Reviewerly/exHarmony huggingface dataset:
SAGE: Steering the Adversarial Generation of Examples With Accelerations

To generate image adversarial examples, state-of-the-art black-box attacks usually require thousands of queries. However, massive queries will introduce additional costs and exposure risks in the real world. Towards improving the attack efficiency, we carefully design an acceleration framework SAGE for existing black-box methods, which is composed of sLocator (initial point optimization) and sRudder (search process optimization). The core idea of SAGE in terms of 1) saliency map can guide the perturbations towards the most adversarial direction and 2) exploiting bounding box (bbox) to capture those salient pixels in the black-box attack. Meanwhile, we provide a series of observations and experiments that demonstrate bbox holds model invariance and process invariance. We extensively evaluate SAGE on four state-of-the-art black-box attacks involving three popular datasets (MNIST, CIFAR10, and ImageNet). The results show that SAGE could present fundamental improvements even against robust models that use adversarial training. Specifically, SAGE could reduce >20% of queries and improve the success rate of attacks to 95%~100%. Compared with the other acceleration framework, SAGE fulfills the more significant effect in a flexible, stable, and low-overhead manner. Moreover, our practical evaluation (Google Cloud Vision API) shows SAGE can be applied to real-world scenarios.
Grid-Based Bayesian Filters With Functional Decomposition of Transient Density

The paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention to grid-based Bayesian filters such as the point-mass filter (PMF) and the marginal particle filter (mPF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition approximates the transient density in a closed region. It is based on a non-negative matrix/tensor factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution involving the density, which is a performance bottleneck of the standard PMF/mPF implementations. The estimate quality and computational costs can be efficiently controlled by choosing an appropriate decomposition rank. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation scenario and a problem involving a univariate non-stationary growth model.
Fine-Tuning Swin Transformer and Multiple Weights Optimality-Seeking for Facial Expression Recognition

Facial expression recognition plays a key role in human-computer emotional interaction. However, human faces in real environments are affected by various unfavorable factors, which will result in the reduction of expression recognition accuracy. In this paper, we proposed a novel method which combines Fine-tuning Swin Transformer and Multiple Weights Optimality-seeking (FST-MWOS) to enhanced expression recognition performance. FST-MWOS mainly consists of two crucial components: Fine-tuning Swin Transformer (FST) and Multiple Weights Optimality-seeking (MWOS). FST takes Swin Transformer Large as the backbone network to obtain multiple groups of fine-tuned model weights for the homologous data domains by hyperparameters configurations, data augmentation methods, etc. In MWOS a greedy strategy was used to mine locally optimal generalizations in the optimal epoch interval of each group of fine-tuned model weights. Then, the optimality-seeking for multiple groups of locally optimal weights was utilized to obtain the global optimal solution. Experiments results on RAF-DB, FERPlus and AffectNet datasets show that the proposed FST-MWOS method outperforms various state-of-the-art methods.
Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information

Breast cancer is the world’s second-largest cause of cancer mortality among women. With the progress of artificial intelligence (AI) in healthcare, the survival rate of breast cancer patients has risen in recent years due to early diagnosis and effective prognosis. However, substantial AI research necessitates a large quantity of high-quality data to perform credible state-of-the-art research. To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE) and the conditional generative adversarial network (CTGAN), to generate high-quality synthetic tabular data of breast tumors and support the diagnosis and prognosis of breast cancer. Additionally, this study proposes an integrated interpretable deep-learning framework that includes the synthetic generation of breast cancer data leading to the classification of breast cancer using the interpretable deep attention-based model TabNet based on the domain of breast cancer research at every stage of the research framework. The research findings are justified using benchmark breast cancer datasets. After rigorous investigation, it was found that the TVAE model outperformed the synthetic generation of breast tumor data with a Chi-Squared test(CS test) score of 0.916 (prognosis) and 0.964 (diagnosis) and a Kolmogorov Smirnov test(KS test) score of 0.887 (prognosis) and 0.928 (diagnosis). In the classification stage, despite being trained with only synthetically generated data, the interpretable TabNet architecture outperformed all other machine-learning and deep-learning classifiers with an accuracy of 96.66 % in diagnosis and 82.83 % in prognosis.
Fuzzy Minimax Nets

In this article, we introduce fuzzy minimax nets as a novel tool to compute the greatest fuzzy bisimulation/simulation between two finite fuzzy labeled graphs. Fuzzy labeled graphs are a universal data structure for representing fuzzy systems, such as fuzzy automata, fuzzy labeled transition systems, fuzzy Kripke models, fuzzy social networks, and fuzzy interpretations in description logic. The greatest fuzzy bisimulation between two such systems characterizes the similarity between their states, actors, or individuals. Using fuzzy minimax nets, we design the first algorithms for the mentioned computational problems in the case of using the product t-norm, as well as the first algorithms whose complexity order does not depend on the fuzzy values occurring in the inputs for those problems in the case of using the Łukasiewicz t-norm.
A Fast and Secured Vehicle-to-Vehicle Energy Trading Based on Blockchain Consensus in the Internet of Electric Vehicles

The organization and management of electricity markets worldwide are rapidly evolving, moving towards decentralized, distributed, and renewable energy-based generation with solutions based on real-time data exchange. A Vehicle-to-Vehicle (V2V) energy trading has emerged as one of the most promising alternatives for relieving the load imposed on the traditional grid enabling two individuals to buy and sell energy directly without intermediaries. However, the Internet of Electric Vehicles (IoEV) environment is trustless, and such P2P energy trading is prone to different kinds of cyber attacks. Blockchain technology has lately been proposed to implement V2V energy trading to securely and fairly share energy. The consensus mechanism is one of the most important modules of blockchain applied to the V2V network. It determines the efficiency and security among untrustworthy EVs of the energy trading blockchain (ETB). Nevertheless, most works on ETB have currently adopted traditional consensus mechanisms. Due to high computing power and communication overhead, these consensus algorithms are unsuitable for applications requiring real-time services such as energy trading. The efficient and secured Hashgraph is the revolutionary technology of consensus in blockchain and a promising technology suitable for V2V energy trading with frequent transactions. However, Hashgraph does not support the dynamic addition and deletion of nodes and is completely decentralized and vulnerable to Sybil Attack in a large-scale blockchain. Furthermore, this “complete decentralization” model may result in states losing the ability to macro-control the energy industry and even systemic energy security issues. Therefore, we propose a Block Alliance Consensus (BAC) mechanism to solve these problems. BAC can maintain the throughput of Hashgraph and resist Sybil Attack in a large-scale P2P energy trading network. We design a cryptography-based leader election mechanism and adopt a reputation incentive mechanism to motivate honest and cooperative electric vehicles (EVs). Finally, we implement ETB and the BAC consensus mechanism on the Hyperledger Fabric platform. The high efficiency and security of BAC and the blockchain-based V2V energy trading platform are verified through experiments.
A Survey on Learning to Reject

Learning to reject is a special kind of self-awareness (the ability to know what you do not know), which is an essential factor for humans to become smarter. Although machine intelligence has become very accurate nowadays, it lacks such kind of self-awareness and usually acts as omniscient, resulting in overconfident errors. This article presents a comprehensive overview of this topic from three perspectives: confidence, calibration, and discrimination. Confidence is an important measurement for the reliability of model predictions. Rejection can be realized by setting thresholds on confidence. However, most models, especially modern deep neural networks, are usually overconfident. Therefore, calibration is a process to ensure confidence matching the actual likelihood of correctness, including two approaches: post-calibration and self-calibration. Calibration reflects the global characteristic of confidence, and the local distinguishing property of confidence is also important. In light of this, discrimination focuses on the performance of accepting positive samples while rejecting negative samples. As a binary classification problem, the challenge of discrimination comes from the missing and nonrepresentativeness of the negative data. Three discrimination tasks are comprehensively analyzed and discussed: failure rejection, unknown rejection, and fake rejection. By rejecting failures, the risk could be controlled especially for mission-critical applications. By rejecting unknowns, the awareness of the knowledge blind zone would be enhanced. By rejecting fakes, security and privacy could be protected. We provide a general taxonomy, organization, and discussion of the methods for solving these problems, which are studied separately in the literature. The connections between different approaches and future directions that are worth further investigation are also presented. With a discriminative and calibrated confidence, learning to reject will let the decision-making process be more practical, reliable, and secure.
NFT-IoT Pharma Chain : IoT Drug traceability system based on Blockchain and Non Fungible Tokens (NFTs)

The public’s health depends on a reliable drug supply chain. Recently, the number of drug counterfeit has increased drastically, resulting in thousands of victims suffering from poisoning and/or treatment failures, which have resulted in new expectations on drug supply chain traceability. Indeed, the drug supply chain involves many parties having heterogeneous interests and are usually reluctant to share traceability data with each other. Furthermore, existing traceability and provenance systems for drug supply chains suffer from separated data storage, lack of information sharing transparency, and trust. Decentralized blockchain-based solutions are advocated to address these limitations by realizing decentralized trustless systems. In this work, we present a fully decentralized, blockchain-based drug traceability solution, worthy of seamless integrating IoT devices throughout the chain. It uses both smart contracts and decentralized off-chain storage to remove the need for middleman and to provide trusted, secure and immutable transaction history. Moreover, to preserve the traceability of drugs, it ensures and enforces data provenance and data integrity in the proposed IoT environment by using blockchain Non-Fungible Tokens (NFTs). We give a test and validation of the approach’s effectiveness in enhancing drug traceability supply chains, as well as an analysis of the approach’s costs and security.
Long Short-Term Memory Neural Equalizer

A trainable neural equalizer based on the long short-term memory (LSTM) neural network architecture is proposed in this article to recover the channel output signal. The current widely used solution for the transmission line signal recovery is generally realized through a decision feedback equalizer (DFE) or : Feed forward equalizer (FFE) combination. The novel learning-based equalizer is suitable for highly nonlinear signal restoration, thanks to its recurrent design. The effectiveness of the LSTM equalizer (LSTME) is shown through an advance design system simulation channel signal equalization task, including a quantitative and qualitative comparison with an FFE–DFE combination. The LSTM neural network shows good equalization results compared with that of the FFE–DFE combination. The advantage of a trainable LSTME lies in its ability to learn its parameters in a flexible manner and to tackle complex scenarios without any hardware modification. This can reduce the equalizer implantation cost for variant transmission channels and bring additional portability in practical applications.
Exploiting Complementary Dynamic Incoherence for DeepFake Video Detection

Recently, manipulated videos based on DeepFake technology have spread widely on social media, causing concerns about the authenticity of video content and personal privacy protection. Although existing DeepFake detection methods achieve remarkable progress in some specific scenarios, their detection performance usually drops drastically when detecting unseen manipulation methods. Compared with static information such as human face, dynamic information depicting the movements of facial features is more difficult to forge without leaving visual or statistical traces. Hence, in order to achieve better generalization ability, we focus on dynamic information analysis to disclose such traces and propose a novel Complementary Dynamic Interaction Network (CDIN). Inspired by the DeepFake detection methods based on mouth region analysis, both the global (entire face) and local (mouth region) dynamics are analyzed with properly designed network branches, respectively, and their feature maps at various levels are communicated with each other using a newly proposed Complementary Cross Dynamics Fusion Module (CCDFM). With CCDFM, the global branch will pay more attention to anomalous mouth movements and the local branch will gain more information about the global context. Finally, a multi-task learning scheme is designed to optimize the network with both the global and local information. Extensive experiments have demonstrated that our approach achieves better detection results compared with several SOTA methods, especially in detecting video forgeries manipulated by unseen methods.
A Two-Stage PBFT Architecture With Trust and Reward Incentive Mechanism

The consensus algorithm is an essential ingredient of any blockchain system. Many different consensus mechanisms, such as practical Byzantine fault tolerance (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and their many derivatives, have been proposed over the years, but the complementary problems of performance and resilience to malicious behavior of the nodes have yet to be resolved in a satisfactory manner. In this work, we propose a consensus mechanism that integrates PoS with PBFT, which can effectively deal with dishonest nodes, both individual validators and leaders, while maintaining high performance. Our model incentivized truthful behavior by using trust score and reward mechanisms as crucial components of the block validation and ordering processes. The performance of the proposed scheme is evaluated using an analytical model that employs a semi-Markov process, defined by an ergodic multidimensional Markov chain with a finite number of states. The results show the efficiency of the proposed model in consensus-based decision making, even under a high likelihood of dishonest node behavior.
Intelligent Fault Diagnosis With Noisy Labels via Semisupervised Learning on Industrial Time Series

Deep neural networks (DNNs) excel at industrial fault diagnosis. Their performance heavily relies on the quality of human-annotated labels. Due to perception limitations of annotators, industrial time series samples (such as vibration and voltage signals) are frequently mislabeled in several conditions, such as samples with frequency domain feature differences and samples on class borders. Hence, an annotated industrial dataset will inevitably contain noisy labels at a certain level, leading to overfitting and poor generalization of DNNs. In this work, we introduce an industrial noisy label semisupervised learning (INL-SSL) fault diagnosis approach, addressing the problem that a certain number of samples in an industrial dataset are mislabeled. The proposed INL-SSL architecture simultaneously trains two DNNs, which cross-train on each other to filter noisy label errors. In particular, a fitted Gaussian mixture model divides time series samples of each DNN flow into an unlabeled set with samples likely to be noisy and a labeled set with samples likely to be clean. Given the labeled and unlabeled data, we proposed a time series MixMatch semisupervised learning strategy to train the diagnostic model. Ablation study verifies the benefit of the proposed time series augmentation techniques for semisupervised training. Extensive experiments on a benchmark industrial dataset of rolling element bearings (REB) reveal that the INL-SSL outperforms state-of-the-art approaches. On another self-collected REB dataset, the proposed approach also exceeds other comparison methods under noise ratios from 20% to 90%, validating the model's generalizability.
TranSkeleton: Hierarchical Spatial–Temporal Transformer for Skeleton-Based Action Recognition

In skeleton-based action recognition, it has been a dominant paradigm to extract motion features with temporal convolution and model spatial correlations with graph convolution. However, it’s difficult for temporal convolution to capture long-range dependencies effectively. Meanwhile, commonly used multi-branch graph convolution leads to high complexity. In this paper, we propose TranSkeleton, a powerful Transformer framework which neatly unifies the spatial and temporal modeling of skeleton sequences. For temporal modeling, we propose a novel partition-aggregation temporal Transformer. It works with hierarchical temporal partition and aggregation, and can capture both long-range dependencies and subtle temporal structures effectively. A difference-aware aggregation approach is designed to reduce information loss during temporal aggregation. For spatial modeling, we propose a topology-aware spatial Transformer which utilizes the prior information of human body topology to facilitate spatial correlation modeling. Extensive experiments on two challenging benchmark datasets demonstrate that TranSkeleton notably outperforms the state of the arts.
A hybrid Data-Driven framework for Spam detection in Online Social Network

Twitter is one of the most prominent online social networks(OSN) used by celebrities, politicians, ordinary people, and organizations to enhance their popularity and brand value. The popularity of Twitter makes it a prime target for spammers to attack. Earlier, spammers used to focus on email and web-search engines; therefore, extensive research in spam detection for email and web search engines has been done. However, the humongous traction of OSN has taken away the focus of spammers from email and web search engines towards OSN and micro-blogging sites. The study is inspired by the need to verify the legitimacy of a profile on OSN to avoid any fake information or rumors on OSN. This paper modulates a hybrid framework for spam detection on Twitter. The sampling algorithm SMOTE-ENN combines SMOTE and Edited Nearest Neighbors (ENN) to generate balanced data that is further fed to various deep learning classification techniques to identify whether the tweet is spam or ham. The efficacy and performance of various state-of-the-art algorithms are evaluated through simulation and are compared through various performance metrics to determine the best spam detection framework for OSN.
A Vision Based Approach to Localize Waste Objects and Geometric Features Exaction for Robotic Manipulation

The quantity of waste generated is increasing daily, demanding an intelligent waste management system for effective and timely solutions. Pollution induced by garbage has been one of the major problems globally for a long time. The unawareness among people about waste disposal causes a threat to living things and creates a major risk for sanitation workers during waste collection, sorting, and recycling. A method is proposed for real-time automatic waste material detection and segregation for easier recycling. The proposed approach uses the State-of-art deep learning architecture Mask RCNN to locate and classify waste objects from the natural environment. Further, the geometric features like centroid, orientation, and the clamping points of the objects are extracted to aid the robotic arm in grasping the waste object. Methods are investigated for ordering the objects in the scene for the energy-efficient automated system. The technique can be adapted into robotic machines for waste management to sort the waste on the road pavements and streets, providing safety, productivity, and reducing the risk on the sanitation workers.
Online Reconfigurable Convolutional Neural Network for Real-Time Applications

The omnipresence of real-time embedded systems enabled the smart paradigm, with smart devices being adopted by a larger portion of people worldwide. Such devices are capable of assisting people through the means of Human-Computer Interaction (HCI). Many features of these smart devices are based on Artificial Intelligence (AI) that uses neural networks (NN). Nevertheless, the computational power of embedded systems in smart devices is limited. Hence, adding more AI-powered modules becomes a challenge. Especially if the system is prone to an overload of tasks or can experience a failure that could lead to a sudden decrease in performance. To ensure that these cases are handled correctly without a total system failure, neural networks need to be able to be reconfigured quickly and effectively to cope with the available situation. This paper proposes a convolutional neural network (CNN) that can be reconfigured online at runtime to reduce its computational needs. The proposed approach changes the size of any NNs online without needing to save a whole new network to disk. This is done through offline training a network in its full size, then removing some layers and replacing them with a smaller number of layers while making sure only the new layers are being trained. After the new configuration has been trained offline, only the new layers are saved. This allows the network to be re-configurable while occupying very little disk space, simply by swapping the old layers and plugging in the new ones during runtime. This proposal has been tested on CIFAR 10 and was able to decrease the model size by up to 73.81% in only 0.18 seconds for a trade-off with accuracy.