Reviewerly/exHarmony huggingface dataset:
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having relatively fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.
HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection

The continuous strengthening of the security posture of Internet of Things (IoT) ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. Using Machine Learning (ML) capabilities to defend against IoT cyber attacks has many potential benefits. However, the currently proposed frameworks do not consider data privacy, secure architectures, and scalable deployments of IoT ecosystems. This paper proposes a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection . We highlight and demonstrate the importance of sharing cyber threat intelligence among inter-organisational IoT networks to improve the model’s detection capabilities. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The transactions (model updates) and processes will run on a secure blockchain , and the smart contract will verify the conformance of executed tasks. We have tested our solution and demonstrated its feasibility by implementing it and evaluating the intrusion detection performance using a key IoT data set. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
GNN-SubNet: disease subnetwork detection with explainable graph neural networks

The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability and explainability.In this work, we present a novel graph-based deep learning framework for disease subnetwork detection via explainable GNNs. Each patient is represented by the topology of a protein-protein interaction (PPI) network, and the nodes are enriched with multi-omics features from gene expression and DNA methylation. In addition, we propose a modification of the GNNexplainer that provides model-wide explanations for improved disease subnetwork detection.The proposed methods and tools are implemented in the GNN-SubNet Python package, which we have made available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet).Supplementary data are available at Bioinformatics online.
A comprehensive overview of Deepfake: Generation, detection, datasets, and opportunities

When used maliciously, deepfake can pose detrimental implications to political and social forces including reducing public trust in institutions, damaging the reputation of prominent individuals, and influencing public opinions. As there is currently no specific law to address deepfakes, thus deepfake detection, which is an action to discriminate pristine media from deepfake media, plays a vital role in identifying and thwarting deepfake. This paper provides readers with a comprehensive and easy-to-understand state-of-the-art related to deepfake generation and detection. Specifically, we provide a synthesized overview and recent progress in deepfakes by categorizing our review into deepfake generation and detection. We underline publicly available deepfake generation tools and datasets for benchmarking. We also provide research insights, discuss existing gaps, and present trends for future research to facilitate the development of deepfake research.
Collaboration among recruiters and artificial intelligence: removing human prejudices in employment

In the global war for talent, traditional recruiting methods are failing to cope with the talent competition, so employers need the right recruiting tools to fill open positions. First, we explore how talent acquisition has transitioned from digital 1.0 to 3.0 (AI-enabled) as the digital tool redesigns business. The technology of artificial intelligence has facilitated the daily work of recruiters and improved recruitment efficiency. Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application, screening, assessment, and coordination. Next, after interviewing with AI recruitment stakeholders (recruiters, managers, and applicants), the study discusses their acceptance criteria for each recruitment stage; stakeholders also raised concerns about AI recruitment. Finally, we suggest that managers need to be concerned about the cost of AI recruitment, legal privacy, recruitment bias, and the possibility of replacing recruiters. Overall, the study answers the following questions: (1) How artificial intelligence is used in various stages of the recruitment process. (2) Stakeholder (applicants, recruiters, managers) perceptions of AI application in recruitment. (3) Suggestions for managers to adopt AI in recruitment. In general, the discussion will contribute to the study of the use of AI in recruitment, as well as providing recommendations for implementing AI recruitment in practice.
Economic estimation of Bitcoin mining’s climate damages demonstrates closer resemblance to digital crude than digital gold

This paper provides economic estimates of the energy-related climate damages of mining Bitcoin (BTC), the dominant proof-of-work cryptocurrency. We provide three sustainability criteria for signaling when the climate damages may be unsustainable. BTC mining fails all three. We find that for 2016-2021: (i) per coin climate damages from BTC were increasing, rather than decreasing with industry maturation; (ii) during certain time periods, BTC climate damages exceed the price of each coin created; (iii) on average, each $1 in BTC market value created was responsible for $0.35 in global climate damages, which as a share of market value is in the range between beef production and crude oil burned as gasoline, and an order-of-magnitude higher than wind and solar power. Taken together, these results represent a set of sustainability red flags. While proponents have offered BTC as representing "digital gold," from a climate damages perspective it operates more like "digital crude".
Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

With the recent emphasis on new energy sources, solar photovoltaic cells have received widespread attention from scholars as a highly efficient and clean new energy source. Researchers model solar photovoltaic cells and analyze the values of various unknown parameters to improve their efficiency in converting solar energy into electric energy. To extract unknown parameters more accurately and efficiently, we propose a whale optimization algorithm (WOA) driven by the information sharing search mechanism and the Nelder-Mead simplex (NMs) mechanism, which ISNMWOA. The WOA can perform a global search of the parameters of the photovoltaic model to obtain a number of initial vectors of model parameters that satisfy the parameter range. The information sharing search strategy performs a further coarse local search on a number of feasible solutions generated. In contrast, the NMs can perform a further refined local search in the vicinity of the optimal parameters to obtain the final vector of optimally performing model parameters. First, we tested ISNMWOA against six advanced algorithms in CEC 2014. We then used the ISNMWOA to evaluate the parameters of the solar cells and PV modules. The RMSE of ISNMWOA on single diode model (SDM), double diode model (DDM), three diode model (TDM), and photovoltaic module model (PV) are 9.8602E-04, 9.8248E-04, 9.8248E-04 and 2.4251E-03 respectively. Compared to the original WOA, the RMSE of ISNMWOA on SDM, DDM, TDM, and PV decreased by 30.01%, 24.24%, 50.98%, and 6.94%, respectively. Moreover, compared to the high-performing GOFPANM, ISNMWOA runs 298 times faster than GOFPANM with almost identical optimization results. We evaluated the performance of three commercial PV modules under different irradiance and temperature conditions using ISNMWOA to verify the reliability of the experiment. The experimental results show that the ISNMWOA outperforms similar algorithms currently available.
MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications

With the recent advances in the Internet of Things (IoT) technologies, various human-centered applications have proliferated and improved the quality of users' life. In the meantime, human activity recognition (HAR) has been considered as an essential component of human-centered applications in IoT due to its capability of providing substantial information about the user states. Whereas deep learning-based HAR methods have been recently proposed, there are still rooms to improve the HAR models, particularly from the perspective of IoT, as the models need to be precise but resource efficient. In this paper, to support IoT systems that require a resource-efficient model, we thereby propose a deep learning-based HAR model, called MultiCNN-FilterLSTM that combines a multihead convolutional neural network (CNN) with a long–short-term memory (LSTM) through a residual connection in which feature vectors are efficiently processed in hierarchical order. Accordingly, a novel approach to using LSTM cells, which we call filterwise LSTM (FilterLSTM), is proposed whereby the HAR model can learn the dependencies among the features at different hierarchical levels. The proposed HAR model has been exhaustively evaluated on two publicly available datasets. The proposed HAR model enhances the classification accuracy by 2.3%–4.4%, while it requires 21%–70% fewer operations than the state-of-the-art models. In addition, the proposed model is deployed to a Raspberry Pi 4 for further analysis in terms of deployment. The experimental results are presented to verify the merits of the proposed HAR model compared to the state-of-art models by exploiting hierarchical relationships and to deliver significant insights on the effectiveness of the proposed FilterLSTM from the perspective of the IoT system.
Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System

Information about healthcare is derived from healthcare data. Healthcare data sharing helps make healthcare systems more efficient as well as improving healthcare quality. Patients should own and control healthcare information, one of their most valuable assets, instead of letting data be spread out among health care providers differ. This protects data from being shared between healthcare systems and privacy. Public ledger accompanied by a decentralized network of peer's compromises patient has been demonstrated to be able to achieve trusted, auditable computing by blockchain. The use of access control and cryptographic primitives are insufficient in addressing modern cyber threats all privacy and security concerns associated with a cloud-based environment. In this paper, the authors proposed a lightweight blockchain technique based on privacy and security for healthcare data for the cloud system. The cost-effectiveness of our system's smart contracts is evaluated, as well as the procedures used for data processing in order to encrypt and pseudonymize patient data.
Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.
Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Due to mobile Internet technology's rapid popularization, the Industrial Internet of Things (IIoT) can be seen everywhere in our daily lives. While IIoT brings us much convenience, a series of security and scalability issues related to permission operations rise to the surface during device communications. Hence, at present, a reliable and dynamic access control management system for IIoT is in urgent need. Up till now, numerous access control architectures have been proposed for IIoT. However, owing to centralized models and heterogeneous devices, security and scalability requirements still cannot be met. In this paper, we offer a smart contract token-based solution for decentralized access control in IIoT systems. Specifically, there are three smart contracts in our system, including the Token Issue Contract (TIC), User Register Contract (URC), and Manage Contract (MC). These three contracts collaboratively supervise and manage various events in IIoT environments. We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units (NTRU) to preserve user privacy during the registration process. Subsequently, to evaluate our proposed architecture's performance, we build a prototype platform that connects to the local blockchain. Finally, experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.
Gamma function based ensemble of CNN models for breast cancer detection in histopathology images

Breast cancer is the second deadliest disease amongst women worldwide. Breast histopathology image analysis is one of the most powerful ways used for the detection of tumour malignancies. Manual breast histopathology image analysis is, however, subjective, time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has become a popular and viable solution for medical image analysis due to recent advances in computer power and memory. However, the performance of the CAD models needs to be improved to use for practical purposes. Convolutional neural network (CNN) based models have achieved promising results for breast histopathological image classification. In this paper, instead of relying on a single CNN model, we have proposed a novel rank-based ensemble method by combining outcomes of three transfer learning CNN models, namely GoogleNet, VGG11 and MobileNetV3_Small. The proposed ensemble model is designed using the Gamma function for solving a 2-class classification problem of breast histopathological images. In comparison to state-of-the-art approaches, our method produces better classification results, with 99.16%, 98.24%, 98.67%, and 96.16% for 40X, 100X, 200X, and 400X levels of magnification, respectively, on a publicly accessible standard dataset called BreakHis and 96.95% on another well-known dataset called ICIAR-2018.
Event-Triggered Impulsive Controller Design for Synchronization of Delayed Chaotic Neural Networks and Its Fractal Reconstruction: An Application to Image Encryption

This paper examines synchronization of delayed chaotic neural networks with impulsive control, event-triggered impulsive control, and event-triggered delayed impulsive control. Lyapunov-based event-triggered mechanism is constructed to derive few sufficient conditions in terms of Linear Matrix Inequalities (LMIs). The feedback and event-triggered gain matrices are evaluated with the help of numerical packages. The fractal interpolation functions are employed to reconstruct the chaotic sequence of the neural networks since the chaotic attractor has self-similar properties and coincides with the fractal functions. In this study, a linear fractal interpolation is used to reconstruct both the master-slave chaotic attractor and the encrypted chaotic attractor, with optimized scaling factors. Finally, an application of the image encryption algorithm is given in terms of RSA public-key cryptosystems, permutation, and finite field diffusion for the chaotic sequence generated by fractal interpolation functions.
A secure image encryption algorithm based on hyper-chaotic and bit-level permutation

In this paper, an encryption algorithm based on hyper-chaotic system and bit-level permutation is proposed. Firstly, the forward diffusion method based on two-dimensional XOR operation is used to diffuse the image. Then, the scrambled images are arranged and scrambled in different directions. Finally, the ciphertext image is obtained by two-dimensional XOR backward diffusion. A novel bit-level permutation used in this algorithm, which not only expands the bit levels in different directions, but also exchanges the values between the bit bevels. It improves the efficiency and security of encryption. The diffusion random matrix is generated by hyperchaotic system. The initial value and control parameters of hyper-chaotic system are calculated by Secure Hash Algorithm 256 (SHA-256) function, which increases the key space and improves the plaintext sensitivity of the algorithm. On the one hand, the simulation results show that the scheme has high security and better plaintext sensitivity and key sensitivity. On the other hand, compared with the related algorithms, it can better resist statistical attacks and differential attacks.
Anomalib: A Deep Learning Library for Anomaly Detection

This paper introduces anomalib <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks and that can be used off-the-shelf. In addition, the library provides components to design custom algorithms that could be tailored towards specific needs. Additional tools, including experiment trackers, visualizers, and hyper-parameter optimizers, make it simple to design and implement anomaly detection models. The library also supports OpenVINO model-optimization and quantization for real-time deployment. Overall, anomalib is an extensive library for the design, implementation, and deployment of unsupervised anomaly detection models from data to the edge.
Multi-server assisted data sharing supporting secure deduplication for metaverse healthcare systems

The integration of Metaverse and healthcare will improve the allocation and utilization of healthcare resources. Metaverse healthcare data is stored on a public server and downloaded using Artificial Intelligence devices. Patients are given a diagnosis promptly through Metaverse healthcare systems. However, storing Metaverse healthcare data on public servers leads to a variety of problems, such as leakage of patient health information and loss of vital medical record data. Attribute-based encryption (ABE) has played an important role in securely and efficiently Metaverse healthcare data sharing. In this paper, a feature-rich and efficient ABE-based Metaverse healthcare data sharing scheme is presented, which realizes constant encryption computation overhead according to a multi-server structure. To reduce the number of invalid ciphertexts and achieve secure deduplication, Metaverse healthcare ciphertext validity and equivalence detection are also provided in this scheme. Furthermore, a novel attribute-based re-encryption is proposed for authority delegation after deduplication. Finally, the experiment simulated on the PBC library shows that the proposed scheme possesses good performance. The freshness of Metaverse healthcare data and the utilization of the medical center’s private server are ensured in this paper.
Automating code review activities by large-scale pre-training

Code review is an essential part to software development lifecycle since it aims at guaranteeing the quality of codes. Modern code review activities necessitate developers viewing, understanding and even running the programs to assess logic, functionality, latency, style and other factors. It turns out that developers have to spend far too much time reviewing the code of their peers. Accordingly, it is in significant demand to automate the code review process. In this research, we focus on utilizing pre-training techniques for the tasks in the code review scenario. We collect a large-scale dataset of real-world code changes and code reviews from open-source projects in nine of the most popular programming languages. To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review scenario. To evaluate our model, we focus on three key tasks related to code review activities, including code change quality estimation, review comment generation and code refinement. Furthermore, we establish a high-quality benchmark dataset based on our collected data for these three tasks and conduct comprehensive experiments on it. The experimental results demonstrate that our model outperforms the previous state-of-the-art pre-training approaches in all tasks. Further analysis show that our proposed pre-training tasks and the multilingual pre-training dataset benefit the model on the understanding of code changes and reviews.