Reviewerly/exHarmony huggingface dataset:
Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory

Spaced repetition, namely, learners review items in a given schedule, has been proven powerful for memorization and practice of skills. Most current spaced repetition methods focus on either predicting student recall or designing an optimal review schedule, thus omitting the integrity of the spaced repetition system. In this work, we propose a novel spaced repetition schedule framework by capturing the dynamics of memory, which alternates memory prediction and schedule optimization to improve the efficiency of learners’ reviews. First, the framework collects logs from students’ reviews and builds memory models with Markov property to capture the dynamics of memory. Then, the spaced repetition optimization is transformed a stochastic shortest path problem and solved via the value iteration method. We also construct a new benchmark dataset for spaced repetition, which is the first to contain time-series information during learners’ memorization. Experimental results on the collected data from the real world and the simulated environment demonstrate that the proposed approach reduces 64% error and 17% cost in predicting recall rates and optimizing schedules compared to several baselines. We have publicly released the dataset containing 220 million rows and codes used in this paper at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/maimemo/SSP-MMC-Plus</uri> .
GAN-LSTM-Based ADS-B Attack Detection in the Context of Air Traffic Control

Automatic dependent surveillance–broadcasting (ADS-B) is a surveillance technology strongly promoted by the International Civil Aviation Organization. It has been widely used in commercial and general aviation, to provide support for the normal operation of air traffic control (ATC) in commercial aviation. However, the openness of the ADS-B protocol makes it extremely vulnerable to cyber attacks. Previous research did not specifically consider the application scenarios of ATC in commercial air transport, and there is a problem of low attack detection rates. This article focuses on ADS-B attack detection under the background of ATC. We combine the flight plan with ADS-B information to construct an airspace flight image stream and process the image stream using a generative adversarial network–long short-term memory (GAN-LSTM) model to predict future images. Then, we identify abnormal images based on the normalized cross-correlation and mark anomalous targets. Our method can quickly locate anomalous targets in the controlled airspace. Based on real flight data, abnormal data for various malicious attacks were forged. We evaluated the detection performance of the method through a confusion matrix and several performance indices. The final experimental results showed that our detection scheme exhibited good detection performances for various attacks, with an average detection accuracy of 92.3%, a false positive rate of 11.9%, and a false negative rate of 6.2%. This approach guarantees the information security of ADS-B, thereby improving the operational security of ATC.
Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

The automatic and accurate classification of medical imaging data has potential applications in computer-aided disease diagnosis, prognosis, and treatment. However, it remains a challenge to optimize recent deep learning algorithms in medical domain for the accurate classification of large-scale 3D volumetric data. To address these challenges, we propose an efficient deep volumetric classification network based on the aggregation of multilevel deep features for accurate classification of large-scale medical 2D/3D imaging data. To perform a detailed quantitative analysis of our method, 26 different datasets were fused to construct a single large-scale multimodal database that comprises a total of seventy different classes, including 151,095 data samples. Additionally, 15 different baseline methods were configured under the same experimental protocol for volumetric model genesis and extensive performance comparison with our method. The experimental results of our method exhibited promising performance as area under the curve of 93.66% and outperformed various state-of-the-art methods.
Efficient ADMM-Based Algorithm for Regularized Minimax Approximation

Minimax approximations have found many applications but are lack of efficient solution algorithms for large-scale problems. Based on the alternating direction method of multipliers (ADMM) for convex optimization, this letter presents an efficient scalarwise algorithm for a regularized minimax approximation problem. The ADMM-based algorithm is then applied in the minimax design of two-dimensional (2-D) digital filters and the training of randomized neural networks for regression on a realworld benchmark dataset. Experimental results demonstrate the fast convergence rate and low computational complexity of the proposed algorithm, as well as the good approximation/prediction performance of the learned approximation model.
Fuzzy Representation Learning on Graph

Recent years have witnessed a drastic surge in graph representation learning, which usually produces low-dimensional and crisp representations from graph topology and high-dimensional node attributes. Nevertheless, a crisp representation of a node or graph actually conceals the uncertainty and interpretability of features. In citation networks, for example, the reference between two papers is always involved with fuzziness denoting the correlation degrees, that is, one connection may simultaneously belong to strong and weak references in different beliefs. The uncertainty in node connections and attributes inspires us to delve into fuzzy representations. This paper, for the first time, proposes an unsupervised fuzzy representation learning model for graphs and networks to improve their expressiveness by making crisp representations fuzzy. Specifically, we develop a fuzzy graph convolution neural network (FGCNN), which could aggregate high-level fuzzy features, leveraging fuzzy logic to fully excavate feature-level uncertainties, and finally generate fuzzy representations. The corresponding hierarchical model composed of multiple FGCNNs, called deep fuzzy graph convolution neural network (DFGCNN), is able to generate fuzzy node representations which are more expressive than crisp ones. Experimental results of multiple network analysis tasks validate that the proposed fuzzy representation models have strong competitiveness against the state-of-the-art baselines over several real-world datasets.
PoiseNet: Dealing With Data Imbalance in DensePose

Data imbalance, a foundational problem in machine learning, has received little attention in DensePose and has become one of the main obstacles in front of existing methods. We reveal two imbalances in DensePose: inter-surface imbalance and intra-surface imbalance. First, the human body parts can be of various sizes, making 3D surfaces contain different numbers of annotations. Classifiers trained on such unbalanced data suffer a decline on surfaces with fewer annotations. Second, annotations within each surface are not uniformly distributed. Regressors trained on such uneven data suffer a decline in 3D surface areas with sparse annotations. To solve these imbalances, we propose PoiseNet which integrates adaptive equalization loss (AEQL) and block balanced localization (BBL). Specifically, to address the inter-surface imbalance, AEQL adaptively reweights surfaces based on the number of annotations and the classification scores. BBL alleviates the intra-surface imbalance by unevenly blocking each surface according to annotation distribution. Experimental results on the DensePose-COCO dataset show that our PoiseNet surpasses baselines by up to 1.4 AP.
PAACDA: Comprehensive Data Corruption Detection Algorithm

With the advent of technology, data and its analysis are no longer just values and attributes strewn across spreadsheets, they are now seen as a stepping stone to bring about revolution in any significant field. Data corruption can be brought about by a variety of unethical and illegal sources, making it crucial to develop a method that is highly effective to identify and appropriately highlight the various corrupted data existing in the dataset. Detection of corrupted data, as well as recovering data from a corrupted dataset, is a challenging problem. This requires utmost importance and if not addressed at earlier stages may pose problems in later stages of data processing with machine or deep learning algorithms. In the following work we begin by introducing the PAACDA: Proximity based Adamic Adar Corruption Detection Algorithm and consolidating the results whilst particularly accentuating the detection of corrupted data rather than outliers. Current state of the art models, such as Isolation forest, DBSCAN also called “Density-Based Spatial Clustering of Applications with Noise” and others, are reliant on fine-tuning parameters to provide high accuracy and recall, but they also have a significant level of uncertainty when factoring the corrupted data. In the present work, the authors look into the most niche performance issues of several unsupervised learning algorithms for linear and clustered corrupted datasets. Also, a novel PAACDA algorithm is proposed which outperforms other unsupervised learning benchmarks on 15 popular baselines including K-means clustering, Isolation forest and LOF (Local Outlier Factor) with an accuracy of 96.35% for clustered data and 99.04% for linear data. This article also conducts a thorough exploration of the relevant literature from the previously stated perspectives. In this research work, we pinpoint all the shortcomings of the present techniques and draw direction for future work in this field.
Novel Surrogate Measures Based on a Similarity Network for Neural Architecture Search

We propose two novel surrogate measures to predict the validation accuracy of the classification produced by a given neural architecture, thus eliminating the need to train it, in order to speed up neural architecture search (NAS).The surrogate measures are based on a solution similarity network, where distance between solutions is measured using the binary encoding of some graph sub-components of the neural architectures.These surrogate measures are implemented within local search and differential evolution algorithms and tested on NAS-Bench-101 and NAS-Bench-301 datasets.The results show that the performance of the similarity-network-based predictors, as measured by correlation between predicted and true accuracy values, are comparable to the state-of-the-art predictors in the literature, however they are significantly faster in achieving these high correlation values for NAS-Bench-101.Furthermore, in some cases, the use of these predictors significantly improves the search performance of the equivalent algorithm (differential evolution or local search) that does not use the predictor.
Efficient Supersingularity Testing of Elliptic Curves Using Legendre Curves

There are two types of elliptic curves, ordinary elliptic curves and supersingular elliptic curves. In 2012, Sutherland proposed an efficient and almost deterministic algorithm for determining whether a given curve is ordinary or supersingular. Sutherland's algorithm is based on sequences of isogenies started from the input curve, and computation of each isogeny requires square root computations, which is the dominant cost of the algorithm. In this paper, we reduce this dominant cost of Sutherland's algorithm to approximately a half of the original. In contrast to Sutherland's algorithm using j-invariants and modular polynomials, our proposed algorithm is based on Legendre form of elliptic curves, which simplifies the expression of each isogeny. Moreover, by carefully selecting the type of isogenies to be computed, we succeeded in gathering square root computations at two consecutive steps of Sutherland's algorithm into just a single fourth root computation (with experimentally almost the same cost as a single square root computation). The results of our experiments using Magma are supporting our argument; for cases of characteristic p of 768-bit to 1024-bit lengths, our proposed algorithm for characteristic p≡1 (mod 4) runs in about 61.5% of the time and for characteristic p≡3 (mod 4) also runs in about 54.9% of the time compared to Sutherland's algorithm.
Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand.However, the use of intelligent black-box models can hinder the decision support system's potential to be fully adopted because opaque processes raise suspicions and doubts among careful decision makers.Moreover, appropriate and comprehensible explanations may foster trustworthiness and allow for reasonable adjustments or even corrections.This work proposed an approach that incorporates three reasonability aspects into Decision Systems: feasibility, rationality, and plausibility.Thus, by providing decision makers with reasonable candidate solutions for a complex problem, they are expected to perform their tasks more effectively (i.e.decide with more efficiency as well as efficacy).The new approach is accompanied by two proofs of concept in the health and public security areas.Comparative results using random and rational approaches, including the simulation of distinct user profiles, are presented.The proposed approach achieved superior metrics with regard to feasibility and plausibility, suggesting that this proposition can be applied to real-world applications.
Forward Secure Message Franking with Updatable Reporting Tags

Message franking is introduced by Facebook in end-to-end encrypted messaging services. It allows to produce verifiable reports of malicious messages by including cryptographic proofs, called reporting tags, generated by Facebook. Recently, Grubbs et al. (CRYPTO'17) proceeded with the formal study of message franking and introduced committing authenticated encryption with associated data (CAEAD) as a core primitive for obtaining message franking. In this work, we aim to enhance the security of message franking and introduce forward security and updates of reporting tags for message franking. Forward security guarantees the security associated with the past keys even if the current keys are exposed and updates of reporting tags allow for reporting malicious messages after keys are updated. To this end, we firstly propose the notion of key-evolving message franking with updatable reporting tags including additional key and reporting tag update algorithms. Then, we formalize five security requirements: confidentiality, ciphertext integrity, unforgeability, receiver binding, and sender binding. Finally, we show a construction of forward secure message franking with updatable reporting tags based on CAEAD, forward secure pseudorandom generator, and updatable message authentication code.
Designing Synchronization Patterns Minimizing the Latency and the Computational Complexity for Inaudible Sound Communication Systems

The inaudible sound communication system of our implementation estimates the correct positions of received symbols in its decoding process by calculating the correlations with the pre-defined synchronization patterns. This paper describes that a theoretical approach based on solving Euler graphs taking account of constraint conditions may potentially provide more efficient synchronization patterns, compared with the conventional technique that employs random sequences for designing synchronization patterns.
A Light-Weighted Hypergraph Neural Network for Multimodal Remote Sensing Image Retrieval

With the continuous maturity of remote sensing technology, the obtained remote sensing images' quality and quantity have surpassed any previous period. In this context, the content-based remote sensing image retrieval (CBRSIR) task attracts a lot of attention and research interest. Nowadays, the previous CBRSIR works mainly face the following problems. First of all, few works can realize one to many cross-modal image retrieval task (such as using optical image to retrieve SAR, optical images at the same time); secondly, researches mainly focus on small-area, target-level retrieval, and few on semantic-level retrieval of the whole image; last but not the least, most of the existing networks are characterized by massive parameters and huge computing need, which cannot be applied to resource-constrained edge devices with power and storage limit. For the sake of alleviating these bottlenecks, the paper introduces a novel light-weighted non-local semantic fusion network based on hypergraph structure for CBRSIR (abbreviated as HGNLSF-Net). Specifically, in the framework, using the topological characteristics of hypergraph, the relationship among multiple nodes can be modeled, so as to understand the global features on remote sensing images better with fewer parameters and less computation. In addition, since the non-local semantics often involves a lot of noise, the hard-link module is constructed to filter noise. A series of experimental results on typical CBRSIR dataset, i.e., MMRSIRD well show that with fewer parameters, the proposed HGNLSF-Net outperforms other methods and achieves optimal retrieval performance.
Salient Knowledge-Based Object Detection

Human use their visual systems to perceive the interest objects in the images and videos with the past experience including shapes, textures, spatial knowledge and other subconscious information. In this paper, we develop an end-to-end object detection framework, combining with salient knowledge of objects. Firstly, we use the convolutional neural networks(CNNs) to extract the multi-scales feature maps representing the normal knowledge of objects in the images and videos. Then, the candidate feature map is selected from the extracted feature maps to encode the salient knowledge of objects using the mathematical strategy, and the new feature map is generated using the candidate feature map and the salient knowledge of objects. Thirdly, we use the feature map combining with salient knowledge and other feature maps at different scales to identify and localize the objects in the images and videos. The results show that our proposed approach can achieve better performance than other attention-based object detectors on PASCAL VOC 2007 and PASCAL VOC 2012, and this indicates the predicted results of our approach have a good consistency with the object's perception of human brains. At the same time, our approach can process 43 frames per second on the device NVIDIA GTX1080, and is more practical from the efficiency of running time.
Breast Cancer Detection and Prediction using Federated Multicriteria Machine Learning

Breast cancer is still one of the most common cancers in women, and it is also the leading cause of mortality among women. Breast cancer detection has been improved using a variety of Machine Learning and Deep Learning techniques. Federated Learning is a distributed learning technique that allows models to be trained on a vast set of decentralized data. In this paper we present a new Multiple Criteria Optimization approach for Federated Learning. We use Goal Programming in particular to address the Federated Learning problem's diverse set of objectives. Numerical experiments also demonstrate the efficacy of this strategy in terms of performance.
Developing Low-Cost Mobile Immersive System MA-VRIOT with Physical Activity Interactions by Integrating IOT Technology

Immersive gamification technologies has been applied in education, skill training, marketing, product design, patients' rehabilitation and so on, by integrating virtual reality technology and gamification mechanism design. These tow technologies are generally usable for improve user engagement. For the considerations of low-cost, popularity, epidemic prevention and sustainability requirements, mobile virtual reality is more commonly used in education. But some constraints limited the interactions of mobile virtual reality, such as hardware computing ability, multi-style interactions development. The hardware computing is not our present concern. In term of interaction, except the head rotation, the human-computer interaction spatial is limited in the front of or in range of head. The aim of this paper is to integrate the IOT technology in immersive gamification system for having flexible, scalable, adaptable and extensible interactions in mobile immersive system development with physical activity interaction, as named as MA-VRIOT. A prototype of MA-VRIOT, by integrating AIOT, google cardboard VR, Unity game engine technologies and gamification mechanisms, to develop a sustainable topic game has been developed for further user engagement measurement.
Human Factors for the Adoption of Blockchain Technology: A Case of the Australian Retail Sector

Australian retail sector has fallen victim to the recent lockdowns due to COVID-19 pandemic, which forced retailers to adopt new technologies to conduct business online. As a result, several businesses are attempting to use Blockchain technology to enhance security and promote transparency. Prior studies indicated the importance of human factors in technology adoption decisions. However, there is limited research on how human factors play out in blockchain technology adoption decisions in the Australian retail sector. Therefore, this study identifies the human factors and presents a research model to investigate their influence on technology adoption decisions in the Australian retail sector. The proposed study will use a quantitative approach and collects data using online survey questionnaires. This study will test the derived hypotheses using the structural equation modelling technique. This study is expected to help develop appropriate policies to enhance blockchain technology adoption in the Australian retail sector.
StyleFormerGAN-VC:Improving Effect of few shot Cross-Lingual Voice Conversion Using VAE-StarGAN and Attention-AdaIN

Voice Conversion (VC) aims to transfer the speaker timbre while retaining the lexical content of the source speech and has attracted much attention lately. Although previous VC models have achieved good performance, unstability can not be avoided when it comes cross-lingual scenario. In this paper, we propose the StyleFormerGAN-VC to achieve better cross language speech conversion, where variational auto-encoder is introduced to model the feature distribution of the cross-lingual utterances and adversarial training is applied to elevate the speech quality. In addition, we combine the Attention mechanism and AdaIN to make our model more generalized to unseen speaker with long utterance. Experiments show that our model performs stably in the cross-lingual scenario and gains well MOS evaluation scores.