Reviewerly/exHarmony huggingface dataset:
A systematic review of foreign language learning with immersive technologies (2001-2020)

<abstract> This study provides a systematic literature review of research (2001–2020) in the field of teaching and learning a foreign language and intercultural learning using immersive technologies. Based on 2507 sources, 54 articles were selected according to a predefined selection criteria. The review is aimed at providing information about which immersive interventions are being used for foreign language learning and teaching and where potential research gaps exist. The papers were analyzed and coded according to the following categories: (1) investigation form and education level, (2) degree of immersion, and technology used, (3) predictors, and (4) criterions. The review identified key research findings relating the use of immersive technologies for learning and teaching a foreign language and intercultural learning at cognitive, affective, and conative levels. The findings revealed research gaps in the area of teachers as a target group, and virtual reality (VR) as a fully immersive intervention form. Furthermore, the studies reviewed rarely examined behavior, and implicit measurements related to inter- and trans-cultural learning and teaching. Inter- and transcultural learning and teaching especially is an underrepresented investigation subject. Finally, concrete suggestions for future research are given. The systematic review contributes to the challenge of interdisciplinary cooperation between pedagogy, foreign language didactics, and Human-Computer Interaction to achieve innovative teaching-learning formats and a successful digital transformation. </abstract>
Virtual Reality Action Interactive Teaching Artificial Intelligence Education System

Comprehensively improving the level of vocational education and teaching quality has become an important initiative to meet the new round of technological revolution and industrial change. The traditional teaching mode can no longer meet the needs of industries and enterprises for job competences, and all higher education institutions are actively thinking about how to carry out teaching reform. Virtual reality (VR) can effectively solve the above-mentioned drawbacks, but the hardware facilities of the existing VR systems are extremely expensive, making it impossible to popularize it in teaching. In this context, this paper is based on VR for the design of an interactive teaching platform. This paper discusses the key technologies of virtual reality, introduces the relevant theoretical foundation, and describes the current virtual reality devices with high popularity and their advantages and disadvantages for systematic analysis. From the perspective of teaching design, the design of human-computer interaction teaching process using VR technology to develop learning scenes is studied, introducing 3D modeling techniques, model construction, the production of picture and text panels, the production of video materials, and the establishment of virtual tour guide 3D scenes. The principle of this paper is to build a 3D vision for building 3D views of educational needs to design high quality images and video animation using 3DMAX and unity 3D engines. Then, we introduce the use of VR glasses (entry-level), smart phones (android/ios), and Bluetooth wireless handle to build a simple interactive teaching platform; the platform is of low cost of construction and student coverage, to solve the teaching resources shortage, equipment conditions lagging behind, the curriculum content aging, teachers and students who cannot interact in real time, and a series of problems.
From Contrastive to Abductive Explanations and Back Again

Explanations of Machine Learning (ML) models often address a Open image in new window question. Such explanations can be related with selecting feature-value pairs which are sufficient for the prediction. Recent work has investigated explanations that address a Open image in new window question, i.e. finding a change of feature values that guarantee a change of prediction. Given their goals, these two forms of explaining predictions of ML models appear to be mostly unrelated. However, this paper demonstrates otherwise, and establishes a rigorous formal relationship between Open image in new window and Open image in new window explanations. Concretely, the paper proves that, for any given instance, Open image in new window explanations are minimal hitting sets of Open image in new window explanations and vice-versa. Furthermore, the paper devises novel algorithms for extracting and enumerating both forms of explanations.
Research on English teaching reform based on artificial intelligence matching model

In the context of information education, English teaching needs to match the development of artificial intelligence to improve the intelligence of English teaching. Based on the artificial intelligence matching model, this paper constructs an English teaching reform model based on artificial intelligence algorithms. Moreover, based on the FISST multi-target tracking method, this paper firstly models the target state and measurement as RFS, and then uses the Bayesian filtering method to recursively calculate the target posterior PDF, which can estimate the number and state of targets in real time and make up for the shortcomings of traditional tracking methods. In addition, the system proposed in this article can be applied to online English teaching. Through this system, teachers can realize one-to-one matching of students, identify the status of students in time, and give corresponding English teaching methods to different students. Finally, this paper designs a controlled experiment to analyze the performance of the algorithm proposed in this paper. The research results show that the model constructed in this paper has certain practical effects.
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

Mobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm.
Unsupervised anomaly instance segmentation for baggage threat recognition

Identifying potential threats concealed within the baggage is of prime concern for the security staff. Many researchers have developed frameworks that can automatically detect baggage threats from security X-ray scans. However, to the best of our knowledge, all of these frameworks require extensive training efforts on large-scale and well-annotated datasets, which are hard to procure in the real world, especially for the rarely seen contraband items. This paper presents a novel unsupervised anomaly instance segmentation framework that recognizes baggage threats, in X-ray scans, as anomalies without requiring any ground truth labels. Furthermore, thanks to its stylization capacity, the framework is trained only once, and at the inference stage, it detects and extracts contraband items regardless of their scanner specifications. Our one-staged approach initially learns to reconstruct normal baggage content via an encoder–decoder network utilizing a proposed stylization loss function. The model subsequently identifies the abnormal regions by analyzing the disparities within the original and the reconstructed scans. The anomalous regions are then clustered and post-processed to fit a bounding box for their localization. In addition, an optional classifier can also be appended with the proposed framework to recognize the categories of these extracted anomalies. A thorough evaluation of the proposed system on four public baggage X-ray datasets, without any re-training, demonstrates that it achieves competitive performance as compared to the conventional fully supervised methods (i.e., the mean average precision score of 0.7941 on SIXray, 0.8591 on GDXray, 0.7483 on OPIXray, and 0.5439 on COMPASS-XP dataset) while outperforming state-of-the-art semi-supervised and unsupervised baggage threat detection frameworks by 67.37%, 32.32%, 47.19%, and 45.81% in terms of F1 score across SIXray, GDXray, OPIXray, and COMPASS-XP datasets, respectively.
Fuzzy least squares projection twin support vector machines for class imbalance learning

In this paper, we propose a novel fuzzy least squares projection twin support vector machines for class imbalance learning (FLSPTSVM-CIL). Unlike twin support vector machine (TSVM) which solves two dual problems, we solve two modified primal formulations by solving two systems of linear equations. The proposed FLSPTSVM-CIL model seeks two projection directions such that the samples of two classes are well separated in the projected space. To avoid the singularity issues, we incorporate an extra regularization term to make the optimization problem positive definite. As the real world data may be imbalanced, we assign appropriate fuzzy weights to the samples such that the classifier is not biased towards the samples of the majority class. The statistical analysis and experimental results on the publicly available UCI benchmark datasets show that the proposed FLSPTSVM-CIL performs better as compared to the baseline models. To show the applications of the proposed FLSPTSVM-CIL model on real world datasets, we performed classification of Alzheimer's disease and breast cancer patients. Experimental results show that the generalization performance of the proposed FLSPTSVM-CIL model for the classification of the breast cancer patients and the mild cognitive impairment versus Alzheimer's disease subjects is better as compared to the baseline models.
Dense Relational Image Captioning via Multi-Task Triple-Stream Networks

We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dense relational captioning,</i> a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions for each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relational proposal generation</i> . For relational understanding between objects, the part-of-speech (POS; i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to learn not only to generate captions but also to understand the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. Then, we present applications of our framework to holistic image captioning, scene graph generation, and retrieval tasks.
Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks

The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.
Cerberus

The bandwidth and latency requirements of modern datacenter applications have led researchers to propose various topology designs using static, dynamic demand-oblivious (rotor), and/or dynamic demand-aware switches. However, given the diverse nature of datacenter traffic, there is little consensus about how these designs would fare against each other. In this work, we analyze the throughput of existing topology designs under different traffic patterns and study their unique advantages and potential costs in terms of bandwidth and latency ''tax''. To overcome the identified inefficiencies, we propose Cerberus, a unified, two-layer leaf-spine optical datacenter design with three topology types. Cerberus systematically matches different traffic patterns with their most suitable topology type: e.g., latency-sensitive flows are transmitted via a static topology, all-to-all traffic via a rotor topology, and elephant flows via a demand-aware topology. We show analytically and in simulations that Cerberus can improve throughput significantly compared to alternative approaches and operate datacenters at higher loads while being throughput-proportional.
Investigating e-Retailers’ Intentions to Adopt Cryptocurrency Considering the Mediation of Technostress and Technology Involvement

Cryptocurrencies have transgressed ever-changing economic trends in the global economy, owing to their conveyance, security, trust, and the ability to make transactions without the aid of formal institutions and governing bodies. However, the adoption of cryptocurrency remains low among stakeholders, including e-retailers. Thus, the current work explores the intentions of e-retailers in the Asia and Pacific region to adopt cryptocurrencies. This study considers the TAM-based SOR, with a combination of non-cognitive attributes (compatibility and convenience) proposed as stimuli for e-retailers to adopt the examined cryptocurrencies. The findings indicate that the proposed non-cognitive attributes are critical in determining e-retailers’ technostress (emotional state). Moreover, it was found that technostress among e-retailers profoundly impacts their intentions to adopt cryptocurrency in business settings. Meanwhile, regulatory support communication can be used to help regulatory bodies and governing institutions control the future economy worldwide. The proposed study offers significant theoretical and practical contributions through its investigation of e-retailers’ intentions to adopt cryptocurrency for the first time in the particular context of technostress and regulatory support.
Instance and Panoptic Segmentation Using Conditional Convolutions

We propose a simple yet effective framework for instance and panoptic segmentation, termed CondInst (conditional convolutions for instance and panoptic segmentation). In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance. In contrast, we propose to attend to the instances with dynamic conditional convolutions. Instead of using instance-wise ROIs as inputs to the instance mask head of fixed weights, we design dynamic instance-aware mask heads, conditioned on the instances to be predicted. CondInst enjoys three advantages: 1) Instance and panoptic segmentation are unified into a fully convolutional network, eliminating the need for ROI cropping and feature alignment. 2) The elimination of the ROI cropping also significantly improves the output instance mask resolution. 3) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference time per instance and making the overall inference time less relevant to the number of instances. We demonstrate a simpler method that can achieve improved accuracy and inference speed on both instance and panoptic segmentation tasks. On the COCO dataset, we outperform a few state-of-the-art methods. We hope that CondInst can be a strong baseline for instance and panoptic segmentation. Code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://git.io/AdelaiDet</uri> .
Self-Supervised Models are Continual Learners

Self-supervised models have been shown to produce comparable or better visual representations than their su-pervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically reduced in a Continual Learning (CL) scenario where data is presented to the model sequentially. In this paper, we show that self-supervised loss functions can be seamlessly converted into distillation mechanisms for CL by adding a predictor network that maps the current state of the repre-sentations to their past state. This enables us to devise a framework for Continual self-supervised visual representation Learning that (i) significantly improves the quality of the learned representations, (ii) is compatible with several state-of-the-art self-supervised objectives, and (iii) needs little to no hyperparameter tuning. We demonstrate the ef-fectiveness of our approach empirically by training six pop-ular self-supervised models in various CL settings. Code: github.com/DonkeyShot21/cassle.
Lexicon-based Twitter sentiment analysis for vote share prediction using emoji and N-gram features

Recently, Twitter sentiment analysis (TSA) has been successfully employed to monitor and forecast elections in many studies. However, most of the existing studies rely on extracting sentiments from explicit textual features. Moreover, only few studies have included non-textual features such as emojis for election forecasts. In this study, we incorporated N-gram features to predict vote shares of 2017 Uttar Pradesh (UP) legislative elections. Also, sentiment distribution of tweets containing emojis was significantly different from tweets without emojis. Therefore, emoji sentiments were detected and incorporated to predict the vote shares. We collected more than 0.3 million tweets, wherein geo-tagging was applied on search keywords that were not exclusive to elections. We employed seven lexicons for labelling tweets and compared two methods to reduce prediction error: sentiment magnitude-based criteria and polarity of tweets. Results show that proposed method of incorporating N-gram features and emoji sentiments significantly decreases prediction error.
Changes in the education landscape caused by COVID-19: Opportunities and challenges from UAE perspective

The study aim is to identify the opportunities for the United Arab Emirates (UAE) educational sector during COVID-19. A survey was conducted among a sample of 100 teachers from 20 UAE higher education institutions. Teachers use online learning tools that cannot fully provide the benefits of face-to-face meetings with students. The research methodology is based on primary and secondary data analysis. Teacher questionnaire was formed; it contains 9 questions on distance learning actualization in a pandemic. Only 25% of those surveyed agreed with the inclusiveness and ease of remote learning technologies use, 20% expected that after a lockdown, learning could return to the status quo; 55% disagreed that the technologies used stimulate creativity and have an evidence base. Among key findings are that almost all respondents (95%) see the necessary reform of distance education for its further adaptation in the post-COVID-19 period. The study shows a significant imperfection of the applied online learning technologies. Studies from around the world have shown that school shutdowns have had similar consequences for most education systems.&#x0D; &#x0D; Keywords: COVID-19; education; online learning; pandemic outbreak; teaching and learning.
ObjectDetect: A Real-Time Object Detection Framework for Advanced Driver Assistant Systems Using YOLOv5

In the recent times, there has been a lot of speculation related to advanced driver-assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as R-CNN, Fast R-CNN, and YOLO were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called “A Real-Time Object Detection Framework for Advanced Driver Assistant Systems” by implementing the state-of-the-art object detection algorithm YOLOv5 which improves the speed in detection of object over real-time. This paper provides a comparison between other state-of-the-art object detectors such as YOLOv3 and YOLOv4. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLOv5 proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called “ObjectDetect” which helps users make better decisions on the road. “ObjectDetect” consists of a simple user interface that displays alerts and warnings.