Reviewerly/exHarmony huggingface dataset:
Graph Few-shot Class-incremental Learning

The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommendation systems, E-commerce platforms, etc. can be represented by graph models. In this paper, we investigate the challenging yet practical problem,Graph Few-shot Class-incremental (Graph FCL) problem, where the graph model is tasked to classify both newly encountered classes and previously learned classes. Towards that purpose, we put forward a Graph Pseudo Incremental Learning paradigm by sampling tasks recurrently from the base classes, so as to produce an arbitrary number of training episodes for our model to practice the incremental learning skill. Furthermore, we design a Hierarchical-Attention-based Graph Meta-learning framework, HAG-Meta from an optimization perspective. We present a task-sensitive regularizer calculated from task-level attention and node class prototypes to mitigate overfitting onto either novel or base classes. To employ the topological knowledge, we add a node-level attention module to adjust the prototype representation. Our model not only achieves greater stability of old knowledge consolidation, but also acquires advantageous adaptability to new knowledge with very limited data samples. Extensive experiments on three real-world datasets, including Amazon-clothing, Reddit, and DBLP, show that our framework demonstrates remarkable advantages in comparison with the baseline and other related state-of-the-art methods.
Blockchain-Cloud Transparent Data Marketing: Consortium Management and Fairness

Data are generated by Internet of Things (IoT) devices and centralized at a cloud server, that can later be traded with third parties, i.e., data marketing, to enable various data-intensive applications. However, the centralized approach is recently under debate due to the lack of (1) transparent and distributed marketplace management, and (2) marketing fairness for both IoT users (data sellers) and third parties (data buyers). In this paper, we propose a Blockchain-Cloud Transparent Data Marketing (Block-DM) with consortium management and executable fairness. First, we introduce a hybrid data-marketing architecture, where the cloud acts as an efficient data management unit and a consortium blockchain serves as a transparent marketing controller. Under the architecture, consent-based secure data trading and identity privacy for data owners are achieved with the distributed credential issuance and threshold credential openings. Second, with a consortium committee, we design a fair on/off-chain data marketing protocol. By financial incentives and succinct ‘commitments’ of marketing operations, the protocol can achieve the marketing fairness and effective detection of unfair marketing operations. We demonstrate the security of Block-DM with thorough analysis. We conduct extensive experiments with a consortium blockchain network on Hyperledger Fabric to show the feasibility and practicality of Block-DM.
Meta PID Attention Network for Flexible and Efficient Real-World Noisy Image Denoising

Recent deep convolutional neural networks for real-world noisy image denoising have shown a huge boost in performance by training a well-engineered network over external image pairs. However, most of these methods are generally trained with supervision. Once the testing data is no longer compatible with the training conditions, they can exhibit poor generalization and easily result in severe overfitting or degrading performances. To tackle this barrier, we propose a novel denoising algorithm, dubbed as Meta PID Attention Network (MPA-Net). Our MPA-Net is built based upon stacking Meta PID Attention Modules (MPAMs). In each MPAM, we utilize a second-order attention module (SAM) to exploit the channel-wise feature correlations with second-order statistics, which are then adaptively updated via a proportional-integral-derivative (PID) guided meta-learning framework. This learning framework exerts the unique property of the PID controller and meta-learning scheme to dynamically generate filter weights for beneficial update of the extracted features within a feedback control system. Moreover, the dynamic nature of the framework enables the generated weights to be flexibly tweaked according to the input at test time. Thus, MPAM not only achieves discriminative feature learning, but also facilitates a robust generalization ability on distinct noises for real images. Extensive experiments on ten datasets are conducted to inspect the effectiveness of the proposed MPA-Net quantitatively and qualitatively, which demonstrates both its superior denoising performance and promising generalization ability that goes beyond those of the state-of-the-art denoising methods.
A Certificateless Signcryption Mechanism Based on Blockchain for Edge Computing

The emergence of edge computing makes it possible to realize new technologies, such as virtual reality and augmented reality. However, a large number of devices and more messages at the edge bring more security problems. Therefore, it is an important research topic to provide users with faster network services while ensuring confidentiality and authentication of data transmission. Because signcryption can encrypt and sign messages at the same time, it has become a new cryptographic primitive. In the meantime, certificateless signcryption guarantees data confidentiality and authentication and addresses traditional single point failure problems based on the trust center and the problem of relying on a trusted third party. Therefore, certificateless signcryption has attracted great attention from academia and industry. But certificateless signcryption also faces two types of attacks. In order to more effectively resist these two types of attacks, we propose a certificateless signcryption mechanism based on blockchain. This mechanism can make good use of the nontamperable feature of blockchain, prevent illegal users from substituting the public key of the user, and guarantee signature nonrepudiation. And our scheme is investigated in a comparative study with eight schemes. Comparative analysis outcomes demonstrate our scheme has achieved better results in efficiency and security. The process of signcryption and unsigncryption consumes the least amount of computation, which is very suitable for the edge computing environment.
A residual convolutional neural network based approach for real-time path planning

Path planning for unmanned aerial vehicles (UAVs) has been widely considered in various tasks. Existing path planning algorithms, such as A* and Jump Point Search, have been proposed and achieved good performance in the static mode, that is, assuming the global environmental information is known and planning is conducted offline. However, in practice, only limited environmental information can be obtained by sensors, which requires a real-time path planning ability. This paper proposes a residual convolutional neural network based approach, denoted as Res-Planner, to address the real-time path planning problem of a UAV. Specifically, the approach generates various scenarios and paths by executing the conventional path planning algorithms in static mode, from which it collects state-behaviour demonstrations to train the proper behaviour in real-time path planning. The experimental results show that our approach can provide feasible paths with approximately the global optimal under limited environmental information conditions.
Exploring the Benefits of Combining DevOps and Agile

The combined adoption of Agile and DevOps enables organizations to cope with the increasing complexity of managing customer requirements and requests. It fosters the emergence of a more collaborative and Agile framework to replace the waterfall models applied to software development flow and the separation of development teams from operations. This study aims to explore the benefits of the combined adoption of both models. A qualitative methodology is adopted by including twelve case studies from international software engineering companies. Thematic analysis is employed in identifying the benefits of the combined adoption of both paradigms. The findings reveal the existence of twelve benefits, highlighting the automation of processes, improved communication between teams, and reduction in time to market through process integration and shorter software delivery cycles. Although they address different goals and challenges, the Agile and DevOps paradigms when properly combined and aligned can offer relevant benefits to organizations. The novelty of this study lies in the systematization of the benefits of the combined adoption of Agile and DevOps considering multiple perspectives of the software engineering business environment.
GameDKT: Deep knowledge tracing in educational games

Despite the multiple deep knowledge tracing (DKT) methods developed for intelligent tutoring systems and online learning environments, there exists only a few applications of such methods in educational computer games. One key challenge is that a player may deploy several interweaved and overlapped skills during gameplay, making the assessment task nontrivial. In this research, we present a generalizable DKT approach called GameDKT that integrates state-of-the-art machine learning with domain knowledge to model the learners’ knowledge state during gameplay, in an attempt to monitor and trace their proficiency level for the different skills required for educational games. Our findings reveal that GameDKT approach could successfully predict the performance of players in the coming game task using the cross-validated CNN model with accuracy and AUC of roughly 85% and 0.913, respectively, thus outperforming the MLP baseline model by up to 14%. When the performance of players is forecasted for up to four game tasks in advance, results show that the CNN model can achieve more than 70% accuracy. Interestingly, this model seems to be better and faster at identifying local patterns and it could achieve a higher performance compared to RNN and LSTM in both one-step and multi-step prediction of learners’ performance in game tasks.
The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping.The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling.Highlights that online reviews play a significant role in boosting a business or slandering it.Product review is an essential factor in customers' decision-making, leading to an intense topic known as fraudulent or fake reviews detection.Given these reviews' power over a business, the treacherous acts of giving false reviews for personal gains have increased with time.In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning.We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments.Our proposed methodology for detecting fake online reviews outperforms on the yelp dataset and the TripAdvisor dataset compared to other state-of-the-art techniques with 95% and 89.03% accuracy.
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation

Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data. Recent work aims to overcome this limitation by leveraging the power of Contrastive Language-Image Pre-training (CLIP), a joint text-image model. While promising, these methods require several hours of preprocessing or training to achieve the desired manipulations. In this paper, we present StyleMC, a fast and efficient method for text-driven image generation and manipulation. StyleMC uses a CLIP-based loss and an identity loss to manipulate images via a single text prompt without significantly affecting other attributes. Unlike prior work, StyleMC requires only a few seconds of training per text prompt to find stable global directions, does not require prompt engineering and can be used with any pre-trained StyleGAN2 model. We demonstrate the effectiveness of our method and compare it to state-of-the-art methods. Our code can be found at http://catlab-team.github.io/stylemc.
A fuzzy adaptive zeroing neural network with superior finite-time convergence for solving time-variant linear matrix equations

Zeroing neural network (ZNN) has a wide application in various fields, which is a very important and novel type of recurrent neural network (RNN). To deepen and expand the design mechanism of the traditional zeroing neural network (TZNN), this paper proposes a new type of the ZNN model with a fuzzy adaptive parameter to settle the time-variant linear matrix equation (TVLME) problem. Due to its adaptability, this new model is thus called the fuzzy adaptive ZNN (FAZNN) model. In comparison to the TZNN model, the FAZNN model has a very obvious feature that the fuzzy adaptive parameter is produced by a fuzzy logic system based on the calculation error, which makes the FAZNN model have the characteristics of adaptability and accelerated convergence. Theoretical analysis and proofs are given for the convergence of the FAZNN model with four classical activation functions involved. More importantly, theoretical proofs show that when the modified Li activation function or Li activation function is used, the FAZNN model can make its state solution converge to the theoretical solution within a shorter finite-time. Moreover, in order to compare the influence of the types of membership functions, two different membership functions are involved in fuzzy logic systems. Theoretical proofs, simulative experiments and a mobile manipulator control application prove that the FAZNN model with the fuzzy adaptive parameter has a significant effect on settling the TVLME problem.
Few-Shot learning for face recognition in the presence of image discrepancies for limited multi-class datasets

One of the primary limitations of deep learning is data-hungry techniques. Deep learning approaches do not typically generalize well for limited datasets with fewer samples. Drawing the inspiration from the way human beings are capable of detecting a face from very few images seen in past (experience), Few-Shot Learning methods are reported in the literature. The problem is more challenging for face recognition tasks for limited dataset where the facial images are captured in various unfavorable conditions (i.e. discrepancies). To that end, in this work, we propose the Siamese Network-based Few-Shot Learning method for multi-class face recognition from a training dataset consisting of only a handful of images per class. We consider three such face image discrepancies namely, low light, head rotation and occlusion. Our work offers novelty primarily in the way the image discrepancies are overcome via Few-Shot learning while recognizing the face with reasonable accuracy. The results are obtained on our manually collected primary dataset (SCAAI_FSL) for multiple classes. Our approach presents a unique solution for face recognition tasks where the images in the training and testing dataset have different discrepancies which is the typical real-world scenario. We have experimented with various face embeddings models and demonstrated our approach for simultaneously handling multiple image discrepancies for SCAAI_FSL dataset and reported the testing accuracy of 72.72%.
Participant Selection for Federated Learning With Heterogeneous Data in Intelligent Transport System

Intelligent Transportation Systems (ITS) utilises the growing trend of both communication technologies and intelligent analytics to make transportation systems more smart and efficient. Federated Learning, a privacy-preserving machine learning paradigm shows promise in being applied in this field. However, the data and device heterogeneity, and highly dynamic environment in ITS pose challenges to the performance of federated learning. One of the recent approaches to address the challenges are to choose proper participants from available clients during training. However, this research field is not fully investigated yet, and many works are still based on the classic random-based selection scheme. In this paper, we present Newt, an enhanced federated learning approach. On one hand, it includes a new client selection utility that explores the trade-off between accuracy performance in each round and system progress. On the other hand, it highlights a feedback control on the selector. Specifically, we implement a control on the selection frequency as a new dimension of client selection method design. We evaluate the proposed system with DNN training tasks on large scale FEMNIST-based datasets that are of different heterogeneity properties. The experiments show that our method outperforms the other baseline methods by as large as 20%.
American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM

Complex hand gesture interactions among dynamic sign words may lead to misclassification, which affects the recognition accuracy of the ubiquitous sign language recognition system. This paper proposes to augment the feature vector of dynamic sign words with knowledge of hand dynamics as a proxy and classify dynamic sign words using motion patterns based on the extracted feature vector. In this method, some double-hand dynamic sign words have ambiguous or similar features across a hand motion trajectory, which leads to classification errors. Thus, the similar/ambiguous hand motion trajectory is determined based on the approximation of a probability density function over a time frame. Then, the extracted features are enhanced by transformation using maximal information correlation. These enhanced features of 3D skeletal videos captured by a leap motion controller are fed as a state transition pattern to a classifier for sign word classification. To evaluate the performance of the proposed method, an experiment is performed with 10 participants on 40 double hands dynamic ASL words, which reveals 97.98% accuracy. The method is further developed on challenging ASL, SHREC, and LMDHG data sets and outperforms conventional methods by 1.47%, 1.56%, and 0.37%, respectively.
A novel image super-resolution algorithm based on multi-scale dense recursive fusion network

With the increasing maturity of convolution neural network (CNN) technology, the image super-resolution reconstruction (SR) method based on CNN is booming and has achieved many remarkable results. Undoubtedly, SR has become the mainstream direction of image reconstruction technology. However, most of the existing SR methods improve the reconstruction performance by increasing the depth of networks, which also increases the number of parameters, number of network computations, and difficulty of training network. To solve the performance complexity dilemma in SR, this paper proposes a network called a multi-scale dense recursive fusion network (MSDRFN). The network is composed of three parts: initial feature extraction module, multi-scale dense fusion group module and recursive reconstruction module. In detail, rough features are first extracted through a shallow feature extraction module, and then are inputted into multi-scale dense fusion blocks (MSDFBs) group. Each MSDFB makes full use of image features in convolution kernels of different sizes to obtain different hierarchical features, and further these output features are inputted into the channel attention mechanism to learn their corresponding weights. All MSDFBs outputs will be restored to high resolution images via the recursive reconstruction module. In addition, the network supplements the information loss with residual learning, which is embodied in one long-jump connection and several short-jump connections. The proposed network is mainly trained in the Pytorch deep learning framework. In comparison experiments on benchmark datasets, the proposed method outperformed the most advanced convolutional methods.
Optimal Volumetric Video Streaming With Hybrid Saliency Based Tiling

Volumetric video enables a six-degree-of-freedom (6DoF) immersive viewing experience and has a wide range of applications in entertainment and education, among others. Most existing approaches to volumetric video streaming are extensions of VR video streaming solutions that do not take into account user behavior and the properties of the video during the tiling process, and the complexity of decoding is high. To this end, we study volumetric video streaming in this paper and address the research questions mentioned above. In particular, we first propose a hybrid visual saliency and hierarchical clustering empowered 3D tiling scheme that better matches the user’s field of view (FoV). Then, we build a quality of experience (QoE) model considering the volumetric video features as the optimization objective. In addition to the usual encoded version, we introduce the reconstructed version (i.e., decoded version, which allows the user to skip the decoding process and thus reduces the decoding overhead) and propose a joint computational and communication resource allocation scheme to achieve a trade-off between communication and computational resources to maximize the QoE. We perform exhaustive simulations and build a prototype system to verify the performance of the proposed tiling and transmission scheme. The results show that the proposed tiling and transmission scheme performs significantly better than the comparison schemes.
An Effective Integrity Monitoring Scheme for GNSS/INS/Vision Integration Based on Error State EKF Model

It is well-known that the Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) /Vision integration has been increasingly used for safety-critical applications like self-driving cars. However, the performance of the GNSS/INS/Vision integration degrades due to unknown faults. Therefore, it is essential to develop integrity monitoring algorithms for the integrated navigation system. A new protection level (PL) formula to ensure the integrity of the state estimation of the GNSS/INS/Vision integration is proposed, which can be calculated by the relation between the filter estimation error and faults. An integrity risk allocation tree is developed for each sensor fault hypothesis including the nominal hypothesis, and the specific PL is given in the case of GNSS, INS and visual measurement faults. In addition, the performance of the proposed PL is comprehensively evaluated. A field vehicular test was conducted to evaluate the performance of the integrity monitoring algorithm for the GNSS/INS/Vision integration. The results demonstrate that the proposed PL for the GNSS/INS/Vision integration can fit the position error against six different fault modes.