Reviewerly/exHarmony huggingface dataset:
Lightweight EfficientNetB3 Model based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Acute lymphoblastic leukemia (ALL) occurs when undeveloped lymphocytes are grown excessively in the bone marrow due to the formation of many immature white blood cells (WBC) in the bone marrow to destroy the healthy cell, which may lead to death.White blood cells are part of the immune system fighting against germs.A timely and accurate cancer diagnosis is important for effective treatment to improve survival rates.Since the image of acute lymphoblastic leukemia cells (cancer cells) under the microscope is complicated to recognize the difference between ALL cancer cells and normal cells.In order to reduce the severity of this disease, it is necessary to classify immature cells at an early stage.In recent years, different classification models have been introduced based on machine learning (ML) and deep learning (DL) algorithms, but they need to be improved.This work enhances the diagnosis of ALL with a computer-aided system that yields accurate results by using deep learning techniques.This research proposed a lightweight DL-assisted robust model based on EfficientNet-B3 using depthwise separable convolutions for classifying acute lymphoblastic leukemia and normal cells in the white blood cell images dataset.The main objective of the proposed lightweight EfficientNet-B3 is to utilize less trainable parameters to enhance the performance and efficiency of the classification of leukemia.Furthermore, two publicly available datasets are considered to evaluate the effectiveness and generalization of the proposed lightweight EfficientNet-B3.In addition, different measures are employed to evaluate the effectiveness and efficiency of the proposed classifier for the accurate and reliable classification of leukaemia cells.In addition, a detailed analysis is given to evaluate and compare the performance and efficiency of the proposed with existing pre-trained and ensemble DL classifiers.Based on experimental results, it is investigated that the proposed model for image classification achieves better performance and outperforms the existing benchmark DL and other ensemble classifiers.Moreover, our finding suggests that the proposed lightweight EfficientNet-B3 model is reliable and generalized to facilitate clinical research and practitioners for leukemia detection.
Computational Framework of Inverted Fuzzy C-Means and Quantum Convolutional Neural Network Towards Accurate Detection of Ovarian Tumors

Due to the advancements in the lifestyle, stress builds enormously among individuals. A few recent studies have indicated that stress is a major contributor for infertility and subsequent ovarian cancer among women of reproductive age. In view of this, the present study proposes a two-stage computational methodology to identify and segment the ovarian tumour and classify it as benign or malignant. Using computerized tomography images, the first stage involves image segmentation using inverted fuzzy c-Means clustering, and second stage consists of deep quantum convolutional neural network in order to detect the tumours. The efficacy of the proposed method is demonstrated using in-house clinically collected dataset by comparing the results with the state-of-the-art methods. The experimental results confirm that the proposed approach outperforms the existing fuzzy C means algorithm by achieving the average Jaccard score of (0.65, 0.84, 0.79) (min, max, avg) and Dice score of (0.70, 0.83, 0.77) (min, max, avg), classification result of 78% for benign and 70.03% for malignant tumours. The classification results using the variant of convolutional neural network (CNN) model ResNet16 are compared with the quantum convolutional neural networks (QCNN) and obtained the classification performance of 87.02% for benign and 79.4% for malignant tumours and 84.4% for benign and 77.03% for malignant tumours respectively.
Classical shadow tomography with locally scrambled quantum dynamics

We generalize the classical shadow tomography scheme to a broad class of finite-depth or finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where the unitary ensemble is invariant under local-basis transformations. In this case, the reconstruction map for the classical shadow tomography depends only on the average entanglement feature of classical snapshots. We provide an unbiased estimator of the quantum state as a linear combination of reduced classical snapshots in all subsystems, where the combination coefficients are solely determined by the entanglement feature. We also bound the number of experimental measurements required for the tomography scheme, so-called sample complexity, by formulating the operator shadow norm in the entanglement feature formalism. We numerically demonstrate our approach for finite-depth local unitary circuits and finite-time local-Hamiltonian generated evolutions. The shallow-circuit measurement can achieve a lower tomography complexity compared to the existing method based on Pauli or Clifford measurements. Our approach is also applicable to approximately locally scrambled unitary ensembles with a controllable bias that vanishes quickly. Surprisingly, we find a single instance of time-dependent local Hamiltonian evolution is sufficient to perform an approximate tomography as we numerically demonstrate it using a paradigmatic spin chain Hamiltonian modeled after trapped ion or Rydberg atom quantum simulators. Our approach significantly broadens the application of classical shadow tomography on near-term quantum devices.
Efficient FHEW Bootstrapping with Small Evaluation Keys, and Applications to Threshold Homomorphic Encryption

There are two competing approaches to bootstrap the FHEW fully homomorphic encryption scheme (Ducas and Micciancio, Eurocrypt 2015) and its variants: the original AP/FHEW method, which supports arbitrary secret key distributions, and the improved GINX/TFHE method, which uses much smaller evaluation keys, but is directly applicable only to binary secret keys, restricting the scheme's applicability. In this paper, we present a new bootstrapping procedure for FHEW-like encryption schemes that achieves the best features of both methods: support for arbitrary secret key distributions at no additional runtime costs, while using small evaluation keys. (Support for arbitrary secret keys is critical in a number of important applications, like threshold and some multi-key homomorphic encryption schemes.) As an added benefit, our new bootstrapping procedure results in smaller noise growth than both AP and GINX, regardless of the key distribution. Our improvements are both theoretically significant (offering asymptotic savings, up to a $$O(\log n)$$ multiplicative factor, either on the running time or public evaluation key size), and practically relevant. For example, for a concrete 128-bit target security level, we show how to decrease the evaluation key size of the best previously known scheme by more than 30%, while also slightly reducing the running time. We demonstrate the practicality of the proposed methods by building a prototype implementation within the PALISADE/OpenFHE open-source homomorphic encryption library. We provide optimized parameter sets and implementation results showing that the proposed algorithm has the best performance among all known FHEW bootstrapping methods in terms of runtime and key size. We illustrate the benefits of our method by sketching a simple construction of threshold homomorphic encryption based on FHEW.
Transformers in the Real World: A Survey on NLP Applications

The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of NLP. In this survey, we review the open-access and real-world applications of transformers in NLP, specifically focusing on those where text is the primary modality. Our goal is to provide a comprehensive overview of the current state-of-the-art in the use of transformers in NLP, highlight their strengths and limitations, and identify future directions for research. In this way, we aim to provide valuable insights for both researchers and practitioners in the field of NLP. In addition, we provide a detailed analysis of the various challenges faced in the implementation of transformers in real-world applications, including computational efficiency, interpretability, and ethical considerations. Moreover, we highlight the impact of transformers on the NLP community, including their influence on research and the development of new NLP models.
Bridging the gap between mechanistic biological models and machine learning surrogates

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.
Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding-based Internet of Things (IoT) systems. To tackle this issue, we propose in this article a rapid detection approach, which consists of multistage stepwise discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The average detection precision (ADP), multiple box rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications.
Consensus cluster structure guided multi-view unsupervised feature selection

As the volume of high-dimensional multi-view data continues to grow, there has been a significant development in multi-view unsupervised feature selection methods, particularly those that perform graph learning and feature selection simultaneously. These methods typically begin by constructing a consensus graph, which is then utilized to ensure that the projected samples maintain the local structure of data. However, these methods require data from multiple views to preserve the same manifold structure, which goes against the reality that similarities may vary across different views. On the other hand, despite inconsistencies between heterogeneous features, multiple views share a unique cluster structure. Inspired by this, we propose consensus cluster structure guided multi-view unsupervised feature selection (CCSFS). Specifically, we generate multiple cluster structures and fuse them into a consensus structure to guide feature selection. The proposed method unifies subspace learning, cluster analysis, consensus learning and sparse feature selection into one optimization framework. By leveraging the inherent interactions between these four subtasks, CCSFS can finally select informative and discriminative features. An efficient algorithm is carefully designed to solve the optimization problem of the objective function. We conduct extensive clustering experiments on seven multi-view datasets to demonstrate that the proposed method outperforms some of the latest competitors.
A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes

Daily activity recognition between different smart home environments faces some challenges, such as an insufficient amount of data and differences in data distribution. However, a deep network requires a large amount of labeled data for training. Additionally, inconsistent data distribution can lead to over-fitting of network learning. Additionally, the time cost of training the network from scratch is too high. In order to solve the above problems, this paper proposes a fine-tuning method suitable for daily activity recognition, which is the first application of this method in our field. Firstly, we unify the sensor space and activity space to reduce the variability in heterogeneous environments. Then, the Word2Vec algorithm is used to transform the activity samples into digital vectors recognizable by the network. Finally, the deep network is fine-tuned to transfer knowledge and complete the recognition task. Additionally, we try to train the network on public datasets. The results show that the network trained on a small dataset also has good transferability. It effectively improves the recognition accuracy and reduces the time cost and heavy data annotation.
Medical image super-resolution reconstruction algorithms based on deep learning: A survey

With the high-resolution (HR) requirements of medical images in clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution (LR) medical images have become a research hotspot. This type of method can significantly improve image SR without improving hardware equipment, so it is of great significance to review it. Aiming at the unique SR reconstruction algorithms in the field of medical images, based on subdivided medical fields such as magnetic resonance (MR) images, computed tomography (CT) images, and ultrasound images. Firstly, we deeply analyzed the research progress of SR reconstruction algorithms, and summarized and compared the different types of algorithms. Secondly, we introduced the evaluation indicators corresponding to the SR reconstruction algorithms. Finally, we prospected the development trend of SR reconstruction technology in the medical field. The medical image SR reconstruction technology based on deep learning can provide more abundant lesion information, relieve the expert's diagnosis pressure, and improve the diagnosis efficiency and accuracy. The medical image SR reconstruction technology based on deep learning helps to improve the quality of medicine, provides help for the diagnosis of experts, and lays a solid foundation for the subsequent analysis and identification tasks of the computer, which is of great significance for improving the diagnosis efficiency of experts and realizing intelligent medical care.
Artificial intelligence and blockchain technology for secure smart grid and power distribution Automation: A State-of-the-Art Review

Artificial Intelligence (AI) integrated with Blockchain distributed ledger technology (BDLT) has become the most attractive research area in the domain of renewable energy and related power automations. However, the increased use of renewable energy-enabled devices raised various challenging problems, such as smart grid-based control management, power distribution, and automations. In this manner, the collaborative approach of blockchain, AI, and wireless sensor networks (WSN) provides a secure platform for control centers to estimate state, which helps to detect and analysis of bad data movement. There are various emerging issues in the field of Blockchain-AI in a renewable environment that poses a serious impact on the technological evaluation, for example, monitoring of contingency, optimal power flow, network reconfiguration, and the commitment of security-constrained units, and control auto-generation. So, in this paper, we perform a systematic review of the state-of-the-art integrated artificial intelligence and blockchain-enabled scheduling, management, optimization, privacy, and security of the smart grid and power distribution automation. One of the focusing aspects of this research is the real-time analysis of the physical layer of the smart grid. However, in this paper, we design a framework of a unified and abstracted state-space, in which the system analysis involves malicious attacks and maintain an effective generalized defense hierarchy in real-time. The current mechanism of analysis of smart grid-enabled physical layer-based malicious attacks is categorized into their associative and targeted components. Thus, we present three different pseudo-smart contracts and digital signatures with consensus policies; that provide an understanding of the new registry of renewable smart grids details, participating stakeholders and their roles, and an updated power distribution automation ledger. We then highlighted the list of emerging power distribution automation-related limitations along with the informational management approaches that present the existing state-of-the-art in this domain, including data-driven, target defense, computation, preservation, etc. Finally, we discuss open research issues and future directions of the smart grid and automation of power distribution security and privacy.
Distributed Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching in Internet-of-Vehicles

Edge caching is a promising approach to reduce duplicate content transmission in Internet-of-Vehicles (IoVs). Several Reinforcement Learning (RL) based edge caching methods have been proposed to improve the resource utilization and reduce the backhaul traffic load. However, they only obtain the local sub-optimal solution, as they neglect the influence from environments by other agents. This paper investigates the edge caching strategies with consideration of the content delivery and cache replacement by exploiting the distributed Multi-Agent Reinforcement Learning (MARL). A hierarchical edge caching architecture for IoVs is proposed and the corresponding problem is formulated with the goal to minimize the long-term content access cost in the system. Then, we extend the Markov Decision Process (MDP) in the single agent RL to the context of a multi-agent system, and tackle the corresponding combinatorial multi-armed bandit problem based on the framework of a stochastic game. Specifically, we firstly propose a Distributed MARL-based Edge caching method (DMRE), where each agent can adaptively learn its best behaviour in conjunction with other agents for intelligent caching. Meanwhile, we attempt to reduce the computation complexity of DMRE by parameter approximation, which legitimately simplifies the training targets. However, DMRE is enabled to represent and update the parameter by creating a lookup table, essentially a tabular-based method, which generally performs inefficiently in large-scale scenarios. To circumvent the issue and make more expressive parametric models, we incorporate the technical advantage of the Deep- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> Network into DMRE, and further develop a computationally efficient method (DeepDMRE) with neural network-based Nash equilibria approximation. Extensive simulations are conducted to verify the effectiveness of the proposed methods. Especially, DeepDMRE outperforms DMRE, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, LFU, and LRU, and the edge hit rate is improved by roughly 5%, 19%, 40%, and 35%, respectively, when the cache capacity reaches 1, 000 MB.
Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations

In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model's accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.
ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles

Virtual simulated scenes are becoming a critical part of autonomous driving. In the context of knowledge automation and machine learning, simulated images are widely used for visual environmental perception. However, even the most inspirational applications have not fully exploited the potential of simulated images in solving real-world problems. In this article, we propose a novel framework “ParallelEye Pipeline,” which uses image-to-image translation and simulated images to automatically generate realistic synthetic images with multiple ground-truth annotations. Specifically, this method has three steps: first, we use Unity3D software to simulate driving scenarios and generate simulated image pairs (including raw images and six ground-truth labels) from the simulated scenes; second, advanced image-to-image translation algorithms can generate realistic and high-resolution synthetic images from simulated image pairs; third, we exploit publicly datasets, simulated images, and synthetic images to conduct experiments for visual perception. The experimental results suggest: 1) synthetic images and simulated images can improve the performance of detectors in real autonomous driving scenarios and 2) image-to-image translation algorithms can be affected by occlusion condition.