Reviewerly/exHarmony huggingface dataset:
A critical review of research on technological pedagogical and content knowledge (TPACK) in language teaching

Research on TPACK has been burgeoning recently. Some review studies in the field of educational technology have indicated issues and trends in TPACK research conducted in the past decades. However, a review of TPACK research specific to language teachers’ knowledge about language teaching with technology has been lacking. To fill this research gap, the present review study intends to understand the landscape of TPACK research on language teachers published from 2011 to 2019. The analysis results reveal that 51 studies were identified with a publication peak in 2015, mostly conducted in Asia and the Middle East. The 51 articles were categorized into four areas: (a) exploring TPACK, (b) assessing TPACK, (c) developing TPACK, and (d) applying TPACK. More specifically, research on exploring TPACK suggests that while teachers had varying levels of confidence in their TPACK competence, their TPACK indicated the dominant use of technology in traditional teacher-centered teaching. Research on assessing TPACK shows that survey items were contextualized to lessen the difficulty of distinguishing between the seven sub-domains of TPACK. However, the difficulty remained perhaps due to a possibility that the seven sub-domains may not appear workable in practice. This issue needs further research. Research on developing TPACK reveals that effective interventions involved language teachers’ understanding of the TPACK framework, modeling from teacher educators or experienced teachers, and language teachers’ engagement in designing lessons collaboratively. Finally, research on applying TPACK indicates that TPACK-informed language learning courses and platforms were perceived to be helpful and effective. These findings may provide researchers with some possible directions.
RarePlanes: Synthetic Data Takes Flight

RarePlanes is a unique open-source machine learning dataset that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very-high resolution dataset built to test the value of synthetic data from an overhead perspective. Previous research has shown that synthetic data can reduce the amount of real training data needed and potentially improve performance for many tasks in the computer vision domain. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI. Reverie's simulation platform and features 50,000 synthetic satellite images simulating a total area of 9331.2km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with ~ 630,000 aircraft annotations. Both the real and synthetically generated aircraft feature 10 fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, wingspan class, propulsion, number of engines, number of vertical-stabilizers, presence of canards, and aircraft role. Finally, we conduct extensive experiments to evaluate the real and synthetic datasets and compare performances. By doing so, we show the value of synthetic data for the task of detecting and classifying aircraft from an overhead perspective.
A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

To proactively fulfill multiple stakeholders' needs in the engineering solution design process, the knowledge recommendation approach is adopted as a key element in the knowledge management system. Nevertheless, most existing knowledge recommendation approaches cannot simultaneously meet the higher standard of in-context accuracy and diversity. To address the issue, this paper proposes a context-aware diversity-oriented knowledge recommendation approach, thereby assisting stakeholders to accomplish engineering solution design in a smarter manner. Three diversity concerns, namely item-diversity, context-diversity, and user-diversity are addressed by semantic-based content analysis, context definition and awareness, and user profile modeling, respectively. Hence, the proposed approach not only maximizes the diversity of the recommended knowledge but also guarantees its accuracy under multiple problem-solving contexts. Moreover, a practical engineering solution design case on a Smart 3D printer platform is conducted, to validate the efficacy of the proposed approach in providing usable and diverse knowledge items. It is anticipated this work can provide useful insights to practitioners in their knowledge-based engineering solution design process.
Eye tracking in human interaction: Possibilities and limitations

There is a long history of interest in looking behavior during human interaction. With the advance of (wearable) video-based eye trackers, it has become possible to measure gaze during many different interactions. We outline the different types of eye-tracking setups that currently exist to investigate gaze during interaction. The setups differ mainly with regard to the nature of the eye-tracking signal (head- or world-centered) and the freedom of movement allowed for the participants. These features place constraints on the research questions that can be answered about human interaction. We end with a decision tree to help researchers judge the appropriateness of specific setups.
Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition

Recognizing the expressions of partially occluded faces is a challenging computer vision problem. Previous expression recognition methods, either overlooked this issue or resolved it using unrealistic assumptions. Motivated by the fact that the human visual system is adept at ignoring the occlusions and focus on non-occluded facial areas, we propose a landmark-guided attention branch to find and discard corrupted features from occluded regions so that they are not used for recognition. An attention map is first generated to indicate if a specific facial part is occluded and guide our model to attend to non-occluded regions. To further improve robustness, we propose a facial region branch to partition the feature maps into non-overlapping facial blocks and task each block to predict the expression independently. This results in more diverse and discriminative features, enabling the expression recognition system to re-cover even though the face is partially occluded. Depending on the synergistic effects of the two branches, our occlusion-adaptive deep network significantly outperforms state-of-the-art methods on two challenging in-the-wild benchmark datasets and three real-world occluded expression datasets.
Challenges in KNN Classification

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the proposed approaches, some experiments were conducted with 15 UCI benchmark datasets.
Knowledge distillation for incremental learning in semantic segmentation

Deep learning architectures have shown remarkable results in scene understanding problems, however they exhibit a critical drop of performances when they are required to learn incrementally new tasks without forgetting old ones. This catastrophic forgetting phenomenon impacts on the deployment of artificial intelligence in real world scenarios where systems need to learn new and different representations over time. Current approaches for incremental learning deal only with image classification and object detection tasks, while in this work we formally introduce incremental learning for semantic segmentation. We tackle the problem applying various knowledge distillation techniques on the previous model. In this way, we retain the information about learned classes, whilst updating the current model to learn the new ones. We developed four main methodologies of knowledge distillation working on both output layers and internal feature representations. We do not store any image belonging to previous training stages and only the last model is used to preserve high accuracy on previously learned classes. Extensive experimental results on the Pascal VOC2012 and MSRC-v2 datasets show the effectiveness of the proposed approaches in several incremental learning scenarios.
Attention Deep Model With Multi-Scale Deep Supervision for Person Re-Identification

As an important part of intelligent surveillance systems, person re-identification (PReID) has drawn wide attention of the public in recent years. Many recent deep learning-based PReID methods have used attention or multi-scale feature learning modules to enhance the discrimination of the learned deep features. However, the attention mechanisms may lose some important feature information. Moreover, the multi-scale models usually embed the multi-scale feature learning module into the backbone network, which increases the complexity of testing network. To address the two issues, we propose a multi-scale deep supervision with attention feature learning deep model for PReID. Specifically, we introduce a reverse attention module to remedy the feature information losing issue caused by the attention module, and a multi-scale feature learning layer with deep supervision to train the network. The proposed modules are only used at the training phase and discarded during the test phase. Experiments on Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 datasets. demonstrate that our model notably beats other competitive state-of-the-art models.
A New Oversampling Method Based on the Classification Contribution Degree

Data imbalance is a thorny issue in machine learning. SMOTE is a famous oversampling method of imbalanced learning. However, it has some disadvantages such as sample overlapping, noise interference, and blindness of neighbor selection. In order to address these problems, we present a new oversampling method, OS-CCD, based on a new concept, the classification contribution degree. The classification contribution degree determines the number of synthetic samples generated by SMOTE for each positive sample. OS-CCD follows the spatial distribution characteristics of original samples on the class boundary, as well as avoids oversampling from noisy points. Experiments on twelve benchmark datasets demonstrate that OS-CCD outperforms six classical oversampling methods in terms of accuracy, F1-score, AUC, and ROC.
Distributed Training of Deep Learning Models: A Taxonomic Perspective

Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster. Developers of DDLS are required to make many decisions to process their particular workloads in their chosen environment efficiently. The advent of GPU-based deep learning, the ever-increasing size of datasets and deep neural network models, in combination with the bandwidth constraints that exist in cluster environments require developers of DDLS to be innovative in order to train high quality models quickly. Comparing DDLS side-by-side is difficult due to their extensive feature lists and architectural deviations. We aim to shine some light on the fundamental principles that are at work when training deep neural networks in a cluster of independent machines by analyzing the general properties associated with training deep learning models and how such workloads can be distributed in a cluster to achieve collaborative model training. Thereby we provide an overview of the different techniques that are used by contemporary DDLS and discuss their influence and implications on the training process. To conceptualize and compare DDLS, we group different techniques into categories, thus establishing a taxonomy of distributed deep learning systems.
Prostate Cancer Detection Using Deep Learning and Traditional Techniques

Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readily by traditional diagnostic techniques. This research employed deep learning long short-term memory (LSTM) and Residual Net (ResNet - 101), independent of hand-crafted features, and is fine-tuned. The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix (GLCM) using non-deep learning classifiers such as support vector machine (SVM) Gaussian Kernel, k-nearest neighbor-Cosine (KNN - Cosine), kernel naive Bayes, decision tree (DT) and RUSBoost tree. This study reduces the features of carcinoma images, employed machine learning and deep learning approaches. For validation of training and testing data, a jack-knife ten-fold cross-validation method was used. The performance was measured using a confusion matrix such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (AC), Mathews Correlation Coefficient (MCC), and area under the curve (AUC). The most remarkable performance was obtained using non-deep learning methods with GLCM features using KNN-Cosine with sensitivity (98.00%), specificity (99.25%), PPV (98.99%), NPV (99.11%), accuracy (99.07%), and AUC (0.998). The LSTM deep learning method yields performance with sensitivity (98.33%), specificity (100%), PPV (100%), NPV (99.26%), accuracy (99.48%), MCC (0.9879) and AUC (0.9999), where using Deep learning method ResNet - 101, we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that ResNet - 101 deep learning outperformed than non-deep learning methods and LSTM. Thus, the deep learning method ResNet - 101 could be used as a better predictor for the detection of prostate cancer.
Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.
Eye-Tracking for Clinical Ophthalmology with Virtual Reality (VR): A Case Study of the HTC Vive Pro Eye’s Usability

A case study is proposed to empirically test and discuss the eye-tracking status-quo hardware capabilities and limitations of an off-the-shelf virtual reality (VR) headset with embedded eye-tracking for at-home ready-to-go online usability in ophthalmology applications.The eye-tracking status-quo data quality of the HTC Vive Pro Eye is investigated with novel testing specific to objective online VR perimetry. Testing was done across a wide visual field of the head-mounted-display's (HMD) screen and in two different moving conditions. A new automatic and low-cost Raspberry Pi system is introduced for VR temporal precision testing for assessing the usability of the HTC Vive Pro Eye as an online assistance tool for visual loss.The target position on the screen and head movement evidenced limitations of the eye-tracker capabilities as a perimetry assessment tool. Temporal precision testing showed the system's latency of 58.1 milliseconds (ms), evidencing its good potential usage as a ready-to-go online assistance tool for visual loss.The test of the eye-tracking data quality provides novel analysis useful for testing upcoming VR headsets with embedded eye-tracking and opens discussion regarding expanding future introduction of these HMDs into patients' homes for low-vision clinical usability.
Deep Learning for Imaging and Detection of Microorganisms

Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. We believe that deep-learning technology-based systems will be on the front line of monitoring and investigation of microorganisms. Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. We believe that deep-learning technology-based systems will be on the front line of monitoring and investigation of microorganisms.
Challenges and Experiences of Online Evaluation in Courses of Civil Engineering during the Lockdown Learning Due to the COVID-19 Pandemic

As a consequence of the global health emergency in early 2020, universities had to tackle a sudden shift in their teaching–learning strategies so that the preset competences could be fulfilled. This study presents the learning outcomes of the implemented tasks, student experiences, and feedback, as well as some reflections from the instructors with a holistic perspective of the courses due to the adopted measures and adaptations. Six courses taught at civil engineering degrees of three universities, two from Spain and one from Peru, were analyzed. The teaching and evaluation strategies are described, and some reflections are made by comparing the student’s performance with the previous course. Though the shift to online learning had to be made from day to day, with no time for preparation, the experience has proved that online learning can be beneficial in some aspects and has probably come to stay, although some other aspects are difficult to replace with respect to face-to-face learning, especially students’ engagement and motivation. The significance of this study relies on a description of the challenges that arose due to the global public health and an assessment of the results of the implemented strategies to account for both teaching and evaluation in modules of civil engineering. After the acquired experience, new questions have arisen, e.g., what type of content is (and what is not) adequate or suitable for online exams? What features have come to stay? Has higher education taken a step forward to tomorrow’s education?
Blockchain-Based UAV Path Planning for Healthcare 4.0: Current Challenges and the Way Ahead

The Internet of Things (IoT) provides billions of Internet-enabled devices connections across the globe. In the IoT era, the healthcare industry has grown up from Healthcare 1.0 to Healthcare 4.0. As in Healthcare 3.0, patients visit hospital for their regular checkups, which increases the overall expenditure on medical treatment. However, with the recent technological advancements such as UAVs and blockchain, healthcare problems are solved with minimum payments and security of shared medical data. Motivated by these facts, this article provides blockchain-based UAV path planning to provide privacy-preservation in Healthcare 4.0. We propose a three-layered architecture for real-time collection, processing, and transmission of medical data. The proposed architecture provides a distributed platform for UAVs that ensures security and privacy for the transfer of medical data from one location to another with the proof of work (PoW) consensus mechanism. The simulation results show that the proposed model provides an effective solution for reliable data transmission and privacy-preserving sensitive healthcare information against potential threats. The comparative analysis also demonstrates the performance improvements in sharing medical data with patients and doctors. In summary, this article provides insights into the end users for the use of blockchain with UAVs in Healthcare 4.0 for future research directions.
Challenges of Hate Speech Detection in Social Media

Abstract The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.