Reviewerly/exHarmony huggingface dataset:
Maximizing integrative learning in software development teams: A systematic review of key drivers and future research agenda

Software development is a complex phenomenon that requires software teams to integrate diverse knowledge and expertise to provide innovative solutions. A critical success factor for software teams is their integrative learning capability which is driven by multiple factors. We systematically review and synthesize extant literature to identify critical antecedents of integrative learning in software teams and uncover knowledge gaps to inform future research. Searching multiple databases, 447 papers were identified, of which 32 were selected for the final analysis after a rigorous data extraction process. We performed open and axial coding and affinity diagramming to thematically analyze each study for antecedents associated with integrative learning. We also triangulated our results by content analyzing the studies using a software-based text analysis tool. The findings indicate five crucial drivers of integrative learning in software teams: social harmony, process agility, team design, technological maturity, and project environment. The study also highlights the value of recognizing the inter-team and intra-team contexts in team learning. Based on the study findings, we propose a model of integrative learning in software teams, discuss future research agendas, and offer practical insights and recommendations to software professionals.
A novel intelligent hyper-heuristic algorithm for solving optimization problems

In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea “ML to choose heuristics” (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems.
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

“I’m an MC still as honest” – Eminem, Rap God We present MCRapper , an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both (1) statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and (2) approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This flexibility offered by MCRapper is a big advantage over previously proposed solutions, which could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper , we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining, by appropriately computing approximations of the negative and positive borders of the collection of patterns of interest, which allow an effective pruning of the pattern space and the computation of strong bounds to the supremum deviation. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks.
Robust fusion filter for multisensor descriptor system with uncertain‐variance noises and packet dropout

Abstract For the multisensor linear stochastic descriptor system with uncertain‐variance noises and packet dropout, the standard state space model of the new reduced‐order augmented state is obtained based on the singular value decomposition (SVD) approach and augmented state approach. By applying the min‐max robust estimation principle and Kalman filtering theory, the local robust time‐varying Kalman filters of the reduced‐order augmented state and fictitious process noise are proposed. Then, the local robust time‐varying filters of the original descriptor system are presented according to the relation of the reduced‐order augmented state and the original state. And the distributed and covariance intersection (CI) fusion robust time‐varying filters are presented, and the actual and conservative filtering error variances are obtained. Further, the robustness of the local and fused robust filters are proved by applying Lyapunov equation approach, and the accuracy relation between them is analyzed. A simulation example applied to a circuit system shows its effectiveness and applicability.
SE-GAN: Skeleton Enhanced Gan-Based Model for Brush Handwriting Font Generation

Previous works on font generation mainly focus on the standard print fonts where character's shape is stable and strokes are clearly separated. There is rare research on brush hand-writing font generation, which involves holistic structure changes and complex strokes transfer. To address this issue, we propose a novel GAN-based image translation model by integrating the skeleton information. We first extract the skeleton from training images, then design an image encoder and a skeleton encoder to extract corresponding features. A self-attentive refined attention module is devised to guide the model to learn distinctive features between different domains. A skeleton discriminator is involved to first synthesize the skeleton image from the generated image with a pre-trained generator, then to judge its realness to the target one. We also contribute a large-scale brush handwriting font image dataset with six styles and 15,000 high-resolution images. Both quantitative and qualitative experimental results demonstrate the competitiveness of our proposed model.
Integrating AR based operator support features for reconfigurable production systems

Augmented Reality (AR) applications have seen a rise in industry in the last few years, due to technological advancements in many of its fundamental areas, such as display and sensing technologies. AR has been used as a driver for supporting human operators in fully manual and semi-automated systems, proving its usefulness in areas such as operation and reconfiguration instructions, safety and resource status awareness. This paper presents an AR application that integrates a set of support features based on the latest advances in AR. The application covers all the aforementioned areas of interest, by including digital instructions, malfunction alerts and resource state visualization in one unified framework. An Asset Administration Shell (AAS) framework has been developed for integration and data exchange between the application and the overall industrial network. The developed application has been validated in an industrial-components-manufacturing case study. Nevertheless, its modularity allows for the adaption in other varying industrial sectors as well.
Sonic Collaborations between Humans, Non-human Animals and Artificial Intelligences: Contemporary and future aesthetics in more-than-human worlds

This article sketches a theoretical framework that allows the conceptual inclusion of non-human animals and artificial intelligences in human sonic collaborations. Post-humanist concepts that question the categorical divide between nature and culture, following Donna Haraway and Bruno Latour, converge with contemporary, non-adaptationist evolutionary aesthetics. Therefore, the anthropocentric ‘othering’ of non-humans gives way to a concept of a more-than-human sociality of sound. We offer some theoretical propositions for the extension of socially engaged sound practices to collaborations between humans and non-human animals and between humans and artificial intelligences, and then exemplify such multispecies sonic collaborations by analysing some existing projects from the fields of sound art and musical performance. After drawing some more general conclusions from these analyses, we hint at potential aesthetical and ethical parallels between animal and AI creative agency. Finally, we point out a few questions we see as important for future advanced settings of such collaborations, especially when it comes to assemblages of different AI technologies and to future concepts of animal–computer interaction that might enable non-human animals and artificial intelligence to cooperate creatively.
Enhancing convolutional neural network model with spectral features for the identification of cervical dysplasia

Abstract Cervical cell classification plays a key role in the computer‐based screening and diagnosis. This article focuses on cell classification and grading to differentiate the stages of cervical dysplasia. The proposed framework uses a unification of wavelet transform and convolutional neural network (CNN) for segregating spectral and spatial features from papanicolaou stained (pap) smear images. A correlation‐based feature selection is adopted to find relevant features from the CNN model. Random Forest classifier is then incorporated to identify cervical dysplasia from the extracted features. The proposed framework is verified on three publicly available datasets. Using the first dataset, the method obtained an average accuracy of 98.12% and area under curve (AUC) of 0.999. In the second (Sipakmed) and third (Herlev) dataset that consists of cell images of both normal and dysplastic condition, the method achieved an accuracy of 97.01%, 82.60% and AUC of 0.999 and 0.907, respectively. The observations of the study imply that spectral features with feature selection along with a proven machine learning model improves the detection rate of cervical dysplasia.
Part-Aware Refinement Network for Occlusion Vehicle Detection

Traditional machine learning approaches are susceptible to factors such as object scale, occlusion, leading to low detection efficiency and poor versatility in vehicle detection applications. To tackle this issue, we propose a part-aware refinement network, which combines multi-scale training and component confidence generation strategies in vehicle detection. Specifically, we divide the original single-valued prediction confidence and adopt the confidence of the visible part of the vehicle to correct the absolute detection confidence of the vehicle. That reduces the impact of occlusion on the detection effect. Simultaneously, we relabel the KITTI data, adding the detailed occlusion information of the vehicles. Then, the deep neural network model is trained and tested using the new images. Our proposed method can automatically extract the vehicle features and solve larger error problems when locating vehicles in traditional approaches. Extensive experimental results on KITTI datasets show that our method significantly outperforms the state-of-the-arts while maintaining the detection time.
ORBoost: An Orthogonal AdaBoost

Ensemble learners and deep neural networks are state-of-the-art schemes for classification applications. However, deep networks suffer from complex structure, need large amount of samples and also require plenty of time to be converged. In contrast, ensemble learners (especially AdaBoost) are fast to be trained, can work with small and large datasets and also benefit strong mathematical background. In this paper, we have developed a new orthogonal version of AdaBoost, termed as ORBoost, in order to desensitize its performance against noisy samples as well as exploiting low number of weak learners. In ORBoost, after reweighting the distribution of each learner, the Gram-Schmidt rule updates those weights to make a new samples’ distribution to be orthogonal to the former distributions. In contrast in AdaBoost, there is no orthogonality constraint even between two successive weak learners while there is a similarity between the distributions of samples in different learners. To assess the performance of ORBoost, 16 UCI-Repository datasets along with six big datasets are deployed. The performance of ORBoost is compared to the standard AdaBoost, LogitBoost and AveBoost-II over the selected datasets. The achieved results support the significant superiority of ORBoost to the counterparts in terms of accuracy, robustness, number of exploited weak learners and generalization on most of the datasets.
Approximating Decision Diagrams for Quantum Circuit Simulation

Quantum computers promise to solve important problems faster than conventional computers ever could. Underneath is a fundamentally different computational primitive that introduces new challenges for the development of software tools that aid designers of corresponding quantum algorithms. The different computational primitives render classical simulation of quantum circuits particularly challenging. While the logic simulation of conventional circuits is comparatively simple with linear complexity with respect to the number of gates, quantum circuit simulation has to deal with the exponential memory requirements to represent quantum states on non-quantum hardware with respect to the number of qubits. Decision Diagrams (DDs) address this challenge through exploitation of redundancies in matrices and vectors to provide significantly more compact representations in many cases. Moreover, the probabilistic nature of quantum computations enables another angle to tackle the complexity: Quantum algorithms are resistant to some degree against small inaccuracies in the quantum state as these only lead to small changes in the outcome probabilities. We propose to exploit this resistance against (small) errors to gain even more compact decision diagrams. In this work, we investigate the potential of approximation in quantum circuit simulation in detail. To this end, we first present four dedicated schemes that exploit the error resistance and efficiently approximate quantum states represented by decision diagrams. Subsequently, we propose two simulation strategies that utilize those approximations schemes in order to improve the efficiency of DD-based quantum circuit simulation, while, at the same time, allowing the user to control the resulting degradation in accuracy. We empirically show that the proposed approximation schemes reduce the size of decision diagrams substantially and also analytically prove the effect of multiple approximations on the attained accuracy. Eventually, this enables speed-ups of the resulting approximate quantum circuit simulation of up to several orders of magnitudes—again, while controlling the fidelity of the result.
Project activities in the professional training of future designers (based on China’s educational experience)

This article presents the findings of the research into improving design professional training by means of project activities alongside art and multimedia technologies (MTs). It discusses the role of artistic skills in the success of project activities, in particular, the skills of visual representation of the environment via MTs. It was confirmed that the development of media significantly changed the technological potential of design, as well as its artistic value. Therefore, professional training content became the primary focus of attention, since it appeared vital to involve new forms of creating artistic design projects with such modern methods as media fusion and motion graphics. The development of digital technologies promoted changes in teaching methods, adjustments in curricula according to the demands of modern design and those of introducing creative approaches in project activity teaching. The experimental part of the study demonstrates the dynamics of forming professional project skills on the authors’ special course ‘Multimedia Technologies’.
Detecting Offensive Tamil Texts Using Machine Learning And Multilingual Transformer Models

Nowadays, social media has permitted an exponential increase in the circulation of hostile and toxic content, which has resulted in an increase in the number of people exposed to it. Many members of the Natural Language Processing community have recently expressed an interest in automated identification of such harmful content as hate speech, provocative language, and abusive language as a means of addressing this problem. Machine learning and multilingual transformer models are used in this study to automatically identify Tamil language comments as either offensive or not offensive messages. The dataset is collected from YouTube and Kaggle. BERT tops the competition when it comes to offensive language identification models, with an accuracy of 82% compared to the others.
Personality Prediction of Social Network Users using LSTM based Sentiment Analysis

The rapid increase in posting personal information on social media has been noticed over last two years especially due to heavy use of smart phones and restrictions imposed on face-to-face meeting due to the current Covid-19 pandemic. Often, user information and user status updates on social media platforms provide valuable insights in determining personality traits. Scraping and analyzing this social media content and accurate prediction of user personality bring many benefits for enterprises including delivering personalized services, product recommendation and improving performance of recruitment system. This study was carried out based on digital footprints left on social media and the Myers-Briggs Type Indicator (MBTI), one of the most reliable personality inventories, was used to identify and predict the personality traits in mobile applications. Long Short-Term memory (LSTM) architecture-based sentiment classifier was proposed in this paper to predict personality and obtained a convincing model performance with overall 78% accuracy for personality traits.
A novel face recognition technique using Convolutional Neural Network, HOG, and histogram of LBP features

Face recognition is a process of verifying an individual using facial images and it is widely employed in identifying people on social media platforms, validating identity at ATMs, finding missing persons, controlling access to sensitive areas, finding lost pets, etc. Face recognition is still a trending research area because of various challenges like illumination variations, different poses, and expressions of the person. Here, a novel methodology is introduced for face recognition using Histogram of Oriented Gradients (HOG), histogram of Local Binary Patterns (LBP), and Convolutional Neural Network (CNN). The features from HOG, histogram of LBP, and deep features from the proposed CNN are linearly concatenated to produce the feature space and then classified by Support Vector Machine. The face databases ORL, Extended Yale B, and CMUPIE are used for experimental work and attained a recognition rate of 98.48%, 97.33%, and 97.28% respectively.
Going beyond short-term, ‘reduced’ PD: Towards an encompassing typology for children’s participation in infrastructuring processes

Although child participation has been on the Child Computer Interaction (CCI) research agenda for many years, there is a lacuna in research on the roles children can play in long-term, sustained Participatory Design (PD) processes. This article explores the characteristics of an infrastructuring approach to PD that revolves around long-term and on-going socio-technical processes as opposed to the more typical short-term PD projects concerned with delivering a finished design product. Based on empirical data collected in three case studies involving both short-and long-term PD processes with children between 6 and 16 years old, we investigated different roles children can play in long-term, sustained PD. The contribution of this study is a comprehensive typology of children’s roles that goes beyond children’s participation in traditional, ‘staged’ PD. This typology offers the necessary vocabulary and analytical toolbox to account for key notions of infrastructuring, including ‘match-making’, ‘publics’, ‘agonism’, ‘attachments’, ‘shared issues’ and ‘capacity-building’. • Research is needed on children’s roles in long-term, sustained Participatory Design processes. • Children take on different roles in infrastructuring processes. • Children take on roles as match-makers, process designers, players, design partners and mediators. • A typology is presented to make sense of children’s roles in infrastructuring processes. • This typology provides a vocabulary for including infrastructuring concepts in CCI research and design.
Rotated Object Detection via Scale-Invariant Mahalanobis Distance in Aerial Images

Rotated object detection in aerial images is a meaningful yet challenging task as objects are densely arranged and have arbitrary orientations. The eight-parameter (coordinates of box vectors) methods in rotated object detection usually use ln-norm losses (L1 loss, L2 loss, and smooth L1 loss) as loss functions. As ln-norm losses are mainly based on non-scale-invariant Minkowski distance, using ln-norm losses will lead to inconsistency with the detection metric rotational Intersection-over-Union (IoU) and training instability. To address the problems, we use Mahalanobis distance to calculate loss between the predicted and the target box vertices' vectors, proposing a new loss function called Mahalanobis Distance Loss (MDL) for eight-parameter rotated object detection. As Mahalanobis distance is scale-invariant, MDL is more consistent with detection metric and more stable during training than ln-norm losses. To alleviate the problem of boundary discontinuity like all other eight-parameter methods, we further take the minimum loss value to make MDL continuous at boundary cases. We achieve state-of-art performance on DOTA-v1.0 with the proposed method MDL. Furthermore, compared to the experiment that uses smooth L1 loss, we find that MDL performs better in rotated object detection.