Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis

Purpose To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. Materials and Methods In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010–2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1–100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. Results The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). Conclusion Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening. Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application Domain Clinical trial registration no. NCT01091545 © RSNA, 2021
Towards optimal placement and scheduling of DNN operations with Pesto

The increasing size of Deep Neural Networks (DNNs) has necessitated the use of multiple GPUs to host a single DNN model, a practice commonly referred to as model parallelism. The key challenge for model parallelism is to efficiently and effectively partition the DNN model across GPUs to avoid communication overheads while maximizing the GPU utilization, with the end-goal of minimizing the training time of DNN models. Existing approaches either take a long time(hours or even days) to find an effective partition or settle for sub-optimal partitioning, invariably increasing the end-to-end training effort. In this paper, we design and implement Pesto, a fast and near-optimal model placement technique for automatically partitioning arbitrary DNNs across multiple GPUs. The key idea in Pesto is to jointly optimize the model placement and scheduling at the fine-grained operation level to minimize inter-GPU communication while maximizing the opportunity to parallelize the model across GPUs. By carefully formulating the problem as an integer program, Pesto can provide the optimal placement and scheduling. We implement Pesto in TensorFlow and show that Pesto can reduce model training time by up to 31% compared to state-of-the-art approaches, across several large DNN models.
An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

Purpose One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition. Design/methodology/approach The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition. Findings Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively. Originality/value The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition

Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.Due to limited labeled resources and domain shift, cross-domain NER is a challenging task.To address these challenges, we propose a progressive domain adaptation Knowledge Distillation (KD) approach -PDALN.It achieves superior domain adaptability by employing three components:(1) Adaptive data augmentation techniques, which alleviate cross-domain gap and label sparsity simultaneously; (2) Multi-level Domain invariant features, derived from a multigrained MMD (Maximum Mean Discrepancy) approach, to enable knowledge transfer across domains; (3) Advanced KD schema, which progressively enables powerful pre-trained language models to perform domain adaptation.Extensive experiments on four benchmarks show that PDALN can effectively adapt highresource domains to low-resource target domains, even if they are diverse in terms and writing styles.Comparison with other baselines indicates the state-of-the-art performance of PDALN.
What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability

Sarcasm and sentiment embody intrinsic uncertainty of human cognition, making joint detection of multi-modal sarcasm and sentiment a challenging task.In view of the advantages of quantum probability (QP) in modeling such uncertainty, this paper explores the potential of QP as a mathematical framework and proposes a QP driven multi-task (QPM) learning framework.The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion network and a quantum measurement mechanism.Each multimodal (e.g., textual, visual) utterance is first encoded as a quantum superposition of a set of basis terms using a complex-valued representation.Then, the quantum-like fusion network leverages quantum state composition and quantum interference to model the contextual interaction between adjacent utterances and the correlations across modalities respectively.Finally, quantum incompatible measurements are performed on the multi-modal representation of each utterance to yield the probabilistic outcomes of sarcasm and sentiment recognition.Experimental results show the state-of-the-art performance of our model.
A Framework for Learning Ante-hoc Explainable Models via Concepts

Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we propose one such model that appends an explanation generation module on top of any basic network and jointly trains the whole module that shows high predictive performance and generates meaningful explanations in terms of concepts. Our training strategy is suitable for unsupervised concept learning with much lesser parameter space requirements compared to baseline methods. Our proposed model also has provision for leveraging self-supervision on concepts to extract better explanations. However, with full concept supervision, we achieve the best predictive performance compared to recently proposed concept-based explainable models. We report both qualitative and quantitative results with our method, which shows better performance than recently proposed concept-based explainability methods. We reported exhaustive results with two datasets without ground truth concepts, i.e., CIFAR10, ImageNet, and two datasets with ground truth concepts, i.e., AwA2, CUB-200, to show the effectiveness of our method for both cases. To the best of our knowledge, we are the first ante-hoc explanation generation method to show results with a large-scale dataset such as ImageNet.
Scene Text Recognition Based on Bidirectional LSTM and Deep Neural Network

Deep learning is a subfield of artificial intelligence that allows the computer to adopt and learn some new rules. Deep learning algorithms can identify images, objects, observations, texts, and other structures. In recent years, scene text recognition has inspired many researchers from the computer vision community, and still, it needs improvement because of the poor performance of existing scene recognition algorithms. This research paper proposed a novel approach for scene text recognition that integrates bidirectional LSTM and deep convolution neural networks. In the proposed method, first, the contour of the image is identified and then it is fed into the CNN. CNN is used to generate the ordered sequence of the features from the contoured image. The sequence of features is now coded using the Bi-LSTM. Bi-LSTM is a handy tool for extracting the features from the sequence of words. Hence, this paper combines the two powerful mechanisms for extracting the features from the image, and contour-based input image makes the recognition process faster, which makes this technique better compared to existing methods. The results of the proposed methodology are evaluated on MSRATD 50 dataset, SVHN dataset, vehicle number plate dataset, SVT dataset, and random datasets, and the accuracy is 95.22%, 92.25%, 96.69%, 94.58%, and 98.12%, respectively. According to quantitative and qualitative analysis, this approach is more promising in terms of accuracy and precision rate.
Understanding Teacher Digital Competence in the Framework of Social Sustainability: A Systematic Review

Due to the global COVID-19 pandemic, ICT has been urgently introduced in education systems in a generalised manner. In this context, it is essential for teachers to master a spectrum of basic digital competencies and manifest digital leadership in the classroom. In addition, it is necessary to consider the relationship between digital competence development and social sustainability, that is, social and cultural heritage, and to what extent they contribute to improving social cohesion and living conditions in a community. This study presents a systematic review of research on teacher digital competence and social sustainability based on the PRISMA model and a review of 22 studies indexed in SCOPUS. The review reveals that most are intended to measure the digital competence level of teachers, usually in compulsory stages of the educational system and through quantitative studies based on virtual questionnaires comprised of closed-ended questions. However, the studies tend to ignore questions related to social sustainability (access to resources, heritage culture, intergenerational transmission, employability, or gender equality). It is therefore urgent to develop research committed to a sustainable society that is oriented towards social justice.
Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. The effect of the attribute reduction process is investigated using thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia. The feature-reduction has achieved higher classification rates up to 8.5% for the best classifiers and up to 15% for other classifiers, for the different data collection regions. Additionally, in the regression, it achieved improvements of average root mean square error up to 5.6% and in mean absolute error values up to 8.3%. The hybrid models could reduce the root mean square errors by 70.2% and 4.3% than the numerical and machine learning models, respectively, when these models are applied to some dataset. For some reduced feature data, the hybrid models could reduce the root mean square errors by 47.3% and 14.4% than the numerical and machine learning models, respectively.
Remote Eye Gaze Tracking Research: A Comparative Evaluation on Past and Recent Progress

Several decades of eye related research has shown how valuable eye gaze data are for applications that are essential to human daily life. Eye gaze data in a broad sense has been used in research and systems for eye movements, eye tracking, and eye gaze tracking. Since early 2000, eye gaze tracking systems have emerged as interactive gaze-based systems that could be remotely deployed and operated, known as remote eye gaze tracking (REGT) systems. The drop point of visual attention known as point of gaze (PoG), and the direction of visual attention known as line of sight (LoS), are important tasks of REGT systems. In this paper, we present a comparative evaluation of REGT systems intended for the PoG and LoS estimation tasks regarding past to recent progress. Our literature evaluation presents promising insights on key concepts and changes recorded over time in hardware setup, software process, application, and deployment of REGT systems. In addition, we present current issues in REGT research for future attempts.
The effects of antecedents and mediating factors on cybersecurity protection behavior

This paper identifies opportunities for potential theoretical and practical improvements in employees' awareness of cybersecurity and their motivational behavior to protect themselves and their organizations from cyberattacks using the protection motivation theory. In addition, it contributes to the literature by examining additional variables and mediators besides the core constructs of the Protection Motivation Model (PMT). This article uses empirical data and structural equation modeling to test the antecedents and mediators of employees' cybersecurity motivational behavior. The study offers theoretical and pragmatic guidance for cybersecurity programs. First, the model developed in this study can partially explain how people may change their cybersecurity protection behavior about security threats and coping actions. Secondly, the result of the study indicates that security coping factors are reliable predictors in projecting individual intention to take protective measures. Third, organizational effort in combatting cyber threats and increasing employee awareness is significantly associated with the use of cyber threat coping processes. Additionally, several practical prescriptions are suggested based on gender, generations, and types of organizations. For example, government organizations have taken well-designed cybersecurity measures and developed detailed protocols to enhance employees’ motivational behavior. Finally, future cybersecurity training materials should adapt to the unique traits of different generations, especially the Gen Edge group and digital natives for all cybersecurity subjects.
Learning Semantic-Aware Spatial-Temporal Attention for Interpretable Action Recognition

Human beings can concentrate on the most semantically relevant visual information when performing action recognition, so as to make reasonable and interpretable predictions. However, most existing approaches, which are applied to address visual tasks, neglect to explicitly imitate such ability for improving the performance and reliability of models. In this paper, we propose an interpretable action recognition framework that can not only improve the performance but also enhance the visual interpretability of 3D CNNs. Specifically, we design a semantic-aware attention module to learn correlative spatial-temporal attention for different action categories. To further leverage the rich semantics of features extracted from different layers, we design a hierarchical semantic fusion module with the help of the learned attention. The proposed two modules can enhance and complement each other, meanwhile, the semantic-aware attention module enjoys the plug-and-play merit. We evaluate our method on different benchmarks with comprehensive ablation studies and visualization analysis. Experimental results demonstrate the effectiveness of our method, showing favorable accuracy against state-of-the-arts while enhancing the semantic interpretability (Code will be available at this link <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PHDJieFu</uri> ).
A Privacy and Efficiency-Oriented Data Sharing Mechanism for IoTs

With the volume of data increasing in the Internet of Things, a new business mode, where data owners share their own data to others for rewards, has emerged. Therefore, how to motivate data owners to participate in the data trading process is the main challenge. So far, lots of works focus on the motivation mechanism designing and ensure a fair distribution of profits among data owners. However, some security and privacy issues are still not well solved and the data owners are still unwilling to participate in the process. Especially, when a data provider claims rewards with its real identity for the shared data, the linkage between its real identity and the shared data will expose the participator's private information included in the shared data, such as location information. To protect user's privacy in the scenario, a privacy and efficiency-oriented data sharing mechanism for IoTs is proposed in this paper. We first propose a blockchain-based data sharing framework in which the behavior of all participants will be supervised. Then, in order to hide the real identities of data providers during the data sharing process, an anonymous certificate-based data sharing policy is proposed. At last, two novel non-interactive zero-knowledge proofs are designed to hide the identities of qualified data providers while claiming rewards to the system. Through security analysis and performance evaluation, the feasibility and effectiveness of the data sharing scheme are illustrated.
Orthogonality of diffractive deep neural network

Some rules of the diffractive deep neural network (D2NN) are discovered. They reveal that the inner product of any two optical fields in D2NN is invariant and the D2NN acts as a unitary transformation for optical fields. If the output intensities of the two inputs are separated spatially, the input fields must be orthogonal. These rules imply that the D2NN is not only suitable for the classification of general objects but also more suitable for applications aimed at optical orthogonal modes. Our simulation shows the D2NN performs well in applications like mode conversion, mode multiplexing/demultiplexing, and optical mode recognition.
An Integrated Adoption Model to Manage Blockchain-Driven Business Innovation in a Sustainable Way

Blockchain technology has the potential to drive innovations across various industries, businesses, and use cases. It is broadly recognized that innovation is a vital source of competitive advantage in a rapidly changing environment. High expectations surround blockchain’s potential for contributing to sustainable economic and social development. However, current blockchain projects still show high failure rates. The associated side effects of such failure rates generate a negative impact on economic and social sustainability performances such as corporate governance, risk management, finance management, human resources and culture management, and competitiveness. This paper assesses adoption models and their risk and success factors. Building on a novel, integrated adoption model to operationalize, measure and manage blockchain-driven business innovation in a sustainable way, we assessed its applicability with an empirical study across 20 industry sectors and 125 business leaders. The results reveal that the developed adoption model holds the potential to support the sustainable usage of blockchain technology for business innovations, not limited to a specific industry or use case. Further case studies and industry activities can be carried out to continue its validation in future works.
Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center

Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model.However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment.Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance.In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in Cloud Environment.In this paper, idea of MOPSO is extended with three distinct features such as Largest Processing Time (LPT) rule is applied to improve load balancing across the resources which leads to energy saving in Cloud Environment; Epsilon Fuzzy Dominance technique is used to select solutions near to Pareto front which improves the diversity of Pareto optimal solutions; and Discrete PSO along with Composite Mutation strategy in the proposed algorithm help to provide better convergence than existing approaches.Hence, the proposed algorithm produced better results than other existing algorithm such as GA and heuristics-based approach.