Reviewerly/exHarmony huggingface dataset:
Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features.
Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design

This paper presents the improved version of the Lévy Flight Distribution (LFD) algorithm for solving real-valued numerical optimization problems. In the proposed algorithm, the Fitness-Distance Balance (FDB) selection method was used to determine the search agents that well know the migration routes and guide the herd. Thus, the FDB-LFD algorithm, which has a much stronger search performance, was developed. The performance of the proposed algorithm was tested and verified on CEC17 and CEC20 benchmark problems for low-, middle- and high-dimensional search spaces. Results of the FDB-LFD was compared to the performance of 11 other powerful and up-to-date metaheuristic search algorithms. According to Friedman statistical test results, the proposed FDB-LFD algorithm ranked first, whereas the LFD was ranked eleventh. This result demonstrated that the changes in the design of the LFD algorithm had been successful. Moreover, using the proposed algorithm, optimum solutions were found for one of the popular industrial engineering applications: the automatic voltage regulator (AVR) system design. The simulation results revealed that the FDB-LFD is an effective algorithm for solving both unconstrained benchmark and constrained industrial engineering design problems.
Generative Layout Modeling using Constraint Graphs

We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute constraints between layout elements as edges in the layout graph. Third, we solve for the final layout using constrained optimization. For the first two steps, we build on recent transformer architectures. The layout optimization implements the constraints efficiently. We show three practical contributions compared to the state of the art: our work requires no user input, produces higher quality layouts, and enables many novel capabilities for conditional layout generation.
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition

Spatio-temporal convolution often fails to learn motion dynamics in videos and thus an effective motion representation is required for video understanding in the wild. In this paper, we propose a rich and robust motion representation based on spatio-temporal self-similarity (STSS). Given a sequence of frames, STSS represents each local region as similarities to its neighbors in space and time. By converting appearance features into relational values, it enables the learner to better recognize structural patterns in space and time. We leverage the whole volume of STSS and let our model learn to extract an effective motion representation from it. The proposed neural block, dubbed SELFY, can be easily inserted into neural architectures and trained end-to-end without additional supervision. With a sufficient volume of the neighborhood in space and time, it effectively captures long-term interaction and fast motion in the video, leading to robust action recognition. Our experimental analysis demonstrates its superiority over previous methods for motion modeling as well as its complementarity to spatio-temporal features from direct convolution. On the standard action recognition benchmarks, Something-Something-V1 & V2, Diving-48, and FineGym, the proposed method achieves the state-of-the-art results.
Towards A Universal Model for Cross-Dataset Crowd Counting

This paper proposes to handle the practical problem of learning a universal model for crowd counting across scenes and datasets. We dissect that the crux of this problem is the catastrophic sensitivity of crowd counters to scale shift, which is very common in the real world and caused by factors such as different scene layouts and image resolutions. Therefore it is difficult to train a universal model that can be applied to various scenes. To address this problem, we propose scale alignment as a prime module for establishing a novel crowd counting framework. We derive a closed-form solution to get the optimal image rescaling factors for alignment by minimizing the distances between their scale distributions. A novel neural network together with a loss function based on an efficient sliced Wasserstein distance is also proposed for scale distribution estimation. Benefiting from the proposed method, we have learned a universal model that generally works well on several datasets where can even outperform state-of-the-art models that are particularly fine-tuned for each dataset significantly. Experiments also demonstrate the much better generalizability of our model to unseen scenes.
Learning with simulated virtual classmates: Effects of social-related configurations on students’ visual attention and learning experiences in an immersive virtual reality classroom

Immersive virtual reality (IVR) provides great potential to experimentally investigate effects of peers on student learning in class and to strategically deploy virtual peer learners to improve learning. The present study examined how three social-related classroom configurations (i.e., students' position in the classroom, visualization style of virtual avatars, and virtual classmates' performance-related behavior) affect students' visual attention toward information presented in the IVR classroom using a large-scale eye-tracking data set of N = 274 sixth graders. ANOVA results showed that the IVR configurations were systematically associated with differences in learners' visual attention on classmates or the instructional content and their overall gaze distribution in the IVR classroom (Cohen's d ranging from 0.28 to 2.04 for different IVR configurations and gaze features). Gaze-based attention on classmates was negatively related to students' interest in the IVR lesson (d = 0.28); specifically, the more boys were among the observed peers, the lower students' situational self-concept (d = 0.24). In turn, gaze-based attention on the instructional content was positively related to students' performance after the IVR lesson (d = 0.26). Implications for the future use of IVR classrooms in educational research and practice are discussed.
RGB image encryption based on symmetric keys using Arnold transform, 3D chaotic map and affine hill cipher

In this work, we propose a novel color image encryption scheme based on symmetric keys using the synergistic approach of Arnold transform, 3D logistic chaotic map with XOR operation, and affine hill cipher technique. All these methods work together and generate a strong cipher to abstain people from illegal access to data. The simulation results of the quality indicators and comparison with the existing algorithms ensure the security, performance, and effectiveness of our proposed scheme.
A teamwork effectiveness model for agile software development

Abstract Teamwork is crucial in software development, particularly in agile development teams which are cross-functional and where team members work intensively together to develop a cohesive software solution. Effective teamwork is not easy; prior studies indicate challenges with communication, learning, prioritization, and leadership. Nevertheless, there is much advice available for teams, from agile methods, practitioner literature, and general studies on teamwork to a growing body of empirical studies on teamwork in the specific context of agile software development. This article presents the agile teamwork effectiveness model (ATEM) for colocated agile development teams. The model is based on evidence from focus groups, case studies, and multi-vocal literature and is a revision of a general team effectiveness model. Our model of agile teamwork effectiveness is composed of shared leadership, team orientation, redundancy, adaptability, and peer feedback. Coordinating mechanisms are needed to facilitate these components. The coordinating mechanisms are shared mental models, communication, and mutual trust. We critically examine the model and discuss extensions for very small, multi-team, distributed, and safety-critical development contexts. The model is intended for researchers, team members, coaches, and leaders in the agile community.
Complex graph convolutional network for link prediction in knowledge graphs

Knowledge graph (KG) embedding models map nodes and edges to fixed-length vectors and obtain the similarity of nodes as the output of a scoring function to predict missing links between nodes. KG embedding methods based on graph convolutional networks (GCNs) have recently gained significant attention due to their ability to add information of neighboring nodes into the nodes’ embeddings. However, existing GCNs are primarily based on real-valued embeddings, which have high distortion, particularly when modeling graphs with varying geometric structures. In this paper, we propose complex graph convolutional network (ComplexGCN), a novel extension of the standard GCNs in complex space to combine the expressiveness of complex geometry with GCNs for improving the representation quality of KG components. The proposed ComplexGCN comprises a set of complex graph convolutional layers and a complex scoring function based on PARATUCK2 decomposition: the former includes information of neighboring nodes into the nodes’ embeddings, while the latter leverages these embeddings to predict new links between nodes. The proposed model demonstrates enhanced performance compared to existing methods on the two recent standard link prediction datasets.
How can Blockchain technology support patent management? A systematic literature review

Patent management tasks involve many stakeholders from diverse disciplines and hierarchies as well as from inside and outside the organization. By drawing on the transaction cost and stakeholder theories, Blockchain technology offers new opportunities to rethink the way in which stakeholders are organized and collaborate. Although Blockchain technology receives considerable attention from both the public and academia, the question of how Blockchain technology leverages patent management in particular has so far been neglected, although it holds great promise. Consequently, this is what we choose to be our basic research question. In response to it, we conduct a systematic literature review, following a procedure in three steps. Out of an initial sample of 714 articles, we select 52 as being particularly relevant, and classify them in accordance with the dimensions of the 7D Patent Management Maturity Model. Three main foci regarding the use of Blockchain technology emerge – i.e. for the generation, the enforcement, and the exploitation of patents. This study contributes to scholarship and management by showing that Blockchain technology mitigates environmental and behavioral uncertainty, while stimulating new governance forms and business models by reorganizing or discarding stakeholders.
Multi-layer Perceptrons

Having described the structure, the operation and the training of (artificial) neural networks in a general fashion in the preceding chapter, we turn in this and the subsequent chapters to specific forms of (artificial) neural networks. We start with the best known and most widely used form, the so-called multi-layer perceptron (MLP), which is closely related to the networks of threshold logic units. They exhibit a strictly layered structure and may employ other activation functions than a step at a crisp threshold.
A geologically-constrained deep learning algorithm for recognizing geochemical anomalies

The effective identification of geochemical anomalies is essential in mineral exploration. Recently, data-driven deep learning algorithms have gained popularity for recognizing the geochemical patterns linked to mineralization. While purely data-driven deep learning algorithms can exploit geochemical patterns well, but the predicted and extracted results may be inconsistent with the geologic knowledge. In this study, a geologically-constrained deep learning algorithm was proposed to extract multivariate geochemical anomalies associated with W polymetallic mineralization in the south Jiangxi Province, China. The construction of the proposed algorithm involved two steps: (1) quantifying the spatial distribution of the known mineral deposits via fractal analysis, and (2) using prior knowledge obtained by the fractal analysis as a geological constraint to restrain an adversarial autoencoder network for delineating geochemical anomalies associated with mineralization. We conducted a comparative study of geologically-constrained and purely data-driven deep learning algorithms. We found that the former obtained more reasonable and interpretable geochemical anomalies linked to W mineralization. The results obtained by a geologically-constrained deep learning algorithm were more consistent with the regional metallogenic law. Therefore, this geological constraint can improve the generalization ability of the deep learning algorithm and enhance the interpretation of the obtained results in geosciences.
Investigating the barriers of blockchain technology integrated food supply chain: a BWM approach

Purpose Blockchain is an evolving technology that has an impact on a variety of industries and related operations including food supply chain. There are several challenges associated in the implementation of blockchain in the food supply chain. As a result, the goal of this research is to identify and analyse the barriers associated with the implementation of blockchain in food supply chains. Design/methodology/approach A comprehensive literature review is conducted to identify 16 primary barriers associated with the implementation of blockchain technology in the food supply chain and experts finalise these identified barriers and further categorise them into four groups. Furthermore, the best worst method is used to prioritise the finalised barriers. Findings The findings suggest that “technological barriers” and “organisational barriers” are primary barriers among the identified barriers for the implementation of blockchain. These barriers could be mitigated through supply chain collaboration, efficient blockchain technology development through research and development, and increasing technical competence. Research limitations/implications In terms of limitation, there is a possibility that some barriers were overlooked in the literature review process, and expert judgement might be prejudiced. This paper examines the blockchain implementation in the food supply chain, to assist policymakers in overcoming these barriers and ensuring effective adoption. Originality/value This study focuses on the effective implementation of blockchain technology in the food supply chain in the context of emerging economies.
Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm

Software Defect Estimation (SDE) is a fundamental problem solving mechanism in the field of software engineering (SE). SDE is a task that identifies software models that are likely to have defects. In addition, SDE plays a vital overall role in improving software quality, reducing software development costs and accelerating software development processes. The Backpropagation Neural Network (BPNN) is a popular machine learning (ML) estimator widely utilized in SE estimation problems. Unfortunately, its performance depends on the initial weight and bias values. Metaheuristic optimization algorithms, as an alternative method, have proven to have strengths in parameter optimizations. Additionally, population-based metaheuristic algorithms suffer from low exploitation capabilities. In this paper, a new hybrid metaheuristic algorithm-based BPNN (SSA–SA) is proposed by hybridizing the Salp Swarm Algorithm (SSA) with the Simulated Annealing (SA) algorithm. The main goal of the hybridization is to adjust the balance between exploration and exploitation in SSA. The proposed algorithm is also assembled with the BPNN estimator to optimize its parameters to reduce the overall estimation error, which boosts the estimation accuracy. Thus, the proposed algorithm addresses the SDE problem. Experimental results prove the superiority of the proposed hybrid algorithm in optimizing BPNN parameters in comparisons against other estimators and algorithms in most SDE datasets and evaluation criteria.
Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.
Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets

Classification accuracy achieved by a machine learning technique depends on the feature set used in the learning process. However, it is often found that all the features extracted by some means for a particular task do not contribute to the classification process. Feature selection (FS) is an imperative and challenging pre-processing technique that helps to discard the unnecessary and irrelevant features while reducing the computational time and space requirement and increasing the classification accuracy. Generalized Normal Distribution Optimizer (GNDO), a recently proposed meta-heuristic algorithm, can be used to solve any optimization problem. In this paper, a hybrid version of GNDO with Simulated Annealing (SA) called Binary Simulated Normal Distribution Optimizer (BSNDO) is proposed which uses SA as a local search to achieve higher classification accuracy. The proposed method is evaluated on 18 well-known UCI datasets and compared with its predecessor as well as some popular FS methods. Moreover, this method is tested on high dimensional microarray datasets to prove its worth in real-life datasets. On top of that, it is also applied to a COVID-19 dataset for classification purposes. The obtained results prove the usefulness of BSNDO as a FS method. The source code of this work is publicly available at https://github.com/ahmed-shameem/Feature_selection.
TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification

Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task due to a variety of issues, such as color inconsistency that results from the non-uniform manual staining operations, clustering of nuclei, and blurry and overlapping nuclei boundaries. Existing approaches involve segmenting nuclei by drawing their polygon representations or by measuring the distances between nuclei centroids. In contrast, we leverage the fact that morphological features (appearance, shape, and texture) of nuclei in a tissue vary greatly depending upon the tissue type. We exploit this information by extracting tissue specific (TS) features from raw histopathology images using the proposed tissue specific feature distillation (TSFD) backbone. The bi-directional feature pyramid network (BiFPN) within TSFD-Net generates a robust hierarchical feature pyramid utilizing TS features where the interlinked decoders jointly optimize and fuse these features to generate final predictions. We also propose a novel combinational loss function for joint optimization and faster convergence of our proposed network. Extensive ablation studies are performed to validate the effectiveness of each component of TSFD-Net. The proposed network outperforms state-of-the-art networks such as StarDist, Micro-Net, Mask-RCNN, Hover-Net, and CPP-Net on the PanNuke dataset, which contains 19 different tissue types and 5 clinically important tumor classes, achieving 50.4% and 63.77% mean and binary panoptic quality, respectively. The code is available at: https://github.com/Mr-TalhaIlyas/TSFD.
Poplar optimization algorithm: A new meta-heuristic optimization technique for numerical optimization and image segmentation

A novel algorithm called Poplar Optimization Algorithm (POA) is developed in this paper to solve continuous optimization problems. The algorithm mimics the sexual and asexual propagation mechanism of poplar, where the basic philosophy of how to execute sexual and asexual propagation for individuals is detail designed in the algorithm. Mutation strategy of backtracking search algorithm is adopted in POA to maintain the diversity in a certain degree. The performance of POA algorithm is tested on 25 functions from the CEC2005 test suite and 30 functions from the CEC2017 test suite with different features. The results of POA are compared with some other population-based algorithms in terms of the quality and efficiency. Finally, the proposed algorithm is used to find the optimal threshold for image segmentation. The results indicate that the poplar optimization algorithm can obtain competitive or superior performance.