Reviewerly/exHarmony huggingface dataset:
A Novel Ensemble Method of RVFL For Classification Problem

Ensemble learning methods, which combine several base classifiers, is a common technique to enhance the classification ability of ensemble models in the field of pattern recognition and machine learning. Rotation Forest, an ensemble algorithm, has been used widely in various fields with nice generalization performance. The main idea of Rotation Forest is to animate concurrently both diversity and individual accuracy within the ensemble. On the other hand, random vector functional link (RVFL) neural network, a randomized version of single layer feed-forward neural network (SLFN), is a successful model because of its universal approximation property. In this paper, we propose a novel ensemble method, known as rotated random vector functional link neural network (RoF-RVFL), which combines rotation forest (RoF) and RVFL classifiers. To verify the effectiveness of the proposed RoF- RVFL method, empirical comparisons are carried out among Rotation Forest (RoF), Random Forest (RaF), RVFL and the proposed RoF-RVFL method over 42 UCI benchmark datasets. The experimental results show that the proposed RoF-Rvflmethod is able to generate more robust network with better generalization performance.
Digital contact-tracing in France: uptake by COVID-19 risk factor and by exposure risk

Few studies have investigated the influence of COVID-19 conspiracy theories on digital contact-tracing adoption and the differentiated uptake of digital contact-tracing by COVID-19 risk factor and by exposure risk.Using a cross-sectional survey conducted in France in November 2020 (N = 1042), we investigate the factors associated with the use of the French 'TousAntiCovid' contact-tracing application. Our independent variables of interest include COVID-19 and 'TousAntiCovid' perceptions, trust in the government, time and risk preferences and the level of adherence to COVID-19 conspiracy theories. We conduct regression analyses by COVID-19 risk factor and exposure groups.Among the full sample, a negative association is found between the propensity to believe in COVID-19 conspiracy theories and the use of 'TousAntiCovid'. French respondents at risk of severe COVID-19 form are more likely to use 'TousAntiCovid'. No difference in uptake is found by exposure group. Group analyses indicate that the factors associated with the uptake of digital contact-tracing differ by COVID-19 risk factor and exposure risk.Governmental communication to fight COVID-19 misinformation and to stress out the utility and data safety of 'TousAntiCovid' should be reinforced. Targeted communication campaigns should be conducted among low adoption groups and key groups in COVID-19 transmission.
kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation

Accurate and automatic medical image segmentation is challenging due to significant size and shape variations of objects (e.g., in multi-scales) and missing/blurring object borders. In this paper, we propose a new deeply supervised k-complete-bipartite network with asymmetric convolutions (kCBAC-Net) to exploit multi-scale features and improve the capability of standard convolutions for segmentation. (1) We leverage a generalized complete bipartite network to reuse multi-scale features, consolidate feature hierarchies at different scales, and preserve maximum information flow between encoder and decoder layers. (2) To further capture multi-scale information, we sequentially connect k complete bipartite network modules together to facilitate their processing in different image scales. (3) We replace the standard convolution by asymmetric convolution block to strengthen the central skeleton parts of standard convolution, enhancing the model's robustness on exploiting more discriminative features. (4) We employ auxiliary deep supervisions to boost information flow in the network and extract highly discriminative features. We evaluate our kCBAC-Net on three datasets (ultrasound lymph node segmentation (2D), 2017 ISIC Skin Lesion segmentation (2D), and MM-WHS CT (3D)), achieving state-of-the-art performance.
Vision-based Hand Gesture Recognition for Human-Computer Interaction using MobileNetV2

In recent years, the demand for gesture recognition has increased enormously due to many applications such as computer games, human-robot interaction, assistance systems, sports, sign language interpreters, and e-commerce. The recognition of hand gestures is one of the most important gesture recognition methods. With simple hand gestures, devices in the smart home area (TV, radio, vacuum cleaner robots, etc.) should be easier to operate. Our method is based on a convolutional neural network, or more precisely on MobileNetV2. With this lean and fast network, we have been able to achieve an accuracy of 99.96 percent in recognition of hand gestures, so that in the future, we will be able to offer an application in the field of Human-Computer Interaction to interact more easily with the ever-increasing number of technologies in everyday life.
Modeling Coverage for Non-Autoregressive Neural Machine Translation

Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously. Despite its high efficiency, NAT usually suffers from two kinds of translation errors: over-translation (e.g. repeated tokens) and under-translation (e.g. missing translations), which eventually limits the translation quality. In this paper, we argue that these issues of NAT can be addressed through coverage modeling, which has been proved to be useful in autoregressive decoding. We propose a novel Coverage-NAT to model the coverage information directly by a token-level coverage iterative refinement mechanism and a sentence-level coverage agreement, which can remind the model if a source token has been translated or not and improve the semantics consistency between the translation and the source, respectively. Experimental results on WMT14 En↔De and WMT16 En↔Ro translation tasks show that our method can alleviate those errors and achieve strong improvements over the baseline system.
Survey of Real-Time Object Detection for Logo Detection System

Image coordinating and acknowledgment are the essence of computer vision, and they have a significant part to play in regular daily existences. In this paper, we present a profound report for object location in pictures and live recordings looking at different calculations. Through this paper, the different models of item location and acknowledgment which is significant for brand notice and observation application are contemplated. By the ceaseless exertion of endless scientists, profound learning calculations are developing quickly with an improved article recognition execution. Different mainstream applications like passerby recognition, clinical imaging, advanced mechanics, self-driving vehicles, face identification, and so on lessen the endeavors of people in numerous zones. Because of the immense field and different cutting-edge calculations, it is a monotonous errand to cover at the same time. The wide reach utilization of visual information from organizations, foundations, people, and social frameworks like "Gleam," "YouTube," and so on is for wide dissemination and sharing of pictures and live recordings. This paper presents the principal outline of article recognition strategies. This is done by dividing the algorithms into two class detectors. In two-phase detector, secured algorithms are speeded-up robust feature (SURF), convolutional neural networks (CNN), region-based convolutional neural networks (RCNN), fast RCNN, faster RCNN, and in one-phase detector, You Only Look Once (YOLO), YOLOV3, SlimYOLOV3, and YOLOv4 are secured. Two-phase detectors focus more around precision, while the essential worry of one-phase detectors is speed. The principle point of this exploration is to consider the different strategies and difficulties of each and every technique for real-time object detection.
Instance-Based Feature Pyramid for Visual Object Tracking

The deep learning based methods have improved the visual tracking precision significantly. However, the background distraction and the high precise localization remain challenging problems. Despite that some methods have fused the deep and shallow layer features to solve these problems, the existing fusion methods, like simply concatenating or adding the features from the different layers, cannot take the advantage of both the deep and shallow layer features fully. In this paper, we propose a new adaptive feature fusion method, called the instance-based feature pyramid (IBFP) to obtain the discriminative high-resolution feature, which not only inherits the discriminative information from the deep layer feature, but also keeps the high precision localization information of the shallow layer feature. For utilizing the deep and shallow features effectively, we design an instance-based upsampling (IBU) module to fuse them, and a compressed space channel selection (CSCS) module to re-weight the feature channels adaptively. We insert the IBU and CSCS modules in the Siamese tracker for end-to-end training and testing. By using the proposed IBU and CSCS modules, we fuse the deep and shallow features in a series manner. Experiments on large-scale benchmark datasets demonstrate that the proposed modules boost the capabilities of distinguishing the targets and the similar distractors and perform favorably against the state-of-the-art.
Cloud-Free Sea-Surface-Temperature Image Reconstruction From Anomaly Inpainting Network

Sea-surface temperature (SST) images obtained by satellites contain noise and missing SSTs due to cloud covers. We propose a method for reconstructing denoised cloud-free SST images via deep-learning-based image inpainting. For denoizing, we use data-assimilation images to train a reconstruction network by considering the physical correctness of SSTs. For reconstruction stability, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anomaly inpainting network</i> , which does not directly complete missing SSTs but estimates the difference between the unobserved SSTs and the average SSTs. SSTs do not fluctuate much over a few days; thus, we can use recent average SSTs as a rough estimation of SSTs and can assume that the SST difference will be within a specific range. We conducted experiments to evaluate our method with satellite SST images and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SST data. The results indicate that our method with anomaly inpainting network qualitatively and quantitatively outperformed conventional SST image inpainting methods.
DC-Net: Dual Context Network for 2D Medical Image Segmentation

Medical image segmentation is essential for disease diagnosis analysis. There are many variants of U-Net that are based on attention mechanism and dense connections have made progress. However, CNN-based U-Net lacks the ability to capture the global context, and the context information of different scales is not effectively integrated. These limitations lead to the loss of potential context information. In this work, we propose a Dual Context Network (DC-Net) to aggregate global context and fuse multi-scale context for 2D medical image segmentation. In order to aggregate the global context, we present the Global Context Transformer Encoder (GCTE), which reshapes the original image and the multi-scale feature maps into a sequence of image patches, and combines the advantages of Transformer Encoder on global context aggregation to improve the performance of encoder. For the fusion of multi-scale context, we propose the Adaptive Context Fusion Module (ACFM) to adaptively fuse context information by learning Adaptive Spatial Weights and Adaptive Channel Weights to improve the performance of decoder. We apply our DC-Net with GCTE and ACFM to skin lesion segmentation and cell contour segmentation tasks, experimental results show that our method can outperform other advanced methods and get state-of-the-art performance.
Remote Sensing Image Vehicle Detection Based on Pre-Training and Random-Initialized Fusion Network

Vehicle detection based on remote sensing images is of great significance to intelligent traffic management and smart city planning. The performances of the mainstream vehicle detection methods such as Fast-RCNN, SSD, FCRN, etc. are mainly affected by their extracted features, whose richness and robustness can be enhanced by introducing pre-training models from other datasets. However, the weights of the pre-training models used in traditional methods like FCRN, only shallow-tuned from the weights of another database training, without deep optimization from random initialization. This letter proposes a new pre-training and random-initialized fusion network (PRFN): the random initialization model and the pre-training model are connected in parallel, and an encoder is used to fuse their features, thereby improving the richness of features and the robustness of the model. We evaluate four different combination patterns of the pre-training and random initialized models, and the effect of the fusion feature encoder. Experiments show the parallel concatenating pattern with the fusion feature encoder achieves the best performance than other patterns. We test on three datasets, our PRFN exceeds the traditional methods, and, respectively, 1.23%, 1.04%, 0.94% higher than the baseline. All codes are available from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LHKRobert/PRFN</uri> .
Usability Testing of Vocabulary Game Prototype Using the Nielsen's Attributes of Usability (NAU) Method

A prototype of vocabulary game was developed for student who wants to learn English. With this application, user can play various game while learning English. The prototype was evaluated through usability testing to access its learnability, efficiency, memorability, error, and satisfaction. In the end, it was found that vocabulary game learnability rate was 74,4%, efficiency rate was 82,3%, memorability rate was 84,1%, error rats was 49,5%, and lastly 80,2% for satisfaction. Method used in this research was literature study carried-out includes material of User Interface, Usability Testing, and Nielsen’s Attribute of Usability Model tested using SEM, Smart PLS as statistic tools. Then from 47 respondents, who were asked to use the interactive UI, they had to run Usability Testing and answered the Post-Test Questionnaire. The result found that 2 hypotheses rejected and 3 hypotheses accepted.
Federated Variational Autoencoder for Collaborative Filtering

Recommender Systems (RSs) are valuable technologies that help users in their decision-making process. Generally, RSs are designed with the assumption that a central server stores and manages historical users' behaviors. However, users are nowadays more aware of privacy issues leading to a higher demand for privacy-preserving technologies. To cope with this issue, the Federated Learning (FL) paradigm can provide good performance without harming the users' privacy. Some efforts have been devoted to adapt standard collaborative filtering methods (e.g., matrix factorization) into the FL framework in recent years. In this paper, we present a Federated Variational Autoencoder for Collaborative Filtering (FedVAE), which extends the state-of-the-art MultVAE model. Additionally, we propose an adaptive learning rate schedule to accelerate learning. We also discuss the potential privacy-preserving capabilities of FedVAE. An extensive experimental evaluation on five benchmark data sets shows that our proposal can achieve performance close to MultVAE in a reasonable number of iterations. We also empirically demonstrate that the adaptive learning rate guarantees both accelerated learning and good stability.
Measuring Illicit Activity in DeFi: The Case of Ethereum

We analyze the magnitude of illicit activities in the Ethereum ecosystem. Using proprietary labeling data from the Blockchain Intelligence Group (BIG), we investigate the characteristics of a number of “malicious” Ethereum addresses. We first calculate the total number of transactions involving these addresses and the total amount of funds transferred through them, and then characterize smart contract addresses for ERC-20 tokens or DeFi applications, that the malicious addresses interact with. Finally, we apply machine learning techniques to identify additional “malicious” addresses by conducting a network clustering analysis within all Ethereum addresses from transactional relationships with the initial set of malicious addresses.
AR Scribble: Evaluating Design Patterns for Augmented Reality User Interfaces

The virtual enhancement of the physical world through Augmented Reality (AR) has an enormous potential in its application, but faces challenges in its development. The lack of standards and the increased complexity of interaction opportunities complicate the definition of suitable User Interfaces (UIs). Several principles and patterns have been formulated to simplify UI design for AR applications, but their joint contribution to a positive usability as well as the influence of individual patterns remain unclear. In this paper we merged design principles for AR to formulate a comprehensive pattern model. Based on this model, we developed AR Scribble, a mobile AR application which imitates a physical spray can to virtually sketch within a real environment. In a user-based study, we evaluated the usability of AR Scribble as well as the role of individual patterns for the overall usability. We found promising indications that the pattern model implementation is related to a positive usability. The individual pattern analysis showed that AR users particularly desire a consistent and structured UI. A consistent appealing design and multimodal interaction concepts were also found to positively correlate with the overall usability.
Sensing Textures

Sensing Textures: Tactile Resistance is a collection of eight swatches comprised of e-textile sensors and actuators that probe ideas around the politics of crafting handmade technological artifacts. By engaging hands-on with the production of these artifacts, this work aims to reflect on alternative modes of world-building that allow for speculation about how bespoke technology can look and feel like when it is highly personalized. These eight swatches explore different crafting techniques while responding to personal narratives, questions about how we perceive the world and how we embody the making of technology collectively in a critical manner.
Semi-supervised Semantic Visualization for Networked Documents

Semantic interpretability and visual expressivity are important objectives in exploratory analysis of text. On the one hand, while some documents may have explicit categories, we could develop a better understanding of a corpus by studying its finer-grained structures, which may be latent. By inferring latent topics and discovering keywords associated with each topic, one obtains a semantic interpretation of the corpus. One the other hand, by visualizing documents, latent topics, and category labels on the same plot, one gains a bird's eye view of the relationships among documents, topics, and various categories. Semantic visualization is a class of methods that unify both topic modeling and visualization. In this paper, we propose a novel semantic visualization model for networked documents that incorporates partial labels. We introduce coordinate-based label distribution and label-dependent topic distribution to visualize documents, topics, and labels in a semi-supervised way. We further derive three variants for singly-labeled, multi-labeled, and hierarchically-labeled documents. The focus on semi-supervision that employs variants of labeling structures is particularly novel. Experiments verify the efficacy of our model against baselines.
Towards An Optimized Dragonfly Algorithm Using Hill Climbing Local Search To Tackle The Low Exploitation Problem

The Dragonfly Algorithm (DA), a swarm intelligence algorithm inspired by the behavior of dragonflies, has been recently proposed and it was found to have a higher performance as compared to other swarm intelligence algorithms. However, it still has certain limitations, and its performance can be further improved. DA has a low exploitation phase, and this leads to problems such as low accuracy of solutions, falling in local optima and low convergence rate. We propose to enhance the exploitation of DA by using the hill climbing algorithm as a local search so as to increase its effectiveness and efficiency in producing high accuracy solutions. The hill climbing algorithm is chosen to be used as a local search for DA since it always optimizes the current solution until the local optima is obtained However, it has not been employed in any existing hybrid of DA. The proposed algorithm performance is to be evaluated by employing it as a training algorithm for an Artificial Neural Network (ANN) to optimize its connection weights. A classification dataset will be used for the training and testing of the ANN trained by the proposed algorithm. The root mean squared error of the ANN will be taken as the objective function of the optimized dragonfly algorithm. The accuracy of the resultant neural network will be compared to that of an ANN trained by the original DA and the time taken for the training process by both algorithms will be compared. Based on the analysis, the optimized algorithm is expected to outperform the original DA by allowing the resultant neural network to have a higher classification accuracy.
Constructing Chinese Historical Literature Knowledge Graph Based on BERT

Knowledge graph construction (KGC) aims to organize knowledge into a semantic network which can reveal relations between entities. Its basis is named entity recognition (NER) and relation extraction (RE) tasks. In recent years, KGC methods for Chinese have made great progress. However, most existing methods concentrate on modern Chinese and ignore the classical Chinese due to its complexity, making research in this field relatively lacking. In this paper, we construct a high-quality classical Chinese labeled dataset for NER and RE tasks. More specifically, we conduct a series of experiments to select an optimal NER model to strengthen the whole pipeline model for NER and RE tasks, augmenting our dataset iteratively and automatically. Additionally, we propose an improved RE model to better combine semantic entity information extracted by the NER model. Moreover, we construct a knowledge graph (KG) based on Chinese historical literature and design a visualization system with intuitive display and query functions.
CANs: Coupled-Attention Networks for Sarcasm Detection on Social Media

In recent years, sarcasm detection on social media has become one of the major challenges in natural language processing, due to the figurative characteristics of sarcasm. With the rise of social media platforms, especially Twitter, which allows users to post textual content while attaching a complement (such as image and video). A prevailing trend is to achieve a satire effect via combinations of text and image on Twitter. Therefore, to overcome the above-mentioned difficulty of sarcasm detection on social media, the key is to establish a multi-modal framework that can synchronically capture the information contained in text and vision. In this research, we propose Coupled-Attention Networks (CANs), which can effectively integrate information of text and image into a unified framework, thus realizing the fusion of different forms of resources. We conduct our experiments on a real-world dataset. Experimental results prove that our method achieves excellent results.