Reviewerly/exHarmony huggingface dataset:
Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?

Digitalization has altered many assumptions underpinning research on innovation management. At the early innings of exploring how digital innovation management stands out, there is a need for further studies in this area. Previous research on how firms use artificial intelligence has distinguished between automation and augmentation of human activities. In this paper, we explore how firms implement artificial intelligence within research and development. Utilizing an international news database spanning 956 articles from 122 newspapers published in 2020, we find that artificial intelligence is primarily adopted to augment human activities (55%) within research and development, rather than to automate matters (11%). We observe differences across sectors where automation is more common in government, information and communication technology (ICT), and technology and software. Our systematic coding shows that artificial intelligence is primarily adopted for exploration research and development (64%), rather than exploitation (5%). Based on these findings, we conclude that research and development from artificial intelligence primarily focuses on novel markets and areas of operations, rather than enhancing existing product markets and activities. Moreover, it augments human labor rather than replaces it; hence, job losses related to artificial intelligence do not seem to be taking place within research and development.
Global path planning based on a bidirectional alternating search A* algorithm for mobile robots

While the A* algorithm has been widely investigated and applied in path planning problems, it has outstanding issues such as long calculation time, large turning angles, and the unsmoothed path in large task spaces. Aiming at overcoming these problems, an improved A* algorithm is proposed in this work. First, a bidirectional alternating search (BAS) strategy is introduced into the A* algorithm; the forward and backward path lists use the current path node in the opposite list as the target node to alternately search for paths until the paths meet, which improves the search efficiency of the algorithm. Then, the heuristic function is weighted via exponential attenuation, which overcomes the shortcomings of the BAS-A* algorithm. Finally, the filtering function of path nodes is introduced to reduce redundant nodes in the path, thereby effectively reducing the turning angle. Moreover, the use of Bézier curves fulfills the requirements of smooth path planning, which is critical for the motion control of mobile robots. Simulations of path planning on maps of different sizes were performed, and the proposed algorithm was compared with that of the A* algorithm, the genetic algorithm (GA) and the simulated annealing algorithm (SA) in terms of the computation time, path length, and turning angle. The results reveal that the proposed algorithm can solve the robot path planning problem more efficiently and smoothly than the other algorithms mentioned above. Additionally, the practicability of the proposed algorithm is validated on the TurtleBot3 Waffle Pi mobile robot.
TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.
Synthetic data generation for tabular health records: A systematic review

Synthetic data generation (SDG) research has been ongoing for some time with promising results in different application domains, including healthcare, biometrics and energy consumption. The need for a robust SDG solution to capitalise on advances in Big Data and AI technology has never been greater to enable access to useful data while ensuring reasonable privacy protections. This paper presents a systematic review from the last 5 years (2016–2021) to analyse and report on recent approaches in synthetic tabular data generation (STDG) with a focus on the healthcare application context to preserve patient privacy, paying special attention to the contribution of Generative Adversarial Networks (GAN). In total 34 publications have been retrieved and analysed. A classification of approaches has been proposed and the performance of GAN-based approaches has been extensively analysed. From the systematic review it has been concluded that there is no universal method or metric to evaluate and benchmark the performance of various approaches and that further research is needed to improve the generalisability of GANs to find a model that works optimally across tabular healthcare data.
Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of interpretability as well as dataset representation bias and prediction fairness of deep learning models for in-hospital mortality prediction. First, we analyze the interpretability of deep learning mortality prediction models and observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models as well as recognizing new important features that domain knowledge does not consider; (2) prediction models rely on demographic features, raising concerns in fairness. Therefore, we then evaluate the fairness of models and do observe the unfairness: (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) models often rely on racial attributes unequally across subgroups to generate their predictions. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors. Our analysis demonstrates that the prediction performance is not the only factor to consider when evaluating models for healthcare applications, since high prediction performance might be the result of unfair utilization of demographic features. Our findings suggest that future research in AI models for healthcare applications can benefit from utilizing the analysis workflow of interpretability and fairness as well as verifying if models achieve superior performance at the cost of introducing bias.
Image Inpainting With Local and Global Refinement

Image inpainting has made remarkable progress with recent advances in deep learning. Popular networks mainly follow an encoder-decoder architecture (sometimes with skip connections) and possess sufficiently large receptive field, i.e., larger than the image resolution. The receptive field refers to the set of input pixels that are path-connected to a neuron. For image inpainting task, however, the size of surrounding areas needed to repair different kinds of missing regions are different, and the very large receptive field is not always optimal, especially for the local structures and textures. In addition, a large receptive field tends to involve more undesired completion results, which will disturb the inpainting process. Based on these insights, we rethink the process of image inpainting from a different perspective of receptive field, and propose a novel three-stage inpainting framework with local and global refinement. Specifically, we first utilize an encoder-decoder network with skip connection to achieve coarse initial results. Then, we introduce a shallow deep model with small receptive field to conduct the local refinement, which can also weaken the influence of distant undesired completion results. Finally, we propose an attention-based encoder-decoder network with large receptive field to conduct the global refinement. Experimental results demonstrate that our method outperforms the state of the arts on three popular publicly available datasets for image inpainting. Our local and global refinement network can be directly inserted into the end of any existing networks to further improve their inpainting performance. Code is available at https://github.com/weizequan/LGNet.git.
Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis.
TPE-GAN: Thumbnail Preserving Encryption Based on GAN With Key

To balance the privacy and usability of images in the cloud, Tajik <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> recently designed a thumbnail preserving encryption (TPE) based on sum-preserving encryption, however, multiple iterations make it inefficient. We use CycleGan to simulate randomized unary encoding (RUE) and achieve a more efficient TPE. Thumbnail consistency loss is proposed to guarantee the visual quality of the encrypted image, and the quality of decrypted image is improved via ssim-loss. Besides, a decryption network with a key is retrained such that it can decrypt cipher images in multiple domains, and a binary string is adopted as the key instead of the decryption network parameters to facilitate sharing of images. Simulations verify the effectiveness of the proposed algorithm.
Quality requirements challenges in the context of large-scale distributed agile: An empirical study

Engineering quality requirements in agile projects does not fit organically with agile methods. Despite the agile community acknowledges this, little empirical evidence has been published on this topic. This exploratory qualitative interview-based study explicates the challenging situations experienced by practitioners in engineering the quality requirements in the context of large-scale distributed agile projects. Moreover, this study describes the practices that agile distributed teams currently use which could contribute by dealing with the identified challenges. The challenging situations and possible mitigation practices were studied from the perspective of 17 practitioners from large distributed agile project teams in six organizations in The Netherlands. Qualitative data were collected using semi-structured, open-ended interviews. Qualitative coding techniques were used for data analysis, to identify the challenges of engineering quality requirements, the mechanisms behind the challenges and the practices used that could mitigate the impact of those challenges. Further, by using dialog mapping technique for qualitative data structuring, we have mapped the identified mechanisms and practices to the challenges. From the perspective of the participating practitioners, our exploratory study revealed 15 challenges classified in five categories: (1) team coordination and communication, (2) quality assurance, (3) quality requirements elicitation, (4) conceptual challenges, and (5) software architecture. The study has also disclosed 13 mechanisms behind the challenges and 9 practices that could mitigate the impact of those challenges. The main contributions of the paper are: (1) the explication of the challenges from practitioners’ perspective and the comparison of our findings with previously published results, (2) the description of the mechanisms behind the challenges, and (3) the identification of the practices currently used by agile teams that could mitigate the impact of the challenges. The findings of this study provide useful input into the process of designing possible solution approaches to overcome the challenges.
Deep gesture interaction for augmented anatomy learning

Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems.
Control of Omnidirectional Robot Using Z-Number-Based Fuzzy System

The control of mobile robots in an uncertain environment that is densely cluttered with dynamic obstacles is an important problem in robotics. A fuzzy set theory is one of the important tools for describing uncertainty and designing control system of the mobile robot. In this paper, a Z-number-based fuzzy inference system for control of the omnidirectional soccer robot is proposed. To design the fuzzy control system of the robot, at first stage, the modeling of the kinematics and dynamics of the omnidirectional four-wheel-driven soccer robot was carried out. Then the design of control system is implemented for the linear and angular speed. Using Z-number-based fuzzy rule base and interpolative reasoning the Z-number-based fuzzy inference system is proposed. The proposed Z-number-based inference mechanism is then used for control of the omnidirectional robot. The fuzzy controller algorithm has been tested in simulations and provided satisfactory results at runtime. The proposed control algorithm minimizes the deviation between the current plant output signal and the reference signal. The results obtained in this paper demonstrate the suitability of using Z-number-based system in control of soccer robots.
The effect of visual motion stimulus characteristics on vection and visually induced motion sickness

Several factors contribute to the likelihood of experiencing illusory sensations of self-motion (i.e., vection) in Virtual Reality (VR) applications. VR users can also experience adverse effects such as disorientation, oculomotor issues, or nausea known as visually induced motion sickness (VIMS). The goal of the present study was to systematically investigate three characteristics of visual motion stimuli—speed, density, and axis of rotation—and how they relate to both vection and VIMS. Two experiments were conducted. In Experiment 1, a stereoscopic stimulus containing a star field of white spheres on a black background was presented to 21 participants. The stimulus contained linear forward motion (expanding optic flow) and was varied with respect to (a) speed (faster, slower) and (b) density (lower, higher). Ratings of vection (onset time, intensity, duration), VIMS (measured via FMS, SSQ), and presence were recorded. In Experiment 1 vection was found to be strongest under faster and higher density conditions. VIMS was overall minimal and not affected by either speed or density. In Experiment 2, rotation along the pitch, yaw, or roll axes were added to the stimulus that created the strongest vection in Experiment 1, resulting in spiral/curvilinear motion profiles. Again, subjective ratings of vection, VIMS, and presence were collected. Results showed that vection intensity was significantly increased when pitch or roll rotation were added to forward motion. Despite overall low VIMS scores, pitch rotation resulted in the highest FMS scores and significantly greater disorientation as measured by the SSQ. No correlations between the vection and VIMS measures were observed. Overall, these results suggest that all three stimulus' characteristics (density, speed, added rotations) can alter the sensation of vection and can have additive effects, but that this increase in vection is not necessarily associated with increases in VIMS.
Slice Finder: Automated Data Slicing for Model Validation

As machine learning (ML) systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it comes to helping users trace model performance problems all the way to the data. We focus on the particular problem of slicing data to identify subsets of the validation data where the model performs poorly. This is an important problem in model validation because the overall model performance can fail to reflect that of the smaller subsets, and slicing allows users to analyze the model performance on a more granular-level. Unlike general techniques (e.g., clustering) that can find arbitrary slices, our goal is to find interpretable slices (which are easier to take action compared to arbitrary subsets) that are large and problematic. We propose Slice Finder, which is an interactive framework for identifying such slices using statistical techniques. Applications include diagnosing model fairness and fraud detection, where identifying slices that are interpretable to humans is crucial.
Cascaded Deep Networks With Multiple Receptive Fields for Infrared Image Super-Resolution

Infrared images have a wide range of military and civilian applications including night vision, surveillance and robotics.However, high-resolution infrared detectors are difficult to fabricate and their manufacturing cost is expensive.In this work, we present a cascaded architecture of deep neural networks with multiple receptive fields to increase the spatial resolution of infrared images by a large scale factor (×8).Instead of reconstructing a high-resolution image from its low-resolution version using a single complex deep network, the key idea of our approach is to set up a mid-point (scale ×2) between scale ×1 and ×8 such that lost information can be divided into two components.Lost information within each component contains similar patterns thus can be more accurately recovered even using a simpler deep network.In our proposed cascaded architecture, two consecutive deep networks with different receptive fields are jointly trained through a multi-scale loss function.The first network with a large receptive field is applied to recover large-scale structure information, while the second one uses a relatively smaller receptive field to reconstruct small-scale image details.Our proposed method is systematically evaluated using realistic infrared images.Compared with state-of-theart Super-Resolution methods, our proposed cascaded approach achieves improved reconstruction accuracy using significantly less parameters.