiable form for every kind of software product. This paper presents a model driven framework to standardize the procedure of eliciting quality requirements in a quantifiable manner. It uses Obeo Designer to develop the Platform Independent Metamodel (PIM) for the proposed framework. Based on the PIM, it develops a drag-and-drop tool palette and an M1 level instance model using Sirius. The validation of the proposed framework is demonstrated with the help of a case study.
Automatic Modulation Classification: A Novel Convolutional Neural Network Based Approach

Deep learning (DL) is a new paradigm of machine learning (ML) that has shown exceptional performance in image, voice and natural language processing. However, researchers have not explored the use of DL to wireless communication to its full potential. The use of DL technology for wireless communication applications has recently gained popularity. This paper looks into the application of deep learning based approach for automatic modulation classification (AMC). Automatic modulation classification has a diverse applications ranging from civilian to military. A deep learning based convolutional neural network (CNN) architecture for AMC is proposed in this paper. We make use of Gaussian noise layer after convolution layers in our proposed architecture which has a regularization effect while training and it reduces over fitting problem. We demonstrate using experiments that the proposed architecture outperforms the existing CNN based architectures for AMC. We also demonstrate the effects of different architecture parameters on the performance of the proposed algorithm.
Aggregation Transfer Learning for Multi-Agent Reinforcement learning

Multi-agent reinforcement learning is currently mainly used in many real-time strategy games. For example, StarCraft, UAV combat. Multi-agent reinforcement learning algorithms have attracted widespread attention. In large-scale multi-agent environment, there is still the problem of state space explosion. Especially in transfer training, since the network input size is fixed, the existing network structure is difficult to adapt to large-scale scenario transfer training. In this paper, we use aggregation transfer training for multi-agent combat problems from aerial unmanned aerial vehicle (UAV) combat scenarios to extend the small-scale learning to large-scale and complex scenarios. We combine the graph neural network (GNN) with the MADDPG algorithm to process the agent observation with aggregation function and take it as input. It starts training from a small-scale multi-UAV combat scenario, gradually increases the number of UAV. The experimental results indicate that MADDPG methods for multi-agent UAV combat problems trained via aggregation transfer learning are able to reach the target performance more quickly, provide superior performance, compared with ones trained without aggregation transfer learning. The versatility of the confrontation model has also been improved.
An improved k-means algorithm based on density normalization

In order to improve the the accuracy of k-means algorithm, many improved k-means algorithms by selecting the initial center point have been proposed. However, even if the initial centers is selected, the size, shape and density of the clusters in the dataset will still affect the clustering performance. An improved k-means algorithm based on density normalization method (DNK-means) is proposed which uses data transformation to solve above problem. The main idea of the algorithm is to calculate the nearest neighbor density of each point and find the point with the highest density among its nearest neighbors. The point with the highest nearest neighbor density is regarded as a candidate point. These candidate points are constructed into a minimum spanning tree to obtain initial centers. The normalized dataset is obtained by transform all points of dataset to their nearest neighbor with highest density. Finally, k-means is employed on the processed dataset to obtain the final clustering result. DNK-means algorithm is tested on some well-known datasets from UCI machine learning repository. The results show that our algorithm achieves better clustering results than the traditional k-means algorithm and latest improved k-means algorithm.
A proposal to identify stakeholders from news for the institutional relationship management activities of an institution based on Named Entity Recognition using BERT

For an organization’s institutional relationship activities, it is strategic that there is an efficient process of identification and characterization of stakeholders based on available information. Given the increasing volume of data currently available, this strategic process has commonly been supported by information technology solutions, with high potential for the use of data mining techniques such as textual analysis and natural language processing (NLP). In this work we analyzed the possibility of using a mechanism of Named Entity Recognition (NER) based on the use of Bidirectional Encoder Representations from Transformers (BERT) with Conditional Random Field (CRF), which in the future can be used as the stakeholder identification solution as a replacement of the rule based identification. We applied the proposed solution in news dataset to evaluate its performance. The experiment results showed us that pre-trained Portuguese models performed better than Multilingual ones by a good margin of at least 3.43 percentage points on Test Dataset. We also added a post processing Prediction Masking to correct invalid tagging scheme transitions to improve Micro F1 Score in both datasets ranging from 0.38 percentage points to 1.29 percentage points of improvement. Thus, we achieved the objective of improving stakeholder detection by proposing a NER model that far surpasses the naive rules-based approach of current application, which consisted of an exact text match of stakeholders based on a dictionary built manually.
Entity knowledge transfer-oriented dual-target cross-domain recommendations

Cross-domain recommendations can assist users in selecting suitable items in the target domain by aggregating or transferring the abundant available data from the auxiliary domain, which has gradually become a promising research area. However, most existing recommendations are only single-target cross-domain recommendations, and ignore the analysis of entity-side (user side or item side) knowledge, resulting in recommender systems suffering from low accuracy of rating predictions. To address these concerns, we propose a novel Entity Knowledge Transfer-oriented Dual-Target Cross-Domain Recommendations (EKTDCR) to improve the prediction performance of these two domains simultaneously in our paper. Specifically, the latent factor and denoising autoencoder techniques are first utilized to extract and learn entity interaction embedding and entity-side embedding respectively, in separate domains, which are the basis of the proposed EKTDCR approach for dual-domain knowledge transfer and model training. Then, based on the previous entity embeddings, a dual-attention mechanism consisting of intra-domain attention and inter-domain attention modules is designed to transfer user preference knowledge to achieve entity feature fusion across domains. In addition, a deep belief network model is adopted to better train the proposed EKTDCR method, and the learnable regularization constraints are further considered to achieve dual-target cross-domain recommendations. Extensive experiments on four real-world datasets demonstrate that our EKTDCR approach can improve the recommendation performance of both domains simultaneously and significantly outperform the state-of-the-art single-domain and cross-domain baseline methods.
Stable isotope and geochemical evidence for genesis of secondary copper deposits at Girilambone, New South Wales, Australia

Abstract The Girilambone copper deposit of New South Wales, Australia, serves as an end-member model for development of a classic oxidation-zone profile in an environment virtually free of both the effects of active tectonics and significant fluctuation in the local water table. The oxidation zone of other copper deposits may be interpreted for history of recently active tectonics, palaeoclimate, and water-table stability by comparison to the Girilambone deposit. Unlike the oxidation profiles of porphyry copper deposits of western North America, which have been overprinted by many water-table fluctuations produced by active tectonics, the Girilambone deposit appears to have little modification to the original oxidation profile. Oxidation of primary sulfides at Girilambone was an exothermic process facilitated by chemolithotrophic bacteria, recorded by malachite oxygen isotope thermometry estimates of up to 52°C, and very light malachite carbon isotope values. The bacteria generated CO 2 which migrated upwards to react with copper rich meteoric fluids of the vadose zone to precipitate malachite. Unlike porphyry copper deposits of western North America which experienced recent tectonic activity, the secondary minerals (clays, iron oxides and copper carbonates) at Girilambone were not repeatedly fractured and offset during oxidation to re-establish permeability. This reduced permeability of the oxidation zone and slowed the release of CO 2 from the system, producing significantly elevated partial pressure of CO 2 , sufficient for azurite formation to dominate. Azurite oxygen and carbon isotope values indicate formation at lower temperatures up to 32°C, and with less bacterial activity than for malachite. The sulfide-digesting bacteria at Girilambone were relatively free of seasonal swings in population, as the deposit does not have the interbanded azurite and malachite typical of episodic bacterial populations triggered by seasonal precipitation. Thus the absence of significant Cu-carbonate banding at Girilambone serves as a palaeoclimate indicator. Deeper in the Girilambone oxidation zone, native copper and cuprite dominate, whereas chalcocite formed an enrichment blanket just above and at the modern water table. Oxygen and carbon isotope values for pseudomorphs of malachite after azurite indicate that these were generated as a retrograde reaction when CO 2 production from bacterial digestion of sulfides waned and temperatures in the oxidation zone were near ambient. In the post mining environment, chloride-rich groundwater seeps actively precipitate atacamite, while exposed remnants of sulfide masses form an outer rind of porous malachite. Exceptions to this oxidation zone sequence occur due to localised fluid channelisation and perched water-table lenses that generated mineralogical overprints.
Extracting Geographic Addresses from Social Media using Deep Recurrent Neural Networks

The importance of geographical, addresses in people's daily lives cannot be underestimated. People usually use the Internet to search for unfamiliar areas and then use map services to mark locations. Using social media to extract information, particularly geographical addresses, is rapidly increasing worldwide. Social media represents the right choice as a source in identifying the location that people need to find. In this paper, a deep neural network using a Bidirectional Long Short-Term Memory with CRF (BI-LSTM-CRF) model is applied for address extraction. In addition, a Bidirectional Encoder Representations from Transformers (BERT) model is implemented to extract the geographical addresses from Facebook posts. Further, we reveal how to use the BIEO tagging method to apply the sequence labeling technique to Arabic postal address extraction. An Arabic corpus from social media is annotated to evaluate our proposed model. The results show that Arabic postal addresses can be extracted through BI-LSTM-CRF and BERT models with a high F-measure.
Evaluation of User Experiences in an Immersive Role Play for Cross-Institutional and Cross-National Virtual Collaborative Learning in Hospitality Management

Both the European Union and the OECD highlight, amongst others, collaboration, virtual communication and problem-solving, as well as social and digital skills as key competences for the 21st century. As such, the Hotel Academy project aims at the development of a joint curriculum that allows for virtual interdisciplinary, international, and intercultural collaboration and exchange between three European universities in the field of hospitality management. In the implementation of the curriculum, immersive technologies are applied to role plays as the primary didactic method. In this paper, we present research on the piloting of an industry-specific desktop/VR-based role play after a detailed description of the project and the VR learning environment. The evaluation of participants’ user experiences follows a mixed methods approach and helps not only to draw attention to learners’ experiences of presence and immersion, but also to the reflection on the role play design and didactical approach, as well as the teaching and learning environment. Our results imply that students were willing to draw their full attention to the underlying learning task when working in the desktop VR environment.
FloodNet: Underwater image restoration based on residual dense learning

The efficiency of the underwater image restoration task is hindered due to the degradation factors like light scattering, color shift, suspended particles and haze occurring in the underwater environment. In recent years, different methods have relied on the underwater image formation model and deep learning techniques to restore the underwater image, but they tend to produce unnatural artifacts and reduced levels of sharpness. To address these challenges, in this paper, we propose FloodNet using residual dense learning with the objective of estimating restored underwater images from a wide variety of degraded underwater images. The proposed FloodNet architecture is a fully convolution neural network that comprises of three modules, namely, low-level feature extraction for extracting features from the degraded underwater image, residual dense blocks that are densely connected via skip-connections for hierarchical feature fusion and to improve the flow of gradient during back-propagation, and finally global feature fusion to adaptively utilize both local and global residual learning to obtain a restored underwater image. We conduct the quantitative and qualitative evaluations on paired and unpaired underwater image datasets, as well as the application and user study analysis. The results demonstrate superior performance of FloodNet against existing SOTA methods. • Propose FloodNet, an end-to-end CNN architecture to restored underwater images from a wide variety of degraded underwater images. • Introduce residual dense blocks to connect the convolutional layers using skip-connections. • Global feature fusion to adaptively utilize both local and global residual learning. • Exhaustive perspective and quantitative experimental analysis on paired and unpaired underwater images. • Perform application and user study analysis.
Association Rules-Based Classifier Chains Method

The order for label learning is very important to the classifier chains method, and improper order can limit learning performance and make the model very random. Therefore, this paper proposes a classifier chains method based on the association rules (ARECC in short). ARECC first designs strong association rules based label dependence measurement strategy by combining the idea of frequent patterns; then based on label dependence relationship, a directed acyclic graph is constructed to topologically sort all vertices in the graph; next, the linear topological sequence obtained is used as the learning order of labels to train each label’s classifier; finally, ARECC uses association rules to modify and update the probability of the prediction for each label. By mining the label dependencies, ARECC writes the correlation information between labels in the topological sequence, which improves the utilization of the correlation information. Experimental results of a variety of public multi-label datasets show that ARECC can effectively improve classification performance.
Design and Production of Educational Video Games for the Inclusion of Deaf Children

This work presents the design and development of an educational video game, a product committed to the project "Model for the production of educational digital content for the inclusion of deaf people" attached to the national program financed by Colciencias - Colombia: "Educational and technological pedagogical architecture for teacher training in and for diversity”. The video game was made under the logic of applied and design research. It recognizes the importance of this resource as a didactic tool, which enhances and strengthens the learning process in deaf and hearing children, in the area of ​​natural sciences. Instructional design includes: topic, purpose, target audience, learning objectives, and competencies. For its planning, elaboration and development, the environment, the rules, the challenge and the genre of the game were taken into account, deriving the prototype in the Unity engine.
Intuitiveness Level: Frustration-Based Methodology for Human–Robot Interaction Gesture Elicitation

For robotics to become more accessible to people not specialized in the area, it is of fundamental importance to improve and simplify the way people interact with robots. Despite human-robot interaction (HRI) being an effervescent research area, most of the works published so far on the use of gesture interfaces for human-robot communication do not clearly describe how the used gestures were elicited, thus hindering the reproducibility of those works. Considering this, we propose a new and reproducible Frustration-Based Approach (FBA), scientifically established on previous research, which can be used to obtain an intuitive and robust gesture vocabulary for HRI. To accomplish this, we propose Intuitiveness Level (IL), a score to rank gestures according with its intuitiveness. Using IL, it is possible to conceive a complex vocabulary, allowing an increasing of robustness, since more than one gesture can be associated to a task. In a general sense, the proposed methodology is not limited only for HRI, and it can also be used for human-machine interaction in general. In short, the contributions of this work are: ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) A complete methodology to elicit gestures to be used as intuitive communication interface between humans and robots. ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> A metric of intuitiveness which takes into account at least three different characteristics about the elicited gestures.
Impact of Digital India on Education System

Digital education is a campaign initiated by the Indian Government to move towards a new direction and ensure that education services are made accessible electronically to a student through enhanced online infrastructure. This paper is focused largely on both primary and secondary data. Primary data is collected primarily from the education sector by lectures and students about how much they like emerging technologies. Since changes are happening anyway, the best thing is to know more about them, so we can lay hold of them and turn them in the direction of our wishes.
Replay Attention and Data Augmentation Network for 3D Dense Alignment and Face Reconstruction

3D face reconstruction from a single-view image in the wild is a long-standing challenging problem. Traditional 3DMM-based methods directly regressed parameters, which probably caused that the network learned the discriminative informative features in the face insufficiently. In this paper, we propose a replay attention and data augmentation network (RADAN) for 3D dense alignment and face reconstruction. Instead of the traditional attention mechanism, our replay attention module aims to increase the sensitivity of the network to informative features by adaptively recalibrating the weight response in the attention mechanism, which typically reinforces the distinguishability of the learned feature representation. In this way, the network is able to further improve the accuracy of face reconstruction and dense alignment in an unconstrained environment. Moreover, to improve the generalization performance of the model and the ability of the network to capture local details, we present a data augmentation strategy to preprocess the sample data, which generates the images that contain more local details and occluded face in a cropping and pasting manner. Extensive qualitative and quantitative experimental results on widely-evaluated benchmarking datasets demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. Code is available at https://github.com/zhouzhiyuanl/RADANet.
Learning-Based Posture Detection Using Purely Passive Magneto-Inductive Tags

We propose a novel low-cost and low-complexity approach that uses the magnetic near-field detuning caused by passive coils in order to detect and classify movements. This idea can enable ubiquitous health monitoring applications on or within the body. In this work, we employ our idea with the specific goal of detecting human body postures with only a few measuring nodes. More precisely, we place resonantly loaded passive coils (tags) on a person&#x0027;s limbs and investigate how the sole presence of these coils affects the input impedances of secondary coils (anchors) which are located on the torso. Due to the strong magnetic near-field coupling of all coils, each different posture changes the inter-coil links and hence leads to a unique set of impedance measurements. These impedance patterns can conversely be learned to use them for classification purposes. In order to verify this idea, we simulate a joint-based human body model and use numerical approximations for the magnetic-near field coupling to generate realistic noisy data sets of impedance measurements. Based on these data sets, we train support vector machines, which we subsequently use to classify the postures on a separate testing set. We investigate how robust this procedure is against increasing noise levels and variations of the postures. In this process, we identify appropriate operating points that lead to an average classification accuracy of 94 percent.