Reviewerly/exHarmony huggingface dataset:
Anti-Forensics for Face Swapping Videos via Adversarial Training

Generating falsified faces by artificial intelligence, widely known as DeepFake, has attracted attention worldwide since 2017. Given the potential threat brought by this novel technique, forensics researchers dedicated themselves to detect the video forgery. Except for exposing falsified faces, there could be extended research directions for DeepFake such as anti-forensics. It can disclose the vulnerability of current DeepFake forensics methods. Besides, it could also enable DeepFake videos as tactical weapons if the falsified faces are more subtle to be detected. In this paper, we propose a GAN model to behave as an anti-forensics tool. It features a novel architecture with additional supervising modules for enhancing image visual quality. Besides, a loss function is designed to improve the efficiency of the proposed model. After experimental evaluations, we show that the DeepFake forensics detectors are susceptible to attacks launched by the proposed method. Besides, the proposed method can efficiently produce anti-forensics videos in satisfying visual quality without noticeable artifacts. Compared with the other anti-forensics approaches, this is tremendous progress achieved for DeepFake anti-forensics. The attack launched by our proposed method can be truly regarded as DeepFake anti-forensics as it can fool detecting algorithms and human eyes simultaneously.
What Drives the Adoption of the Blockchain Technology? A Fit-Viability Perspective

Blockchain technology has the promise of transforming security and trust in digital transactions. However, concerns about technical complexity and the benefits of deployment have blunted its adoption. We examine factors that influence managerial intention to adopt blockchain technology. We extend the fit-viability model (FVM) and develop a value-based technology adoption model through an empirical study of 242 managers mostly in medical and financial industries. Managers in such organizations are likely to consider fit and viability in adopting blockchain technology to store and protect data. Drawing upon Fit-Viability and Task-Technology Fit models, and the Unified Theory of Acceptance and Use of Technology (UTAUT), we test a model with Partial Least Squares (PLS) to assess managers’ intention to adopt blockchain technology. Our findings indicate that functional and symbolic benefits have positive impact on managers’ assessment of task-technology fit. Furthermore, viability is an important criterion in adopting blockchain technology.
CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search

In recent years, there is an increasing interest in designing autonomous vehicles such as Mobile Robots. However, one of the fundamental needs of Mobile Robots is a collision-free navigation with an optimal path from the source to the destination. In this paper, a collision-free low-complexity Mobile Robot navigation scheme called Collision Aware Mobile Robot navigation in Grid-Environment is designed. The proposed scheme uses the Radio Frequency based Identification method for Mobile Robot localization, the hybrid approach for the path planning, and a predefined decision table for the navigation. The algorithms are implemented in two stages, construction of virtual world and generation of optimal shortest path. The algorithms’ performance is analyzed in grid-based environment of size 5×5,20×20,35×35, and 50 × 50 with four different cases. It is observed that for the environment with no obstacles, the robot explores fewer cells in finding the shortest path. However, the number of turning in the shortest path is always less in the environment with some obstacles than in the case of whole virtual world exploration. The performance results show the effectiveness of the proposed scheme for Mobile Robot navigation in the grid environment.
MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization-based deep convolutional autoencoder (MR-DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy-stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back-propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross-layer manifold invariance. Finally, MR-DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state-of-the-art performance. To help understand the principle behind MR-DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.
Customer-robot interactions: Understanding customer experience with service robots

Technology developments relating to automation, artificial intelligence, and robots have transformed the landscape of service industries, including hospitality and tourism. Through a qualitative content analysis of online review data, this study seeks a comprehensive and grounded understanding of customer experience with service robots in hospitality and tourism settings. The analysis identified four categories of customer experience: (1) sensory experience (verbal language, physical appearance, kinesics, and paralanguage), (2) cognitive experience (utility, cuteness, autonomy, coolness, interactivity, and courtesy), (3) affective experience (enjoyment, novelty, negative emotion, and satisfaction), and (4) conative experience (approach/resistance). Results led to the development of a framework representing customer experience with service robots and to insights into customer-robot interactions. Most customers described positive experiences, and while service robots performed well in delivering functional and emotional value, social interaction skills need improvement.
A Survey of Decentralizing Applications via Blockchain: The 5G and Beyond Perspective

Trusted third parties (TTPs) are frequently used for serving as an authority to issue and verify transactions in applications. Although the TTP-based paradigm provides customers with convenience, it causes a whole set of inevitable problems such as security threats, privacy vulnerabilities, and censorship. The TTP-based paradigm is not suitable for all modern networks, e.g., 5G and beyond networks, which are been evolving to support ubiquitous, decentralized, and autonomous services. Driven by the vision of blockchain technologies, there has been a paradigm shift in applications, from TTP-based to decentralized-trust-based. Decentralized applications (DApps) with blockchains promise no trust on authorities, tackling the key challenges of security and privacy problems. A main thrust of blockchain research is to explore frameworks and paradigms for decentralizing applications, fostering a number of new designs ranging from network architectures to business models. Therefore, this paper provides a compact and concise survey on the state-of-the-art research of decentralizing applications with blockchain in the 5G and beyond perspective. We provide four burning 5G and beyond challenges and discuss five aspects of motivation for decentralizing applications with blockchain. Then, we define nine fundamental modules of blockchains and explain the potential influence of these modules on decentralization in depth. We also discuss the interrelation between decentralization and some desired blockchain properties. Particularly, we present the capabilities of blockchain for decentralizing applications through reviewing DApps for 5G and beyond. We clearly distinguish three blockchain paradigms and discuss how developers to make right choices for 5G and beyond. Finally, we highlight important learned lessons and open issues in applying blockchain for decentralizing applications. Lessons learned and open issues from this survey will facilitate the transformation of centralized applications to DApps.
Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices

Efficient and light-weight super resolution (SR) is highly demanded in practical applications. However, most of the existing studies focusing on reducing the number of model parameters and FLOPs may not necessarily lead to faster running speed on mobile devices. In this work, we propose a re-parameterizable building block, namely Edge-oriented Convolution Block (ECB), for efficient SR design. In the training stage, the ECB extracts features in multiple paths, including a normal 3 x 3 convolution, a channel expanding-and-squeezing convolution, and 1st-order and 2nd-order spatial derivatives from intermediate features. In the inference stage, the multiple operations can be merged into one single 3 3 convolution. ECB can be regarded as a drop-in replacement to improve the performance of normal 3 3 convolution without introducing any additional cost in the inference stage. We then propose an extremely efficient SR network for mobile devices based on ECB, namely ECBSR. Extensive experiments across five benchmark datasets demonstrate the effectiveness and efficiency of ECB and ECBSR. Our ECBSR achieves comparable PSNR/SSIM performance to state-of-the-art light-weight SR models, while it can super resolve images from 270p/540p to 1080p in real-time on commodity mobile devices, e.g., Snapdragon 865 SOC and Dimensity 1000+ SOC. The source code can be found at https://github.com/xindongzhang/ECBSR.
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation

Recent advances in transformer-based models have drawn attention to exploring these techniques in medical image segmentation, especially in conjunction with the UNet model (or its variants), which has shown great success in medical image segmentation, under both 2D and 3D settings. Current 2D based methods either directly replace convolutional layers with pure transformers or consider a transformer as an additional intermediate encoder between the encoder and decoder of U-Net. However, these approaches only consider the attention encoding within one single slice and do not utilize the axial-axis information naturally provided by a 3D volume. In the 3D setting, convolution on volumetric data and transformers both consume large GPU memory. One has to either downsample the image or use cropped local patches to reduce GPU memory usage, which limits its performance. In this paper, we propose Axial Fusion Transformer UNet (AFTer-UNet), which takes both advantages of convolutional layers’ capability of extracting detailed features and transformers’ strength on long sequence modeling. It considers both intra-slice and inter-slice long-range cues to guide the segmentation. Meanwhile, it has fewer parameters and takes less GPU memory to train than the previous transformer-based models. Extensive experiments on three multi-organ segmentation datasets demonstrate that our method outperforms current state-of-the-art methods.
Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification

Deep learning models have shown their great capability for the hyperspectral image (HSI) classification task in recent years. Nevertheless, their vulnerability towards adversarial attacks could not be neglected. In this study, we systematically analyze the influence of adversarial attacks on the HSI classification task for the first time. While existing research of adversarial attacks focuses on the generation of adversarial examples in the RGB domain, the experiments in this study show such adversarial examples could also exist in the hyperspectral domain. Although the difference between the generated adversarial image and the original hyperspectral data is imperceptible to the human visual system, most of the existing state-of-the-art deep learning models could be fooled by the adversarial image to make wrong predictions. To address this challenge, a novel self-attention context network (SACNet) is further proposed. We discover that the global context information contained in HSI can significantly improve the robustness of deep neural networks when confronted with adversarial attacks. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed SACNet possesses stronger resistibility towards adversarial examples compared with the existing state-of-the-art deep learning models.
Explanations in Autonomous Driving: A Survey

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environment and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs’ behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have ‘seen’, done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations by highlighting the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their AV explanation requirements. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, vehicle control, and system management). Finally, we discuss pertinent challenges and provide recommendations including a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving.
Blockchain-Empowered Space-Air-Ground Integrated Networks: Opportunities, Challenges, and Solutions

The terrestrial networks face the challenges of severe cost inefficiency and low feasibility to provide seamless services anytime and anywhere, especially in the extreme or hotspot areas (e.g., disaster areas, mountains, and oceans) due to limited service coverage and capacity. The integration of multi-dimensional networks consisting of space, air, and ground layers is expected to provide solutions in delivering cost-effective and ubiquitous Internet of things (IoT) services for billions of users and interconnected smart devices. Autonomous data collection, exchange, and processing across different network segments with minimal human interventions in space-air-ground IoT (SAG-IoT) can bring great convenience to consumers, however, it also suffers new attacks from intruders. Severe privacy invasion, reliability issues, and security breaches of SAG-IoT can hinder its wide deployment. The emerging blockchain holds great potentials to address the security concerns in SAG-IoT, thanks to its prominent features of decentralization, transparency, immutability, traceability, and auditability. Despite of the benefits of blockchain-empowered SAG-IoT, there exist a series of fundamental challenges in terms of efficiency and regulation due to the intrinsic characteristics of SAG-IoT (e.g., heterogeneity, time-variability, and poor interoperability) and the limitations of existing blockchain approaches (e.g., capacity and scalability). This article presents a comprehensive survey of the integration of blockchain technologies for securing SAG-IoT applications. Specifically, we first discuss the architecture, characteristics, and security threats of SAG-IoT systems. Then, we concentrate on the promising blockchain-based solutions for SAG-IoT security. Next, we discuss the critical challenges when integrating blockchain in SAG-IoT security services and review the state-of-the-art solutions. We further investigate the opportunities of blockchain in artificial intelligence and beyond 5G networks and provide open research directions for building future blockchain-empowered SAG-IoT systems.
Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases

The performance of a model in machine learning problems highly depends on the dataset and training algorithms. Choosing the right training algorithm can change the tale of a model. While some algorithms have a great performance in some datasets, they may fall into trouble in other datasets. Moreover, by adjusting hyperparameters of an algorithm, which controls the training processes, the performance can be improved. This study contributes a method to tune hyperparameters of machine learning algorithms using Grey Wolf Optimization (GWO) and Genetic algorithm (GA) metaheuristics. Also, 11 different algorithms including Averaged Perceptron, FastTree, FastForest, Light Gradient Boost Machine (LGBM), Limited memory Broyden Fletcher Goldfarb Shanno algorithm Maximum Entropy (LbfgsMxEnt), Linear Support Vector Machine (LinearSVM), and a Deep Neural Network (DNN) including four architectures are employed on 11 datasets in different biological, biomedical, and nature categories such as molecular interactions, cancer, clinical diagnosis, behavior related predictions, RGB images of human skin, and X-rays images of Covid19 and cardiomegaly patients. Our results show that in all trials, the performance of the training phases is improved. Also, GWO demonstrates a better performance with a p-value of 2.6E-5. Moreover, in most experiment cases of this study, the metaheuristic methods demonstrate better performance and faster convergence than Exhaustive Grid Search (EGS). The proposed method just receives a dataset as an input and suggests the best-explored algorithm with related arguments. So, it is appropriate for datasets with unknown distribution, machine learning algorithms with complex behavior, or users who are not experts in analytical statistics and data science algorithms.
Causal Inference

Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms is one of key steps toward to the artificial intelligence 2.0. With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence, we invited researchers working on causal inference to write this survey from different aspects of causal inference. This survey includes the following sections: “Estimating average treatment effect: A brief review and beyond” from Dr. Kun Kuang, “Attribution problems in counterfactual inference” from Prof. Lian Li, “The Yule–Simpson paradox and the surrogate paradox” from Prof. Zhi Geng, “Causal potential theory” from Prof. Lei Xu, “Discovering causal information from observational data” from Prof. Kun Zhang, “Formal argumentation in causal reasoning and explanation” from Profs. Beishui Liao and Huaxin Huang, “Causal inference with complex experiments” from Prof. Peng Ding, “Instrumental variables and negative controls for observational studies” from Prof. Wang Miao, and “Causal inference with interference” from Dr. Zhichao Jiang.
Drones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy

Edge Intelligence is an emerging technology which has attracted significant attention. It applies Artificial Intelligence (AI) closer to the network edge for supporting Beyond fifth Generation (B5G) needs. On the other hand, drones can be used as relay station (mobile drone edge intelligence) to gather data from smart environments. Federated Learning (FL) enables the drones to perform decentralized collaborative learning by developing local models, sharing the model parameters with neighbors and the centralized unit to improve global model accuracy in smart environments. However, drone edge intelligence faces challenges such as security and decentralization management, limiting its functions to support green smart environments. Blockchain is a promising technology that enables privacy-preserving data sharing in a distributed manner. There are several challenges that still need to be addressed in blockchain-based applications, such as scalability, energy efficiency, and transaction capacity. Motivated by the significance of FL and blockchain, this survey focuses on the synergy of FL and blockchain to enable drone edge intelligence for green sustainable environments. Moreover, we discuss the combination of FL and blockchain technological aspects, motivation, and framework for green smart environments. Finally, we discuss the challenges and opportunities, and future trends in this domain.