Reviewerly/exHarmony huggingface dataset:
Blockchain and AI-based Secure Onion Routing Framework for Data Dissemination in IoT Environment Underlying 6G Networks

Internet of Things (IoT) has massively adopted the market due to the current era demanding fully intelligent and autonomous services. The IoT industry is flourishing in terms of Internet of healthcare things, Internet of vehicles and autonomous driving, unmanned aerial vehicles, satellite and industrial Internet of things smart homes, etc. IoT can be enabled by employing connected devices such as sensors, continuously communicating, storing, and disseminating data. Data dissemination through energy-constrained low-power IoT sensors suffers from issues such as security and privacy of data. Traditional solutions using single-layer or multi-layer cryptographic algorithms do not suffice the need for security and privacy. Thus, we have proposed a blockchain-based secure onion routing protocol for a trusted and anonymous data dissemination framework for sensor communication in IoT environment. We have employed machine learning (ML) algorithms to classify adversarial and non-adversarial data fed to the onion router to reduce computational time. Our proposed framework uses a Random Forest (RF) classifier outperforming with 93.65% accuracy compared to other ML algorithms. Also, we have achieved low data storage cost, low latency, and high scalability by employing Inter Planetary File System (IPFS) and a 6G network, respectively.
Open World Entity Segmentation

We introduce a new image segmentation task, called Entity Segmentation (ES), which aims to segment all visual entities (objects and stuffs) in an image without predicting their semantic labels. By removing the need of class label prediction, the models trained for such task can focus more on improving segmentation quality. It has many practical applications such as image manipulation and editing where the quality of segmentation masks is crucial but class labels are less important. We conduct the first-ever study to investigate the feasibility of convolutional center-based representation to segment things and stuffs in a unified manner, and show that such representation fits exceptionally well in the context of ES. More specifically, we propose a CondInst-like fully-convolutional architecture with two novel modules specifically designed to exploit the class-agnostic and non-overlapping requirements of ES. Experiments show that the models designed and trained for ES significantly outperforms popular class-specific panoptic segmentation models in terms of segmentation quality. Moreover, an ES model can be easily trained on a combination of multiple datasets without the need to resolve label conflicts in dataset merging, and the model trained for ES on one or more datasets can generalize very well to other test datasets of unseen domains. The code has been released at https://github.com/dvlab-research/Entity.
Improving the Intra-class Long-Tail in 3D Detection via Rare Example Mining

Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples. However, even the best performing models suffer from the most naive mistakes when it comes to rare examples that do not appear frequently in the training data, such as vehicles with irregular geometries. Most studies in the long-tail literature focus on class-imbalanced classification problems with known imbalanced label counts per class, but they are not directly applicable to the intra-class long-tail examples in problems with large intra-class variations such as 3D object detection, where instances with the same class label can have drastically varied properties such as shapes and sizes. Other works propose to mitigate this problem using active learning based on the criteria of uncertainty, difficulty, or diversity. In this study, we identify a new conceptual dimension - rareness - to mine new data for improving the long-tail performance of models. We show that rareness, as opposed to difficulty, is the key to data-centric improvements for 3D detectors, since rareness is the result of a lack in data support while difficulty is related to the fundamental ambiguity in the problem. We propose a general and effective method to identify the rareness of objects based on density estimation in the feature space using flow models, and propose a principled cost-aware formulation for mining rare object tracks, which improves overall model performance, but more importantly - significantly improves the performance for rare objects (by 30.97%).
Learning to Align Sequential Actions in the Wild

State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame mapping across sequences, which does not leverage temporal information, or assume monotonic alignment between each video pair, which ignores variations in the order of actions. As such, these methods are not able to deal with common real-world scenarios that involve background frames or videos that contain non-monotonic sequence of actions. In this paper, we propose an approach to align sequential actions in the wild that involve diverse temporal variations. To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions. Our model accounts for both monotonic and non-monotonic sequences and handles background frames that should not be aligned. We demonstrate that our approach consistently outperforms the state-of-the-art in self-supervised sequential action representation learning on four different benchmark datasets. Code is publicly available at https://github.com/weizheliu/VAVA.
PanoSynthVR: Toward Light-weight 360-Degree View Synthesis from a Single Panoramic Input

We investigate how real-time, 360° view synthesis can be achieved on current virtual reality hardware from a single panoramic image input. We introduce a light-weight method to automatically convert a single panoramic input into a multi-cylinder image representation that supports real-time, free-viewpoint view synthesis rendering for virtual reality. We apply an existing convolutional neural network trained on pinhole images to a cylindrical panorama with wrap padding to ensure agreement between the left and right edges. The network outputs a stack of semi-transparent panoramas at varying depths which can be easily rendered and composited with over blending. Quantitative experiments and a user study show that the method produces convincing parallax and fewer artifacts than a textured mesh representation.
Service quality evaluation and service improvement using online reviews: A framework combining deep learning with a hierarchical service quality model

In the era of big data, service quality evaluation using online reviews has become a popular topic. However, very few studies focus simultaneously on service quality evaluation and service improvement. In this study, a research framework for service quality evaluation and service improvement is proposed, sentiment analysis is used to extract the temporal scores of the service attributes of each subdimension of the service quality model from online reviews, and a long short-term memory network is used to predict the scores for the service quality provider. Furthermore, a long short-term memory network-based sensitivity analysis, in conjunction with improvement costs, is used to rank the subdimensions in the service quality model. Then, service improvement strategies are determined according to the rankings of the service attributes. Hotels' online reviews were used to investigate the effectiveness of the proposed framework. A series of service improvement strategies for the specific service attributes are provided.
Data poisoning attacks against machine learning algorithms

For the past decade, machine learning technology has increasingly become popular and it has been contributing to many areas that have the potential to influence the society considerably. Generally, machine learning is used by various industries to enhance their performances. Moreover, machine learning algorithms are used to solve some hard problems of systems that may contain very critical information. This makes machine learning algorithms a target of adversaries, which is an important problem for systems that use such algorithms. Therefore, it is significant to determine the performance and the robustness of a machine learning algorithm against attacks. In this paper, we analyze empirically the robustness and performances of six machine learning algorithms against two types of adversarial attacks by using four different datasets and three metrics. In our experiments, we analyze the robustness of Support Vector Machine, Stochastic Gradient Descent, Logistic Regression, Random Forest, Gaussian Naive Bayes, and K-Nearest Neighbor algorithms to create learning models. We observe their performances in spam, botnet, malware, and cancer detection datasets when we launch adversarial attacks against these environments. We use data poisoning for manipulating training data during adversarial attacks, which are random label flipping and distance-based label flipping attacks. We analyze the performance of each algorithm for a specific dataset by modifying the amount of poisoned data and analyzing behaviors of accuracy rate, f1-score, and AUC score. Analyses results show that machine learning algorithms have various performance results and robustness under different adversarial attacks. Moreover, machine learning algorithms are affected differently in each stage of an adversarial attacks. Furthermore, the behavior of a machine learning algorithm highly depends on the type of the dataset. On the other hand, some machine learning algorithms have better robustness and performance results against adversarial attacks for almost all datasets.
Smart insoles review (2008-2021): Applications, potentials, and future

During the past decade, smart insole-like devices become more and more popular in daily applications, such as gait studies, activity recognition, health monitoring and evaluation, human–machine interactions and footstep energy harvesting. The utilization of smart insole-like devices in daily life has gained ever-increasing attractions from multidisciplinary communities that various designs have been proposed. This paper aims to provide a thorough review of smart insole-like technologies from historical evolutions to state-of-the-art developments. Analysis and comparisons are provided between 81 prototypes from research studies and 15 popular commercial products from year 2008 to 2021, focusing on reviewing sensor types, sensor positions, applications, experiment validation approaches and low energy expenditure strategies. The potentials and strengths towards daily life uses are summarized and evaluated. In the end, we summarize the current research progress, highlight the challenges to be addressed and envision the future path planning. To the best of authors’ knowledge, this is the first paper reviewing smart insole wearable devices from multidisciplinary aspects and their potentials towards daily life uses, aiming to provide a comprehensive summary of the state of knowledgeable in the field.
Secure Task Offloading in Blockchain-Enabled Mobile Edge Computing With Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.
An asymmetric image encryption scheme based on hash SHA-3, RSA and compressive sensing

In order to avoid the unauthorized access to private images and make a secure communication, an asymmetric image encryption algorithm based on SHA-3 and compressive sensing is proposed. Firstly, a random matrix is generated and a modular-addition operation is performed between it with the plain image, obtaining a preprocessed image. Then, hash values of the preprocessed image are computed by secure hash algorithm in the third generation (SHA-3), and are grouped and summed to get three plaintext keys. Using Rivest-Shamir-Adleman (RSA), three ciphertext keys can be obtained correspondingly. Secondly, a new mathematical transformation model (MTM) is designed to transform all keys into initial values for chaotic system. Then, keystream is calculated accordingly. Thirdly, the plain image is compressed by compressive sensing (CS), and further confused by random sequences. Then, applying discrete wavelet transformation (DWT) to the confused image and generating four components of high and low frequencies. Chaotic sequences are used again to confuse the low frequency components, and then all components are recombined again into a matrix. Thereafter, perform inverse DWT (IDWT) to get a middle cipher image (MCI). Finally, another random matrix is generated by chaotic sequences, and the final cipher image is obtained by a modular-addition operation again to the MCI. Experiments show that the proposed scheme can reconstruct the plain image with a high quality. In particular, the proposed method can resist known plaintext attack and chosen plaintext attack because the keystreams are related to the plain image and with no extra transmission.
Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation

Image segmentation is a basic technology in the field of image processing and computer vision. Medical image segmentation is an important application field of image segmentation and plays an increasingly important role in clinical diagnosis and treatment. Deep learning has made great progress in medical image segmentation. In this paper, we proposed Residual-Attention UNet++, which is an extension of the UNet++ model with a residual unit and attention mechanism. Firstly, the residual unit improves the degradation problem. Secondly, the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task. Three medical image datasets such as skin cancer, cell nuclei, and coronary artery in angiography were used to validate the proposed model. The results showed that the Residual-Attention UNet++ achieved superior evaluation scores with an Intersection over Union (IoU) of 82.32%, and a dice coefficient of 88.59% with the skin cancer dataset, a dice coefficient of 85.91%, and an IoU of 87.74% with the cell nuclei dataset and a dice coefficient of 72.48%, and an IoU of 66.57% with the angiography dataset.
Demystifying Federated Learning for Blockchain

Blockchain genuinely has the power to revolutionise the energy market mechanism by introducing smart PPAs (purchase power agreements), smart micro grids, and REC certificates. By converting energy resources into digital commodities that can be exchanged on a blockchain, new investment and trading possibilities that allow new players to join easily and encourage innovations will be enabled. It may also contribute to a shift driven by the organisation to address the issue of last mile connectivity. While recent grid developments have broadened the use of advanced control techniques, the next-generation grid requires technology that makes it simpler to connect distributed energy services (DERs) for users who both acquire and sell power seamlessly. This chapter will present a case study of a blockchain-based optimization paradigm and framework for crowd-sourced operations.
SGM3D: Stereo Guided Monocular 3D Object Detection

Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image. It poses a great challenge due to its ill-posed property which is critically lack of depth information in the 2D image plane. While there exist approaches leveraging off-the-shelve depth estimation or relying on LiDAR sensors to mitigate this problem, the dependence on the additional depth model or expensive equipment severely limits their scalability to generic 3D perception. In this paper, we propose a stereo-guided monocular 3D object detection framework, dubbed SGM3D, adapting the robust 3D features learned from stereo inputs to enhance the feature for monocular detection. We innovatively present a multi-granularity domain adaptation (MG-DA) mechanism to exploit the network's ability to generate stereo-mimicking features given only on monocular cues. Coarse BEV feature-level, as well as the fine anchor-level domain adaptation, are both leveraged for guidance in the monocular domain.In addition, we introduce an IoU matching-based alignment (IoU-MA) method for object-level domain adaptation between the stereo and monocular predictions to alleviate the mismatches while adopting the MG-DA. Extensive experiments demonstrate state-of-the-art results on KITTI and Lyft datasets.
The intersection of evolutionary computation and explainable AI

In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in the research community, motivated by the need for explanations in critical AI applications. Some recent advances in XAI are based on Evolutionary Computation (EC) techniques, such as Genetic Programming. We call this trend EC for XAI. We argue that the full potential of EC methods has not been fully exploited yet in XAI, and call the community for future efforts in this field. Likewise, we find that there is a growing concern in EC regarding the explanation of population-based methods, i.e., their search process and outcomes. While some attempts have been done in this direction (although, in most cases, those are not explicitly put in the context of XAI), we believe that there are still several research opportunities and open research questions that, in principle, may promote a safer and broader adoption of EC in real-world applications. We call this trend XAI within EC. In this position paper, we briefly overview the main results in the two above trends, and suggest that the EC community may play a major role in the achievement of XAI.
Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data

Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i.e., retrogress) after each communication and the intractable class imbalance. Thus, in this paper, we propose a novel personalized FL framework to tackle these two problems. For the retrogress problem, we first devise a Progressive Fourier Aggregation (PFA) at the server side to gradually integrate parameters of client models in the frequency domain. Then, at the client side, we design a Deputy-Enhanced Transfer (DET) to smoothly transfer global knowledge to the personalized local model. For the class imbalance problem, we propose the Conjoint Prototype-Aligned (CPA) loss to facilitate the balanced optimization of the FL framework. Considering the inaccessibility of private local data to other participants in FL, the CPA loss calculates the global conjoint objective based on global imbalance, and then adjusts the client-side local training through the prototype-aligned refinement to eliminate the imbalance gap with such a balanced goal. Extensive experiments are performed on real-world dermoscopic and prostate MRI FL datasets. The experimental results demonstrate the advantages of our FL framework in real-world medical scenarios, by outperforming state-of-the-art FL methods with a large margin. The source code is available at https://github.com/CityU-AIM-Group/PRR-Imbalancehttps://github.com/CityU-AIM-Group/PRR-Imbalance.
Artificial Intelligence Crime: An Overview of Malicious Use and Abuse of AI

The capabilities of Artificial Intelligence (AI) evolve rapidly and affect almost all sectors of society. AI has been increasingly integrated into criminal and harmful activities, expanding existing vulnerabilities, and introducing new threats. This article reviews the relevant literature, reports, and representative incidents which allows to construct a typology of the malicious use and abuse of systems with AI capabilities. The main objective is to clarify the types of activities and corresponding risks. Our starting point is to identify the vulnerabilities of AI models and outline how malicious actors can abuse them. Subsequently, we explore AI-enabled and AI-enhanced attacks. While we present a comprehensive overview, we do not aim for a conclusive and exhaustive classification. Rather, we provide an overview of the risks of enhanced AI application, that contributes to the growing body of knowledge on the issue. Specifically, we suggest four types of malicious abuse of AI (integrity attacks, unintended AI outcomes, algorithmic trading, membership inference attacks) and four types of malicious use of AI (social engineering, misinformation/fake news, hacking, autonomous weapon systems). Mapping these threats enables advanced reflection of governance strategies, policies, and activities that can be developed or improved to minimize risks and avoid harmful consequences. Enhanced collaboration among governments, industries, and civil society actors is vital to increase preparedness and resilience against malicious use and abuse of AI.