Reviewerly/exHarmony huggingface dataset:
Trading mode design for a virtual power plant based on main-side consortium blockchains

• A VPP transaction structure integrating main-side chains and multi-agent technology is established with strong scalability; • A reputation evaluation and feedback mechanism matching the main chain of VPP is designed to maintain a good VPP operation environment; • The specific trading process of VPP based on the main-side chains and the key smart contract functions are clarified; • The VPP internal market rules are conducive to economic and efficient operation. To stimulate the market vitality of distributed resources, the virtual power plant (VPP) has gradually transformed from a third-party profit organization to a service-oriented trading platform, and the transaction has shown a trend of decentralization. In this case, a new VPP trading mode is designed to solve the difficulties in multi-layer coordination and the lack of trust between market entities from the same layer. First, the consortium chain is combined with the multi-agent system to build a VPP internal transaction framework. In order to match the characteristic of the VPP with multi-layer market entities, the main-side chain governance structure is adopted to form data barriers and ensure the scalability of the transaction framework. Second, the existing consensus protocol is improved in accordance with the feature of VPP main chain, and the consensus reputation is introduced to maintain the operation efficiency of the blockchain. In order to improve the execution quality of distributed transactions and the VPP operation stability, the trading reputation closely related to the transaction result is introduced to constrain the trading behavior of aggregators, and a feasible bidding strategy is provided. Finally, the specific trading process of VPP based on the main-side chains is analyzed, and the main functions of smart contracts are designed in combination with internal market rules. The simulation results prove the feasibility and effectiveness of the proposed VPP trading mode.
Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection

Image Anomaly Detection is a significant stage for visual quality inspection in intelligent manufacturing systems. According to the assumption that only normal images are available during the training stage, unsupervised methods have been studied recently for image anomaly detection. But anomalous images of small scale can be collected for training in many real-world industrial scenarios, and the unsupervised methods make no use of them to improve the detection accuracy. This leads to a semi-supervised image anomaly detection with an unbalanced detection challenge. In this paper, a Logit Inducing with Abnormality Capturing (LIAC) method is proposed to address semi-supervised image anomaly detection. Firstly, a Logit Inducing Loss is proposed to train a classifier for dealing with unbalanced detection. And secondly, an Abnormality Capturing Module is proposed to address anomaly detection. With labeling only 40 anomalous images for training, the proposed LIAC method achieves a 98.8% f1-score on the image anomaly detection of the printed circuit board, compared with the state-of-the-art methods. More, the proposed LIAC method is experimentally compared with the state-of-the-art methods on MTD, ROCT, and ELPV three open-source datasets, respectively achieves f1-score of 85.2%, 96.8%, and 66.6% with given 40 anomalous images for training.
GAN Against Adversarial Attacks in Radio Signal Classification

Although Deep Neural Networks (DNN) can achieve state-of-the-art performance in automatic modulation recognition (AMC) tasks, they have sufferd tremendous failures from adversarial attacks, which means the input signals are contaminated by imperceptible but intentional perturbations. However, little work has been done to consider eliminating adversarial perturbations while keeping the high classification accuracy of clean signals. In this letter, we propose an effective data preprocess framework based on Generative Adversarial Nets (GAN) to defend against the adversarial examples. The experiments show that the proposed method can effectively eliminate adversarial perturbations and maintain the high classification accuracy of clean samples.
A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most extensively studied problems in the combinatorial optimization area and still presents unsolved challenges due to its NP-hard attribute.Although many real-coded algorithms are available for TSP, they still have some performance challenges in the switch from continuous space to discrete space and perform at low convergence speed.This paper proposes a real-coded carnivorous plant algorithm with a heuristic decoding method (CPA-HDM) to solve the traveling salesman problem (TSP), which exhibits good convergence speed and solution accuracy.In this improved method, a new heuristic decoding method (HDM) is designed, which helps to map continuous variables to discrete ones without losing information, maintain population diversity, and enhance the solution quality after decoding.To balance the algorithm's search capability and enhance the probability of preferable individuals generated, an adaptive attraction probability (AAP), an improved growth model of carnivorous plants (IGMOCP), and a position update method of prey (IPUMOP) are developed.Aiming to reduce the probability of premature and prevent search stagnation, an improved reproduction strategy (IRS) and an adaptive combination perturbation are reconstructed.Finally, a local search algorithm is employed to improve the exploitation capability.To verify its validation, CPA-HDM is compared with six algorithms, for solving 28 TSP instances.The simulation results and statistical analyses demonstrate the superior performance of the proposed algorithm.
Can deep convolutional neural networks support relational reasoning in the same-different task?

Same-different visual reasoning is a basic skill central to abstract combinatorial thought. This fact has lead neural networks researchers to test same-different classification on deep convolutional neural networks (DCNNs), which has resulted in a controversy regarding whether this skill is within the capacity of these models. However, most tests of same-different classification rely on testing on images that come from the same pixel-level distribution as the training images, yielding the results inconclusive. In this study, we tested relational same-different reasoning in DCNNs. In a series of simulations we show that models based on the ResNet architecture are capable of visual same-different classification, but only when the test images are similar to the training images at the pixel level. In contrast, when there is a shift in the testing distribution that does not change the relation between the objects in the image, the performance of DCNNs decreases substantially. This finding is true even when the DCNNs' training regime is expanded to include images taken from a wide range of different pixel-level distributions or when the model is trained on the testing distribution but on a different task in a multitask learning context. Furthermore, we show that the relation network, a deep learning architecture specifically designed to tackle visual relational reasoning problems, suffers the same kind of limitations. Overall, the results of this study suggest that learning same-different relations is beyond the scope of current DCNNs.
Scene Graph Semantic Inference for Image and Text Matching

With the rapid development of information technology, image and text data have increased dramatically. Image and text matching techniques enable computers to understand information from both visual and text modalities and match them based on semantic content. Existing methods focus on visual and textual object co-occurrence statistics and learning coarse-level associations. However, the lack of intramodal semantic inference leads to the failure of fine-level association between modalities. Scene graphs can capture the interactions between visual and textual objects and model intramodal semantic associations, which are crucial for the understanding of scenes contained in images and text. In this article, we propose a novel scene graph semantic inference network (SGSIN) for image and text matching that effectively learns fine-level semantic information in vision and text to facilitate bridging cross-modal discrepancies. Specifically, we design two matching modules and construct scene graphs within each matching module for aggregating neighborhood information to refine the semantic representation of each object and achieve fine-level alignment of visual and textual modalities. We perform extended experiments in Flickr30K and MSCOCO and achieve state-of-the-art results, which validate the advantages of our proposed approach.
Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification

Contrastive self-supervised learning has recently benefited fMRI classification with inductive biases. Its weak label reliance prevents overfitting on small medical datasets and tackles the high intraclass variances. Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored. Additionally, existing methods predict labels on individual contrastive representation without recognizing neighbouring information in the patient group, whereas interpatient contrast can act as a similarity measure suitable for population-based classification. We hereby proposed contrastive functional connectivity graph learning for population-based fMRI classification. Representations on the functional connectivity graphs are “repelled” for heterogeneous patient pairs meanwhile homogeneous pairs “attract” each other. Then a dynamic population graph that strengthens the connections between similar patients is updated for classification. Experiments on a multi-site dataset ADHD200 validate the superiority of the proposed method on various metrics. We initially visualize the population relationships and exploit potential subtypes. Our code is available at https://github.com/xuesongwang/Contrastive-Functional-Connectivity-Graph-Learning .
Non-fungible tokens: Use cases of NFTs and future research agenda

Blockchain is a disruptive technology that is applied in many different areas (Atici, 2022). Non-fungible tokens (NFTs) are becoming increasingly popular and are already widely used in practice. New use cases for NFTs are emerging all the time. Due to the fact that they are unique, NFTs can prevent counterfeiting, as each token contains the digital signature of the owner (Rehman, Zainab, Imran, &amp; Bawany, 2021). For the use of NFTs to advance in the institutional setting, the opportunities for using NFTs need to be clearly explored. Based on a systematic literature review (SLR), this paper describes and lists the most discussed use cases for NFTs in the scientific literature. The most discussed use cases are art and collectibles, video games, and applications in the metaverse. Another contribution of this article is a future research agenda that contains open questions in the field of NFTs. It aims to provide researchers with promising research avenues to stimulate future research. The results show that there are still challenges in the field of NFT that need to be further explored. In summary, our article aims to equip both researchers and practitioners with an initial overview and knowledge about NFTs, including their applications and challenges.
Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening

Abstract Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
Adversarial Attacks and Defenses Toward AI-Assisted UAV Infrastructure Inspection

Unmanned aerial vehicles (UAVs) have been widely adopted to assist infrastructure inspection tasks, since they have shown their potential to benefit the inspection in terms of efficiency, cost, and safety. To improve the effectiveness of the inspection, there has been a growing focus of recent research in integrating AI techniques into UAV infrastructure inspection and has achieved promising results. However, no prior work has studied whether such integration will also introduce new security concerns, especially considering the existence of potential vulnerabilities in underlying AI models toward adversarial inputs. In this article, we perform the first study to fill this critical gap by identifying and validating the security vulnerabilities of AI models in the context of UAV infrastructure inspection with a focus on bridge infrastructure. To understand the security property of AI-assisted UAV bridge inspection, we design a two-stage approach for the construction of effective adversarial inputs, with which we successfully validate the existence of security vulnerability using dynamic analysis. Spatial constraints, physical limits, and dynamic environmental changes are taken into consideration in our analysis to make it practical in the physical world. Our evaluation results on a real-world data set show that our constructed adversarial inputs can mislead the UAV to miss detecting a significant amount of risk-prone regions by exploiting the identified vulnerability. Based on such an observation, this article also discusses the defenses based on adversarial training to improve the robustness of AI-assisted UAV bridge inspection, which has been demonstrated to be effective according to our experimental evaluation results.
A Novel Remote Visualization of Screen Images Against High-Resolution Display With Divided Screens Focusing on the Difference of Transfer Function of Multiple Emanations

Electromagnetic (EM) remote visualization of screen images attacks (RVSIAs) is among the most actively researched topics in hardware security. Target displays are being developed daily; for example, recently developed high-resolution displays have multiple divided areas on the screen, and each area transmits different pixel information. In this case, it is difficult to reconstruct the screen using the conventional methods because the target screen information is leaked by multiple lines at the same frequency and timing, and multiple screen information is mixed into the EM waves. By contrast, in this article, we propose a novel RVSIA method that focuses on the difference in the transfer functions of multiple emanations from a high-resolution display. Considering the structure and signal transmission method of recent high-resolution displays, the proposed attack method observes leaked EM waves from multiple observation positions and alters the receiving frequencies of the receiver. For the first time, we have been able to separate multiple screen information contained in leaked EM waves using an independent component analysis method. In the experiments, we applied the proposed method to an actual laptop PC that divides the screen into multiple areas. As a result, it was possible to reconstruct screen information using the proposed attack method. Thus, the proposed method enables successful attacks against recent high-resolution displays. Furthermore, the measurement equipment employed remains similar to those utilized in the conventional attacks.
Resource-Efficient Authenticated Data Sharing Mechanism for Smart Wearable Systems

Recent technological developments in the wearable devices (WDs) and smartphones enabled ubiquitous and always-on monitoring of the users' movements. WDs collect the sensitive and personal information associated with a user's health and send the collected information to the user's terminal or smart device (SD). The SD allows the user to store the collected information on the cloud server (CSE) for subsequent sharing with other users, such as doctors. The data storage and sharing process thus performed is prone to various security challenges, which mandate a reliable and authenticated data storage and a secure data sharing mechanism. This article presents an access control scheme based on an authenticated encryption algorithm, called elliptic curve cryptography, and hash function. The scheme allows users to get authenticated and establish a session key (SK) with CSE. The establishment of SK enables users to securely store the data in the storage module of CSE. Additionally, before storing, the user encrypts the data using a secret key derived from the users' bio-metric information. Moreover, the scheme allows a user to share his data with other users without requiring the security constraints implemented by the cloud service providers. The security of the SK established under the scheme is validated formally through Scyther and the random oracle models. Moreover, informal analysis is conducted, which illustrates that the proposed scheme is resilient against various security attacks. Finally, the proposed scheme is compared with the existing scheme to ensure its efficiency and superior security features.
An Optimization Framework for Efficient Vision-Based Autonomous Drone Navigation

Fully autonomous drones are a new emerging field that has enabled many applications such as gas source leakage localization, wild-fire detection, smart agriculture, and search and rescue missions in unknown limited communication and GPS denied environments. Artificial intelligence and deep Neural Networks (NN) have enabled applications such as visual perception and navigation which can be deployed to make drones smarter and more efficient. However, deploying such techniques on tiny drones is extremely challenging due to the limited computational resources and power envelope of edge devices. To achieve this goal, this paper proposes an efficient end-to-end optimization method for deploying deep NN models for vision-based autonomous drone navigation applications, such as obstacle avoidance and steering task. This paper formulates two different methods for implementing the NN inference phase onto tiny drones and analyzing the implementation results for each case: 1) a Cloud-IoT implementation and 2) Onboard Processing. Several models are trained with state-of-the-art scalable NN architectures and the most efficient cases in terms of computation complexity and accuracy are selected for implementation on a cloud server and several edge devices. By designing hardware-friendly NN models and optimal configuration of the implementation platforms, we were able to reach up to 97% accuracy, speed up the computation 2.3×, have 22× less complexity, and 53% energy reduction. Also, we achieve up to 25 fps on the GAP8 processor, which is enough for real-time drone navigation requirements, even when the model is running on a small IoT device.
A novel blood glucose time series prediction framework based on a novel signal decomposition method

Unstable blood glucose levels will bring about a series of complications, and even threaten our life in severe cases. Therefore, accurately predicting and controlling blood glucose has become an indispensable part of diabetes treatment. For the sake of boosting the accuracy of blood glucose prediction, a novel time-frequency signal analytical technology called improved ensemble empirical mode decomposition based on fractal dimension (FEEMD) is first proposed to decompose the nonlinear and non-stationary raw blood glucose series to obtain intrinsic mode functions (IMFs) and residual components (Rs) in different frequency bands. Then, in order to overcome the problem that the kernel extreme learning machine (KELM) is easy to fall into the local optimum, the sparrow search algorithm (SSA) is introduced to further enhance the robustness and stability of KELM model. Ultimately, the predictions of each optimized KELM are fused to achieve the final prognostic results. Through the experimental results of three patients, the following contributions can be summarized: (a) FEEMD exploits the box-counting dimension to remove abnormal signals in the original sequence, which guarantees the completeness of decomposition and obtains better decomposition results; (b) SSA is exploited to optimize the KELM, which can significantly boost the prediction effects; (c) When compared with the other seven benchmark models, the proposed framework in this paper is more suitable for predicting blood glucose and has higher prediction accuracy; (d) This paper also verifies that the proposed architecture can effectively reduce the false negative rate and increase the overall alarming accuracy in early warning of hypoglycemia.
Triple-View Feature Learning for Medical Image Segmentation

Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit promising performance in medical image segmentation, but come with a high labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses triple-view feature learning on a limited amount of labelled data and a large amount of unlabeled data. The triple-view architecture consists of three pixel-level classifiers and a low-level shared-weight learning module. The model is first initialized with labelled data. Label processing, including data perturbation, confidence label voting and unconfident label detection for annotation, enables the model to train on labelled and unlabeled data simultaneously. The confidence of each model gets improved through the other two views of the feature learning. This process is repeated until each model reaches the same confidence level as its counterparts. This strategy enables triple-view learning of generic medical image datasets. Bespoke overlap-based and boundary-based loss functions are tailored to the different stages of the training. The segmentation results are evaluated on four publicly available benchmark datasets including Ultrasound, CT, MRI, and Histology images. Repeated experiments demonstrate the effectiveness of the proposed network compared against other semi-supervised algorithms, across a large set of evaluation measures.