Reviewerly/exHarmony huggingface dataset:
Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does not require any mathematical model to drive a system inside an unknown environment. This lack of intuition can be an obstacle to design experiments and implement this approach. Reversely there is a need to gain experience and intuition from experiments. In this article, we propose a general framework to reproduce successful experiments and simulations based on the inverted pendulum, a classic problem often used as a benchmark to evaluate control strategies. Two algorithms (basic Q-Learning and Deep Q-Networks (DQN)) are introduced, both in experiments and in simulation with a virtual environment, to give a comprehensive understanding of the approach and discuss its implementation on real systems. In experiments, we show that learning over a few hours is enough to control the pendulum with high accuracy. Simulations provide insights about the effect of each physical parameter and tests the feasibility and robustness of the approach.
A more efficient public-key authenticated encryption scheme with keyword search

To realize ciphertext retrieval without decryption in the public key settings, Boneh et al. in 2004 proposed a cryptographic primitive named Public-key Encryption with Keyword Search (PEKS) and proposed the first PEKS scheme. Following Boneh et al.’s work, various PEKS schemes were proposed; however, most schemes are vulnerable to inside keyword guessing attacks (IKGA). Huang and Li proposed a new primitive named Public-key Authenticated Encryption with Keyword Search (PAEKS) in 2017, which resists the IKGA in the setting of only one test server. Their main idea to resist IKGA is to require the data sender to authenticate the ciphertext while encrypting a keyword. However, in the era of big data, as the number of encrypted files increases, huge storage overhead and heavy retrieval costs become problems in PAEKS. In this paper, we proposed a new PAEKS scheme that costs low storage space and supports fast search. We introduced the ciphertext deduplication into PAEKS to reduce storage space and leveraged the inverted index to accelerate the retrieval process. We simulated the proposed scheme and several related schemes with a desktop computer. The experiment results show that our proposed scheme is of lower storage overhead and higher retrieval efficiency compared with related schemes.
Lung and colon cancer detection with convolutional neural networks on histopathological images

Lung cancer is deadly cancer same as colon cancer, both of them can grow simultaneously. Most researchers conduct research to detect one disease on one single body organ. So, in this study, we proposed a computer-aided diagnosing system using the Convolutional Neural Network (CNN) to detect lung and colon cancer tissues on the LC25000 dataset. The LC25000 dataset contains 25000 histopathological color image samples of colon and lung tissues which indicated cancer (adenocarcinoma) or not. In this study, we used three pre-trained CNN models, which are ShuffleNet V2, GoogLeNet, and ResNet18 also one simple customized CNN model. From the evaluation metrics tables given in this study, the highest accuracy to classify lung cancer was gained by ResNet18 with 98.82% accuracy but the shortest training time gained by ShuffleNet V2 with 1749.5 seconds. ShuffleNet V2 was the best model used to classify colon data, it gives 99.87% accuracy with the fastest training times 1202.3 seconds. The customized CNN model proposed by us get 93.02% accuracy to classify lung cancer and 88.26% accuracy for colon cancer. The proposed CNN model also gained the shortest training time which was better than GoogLeNet and ResNet18.
Sub-Mbps key-rate continuous-variable quantum key distribution with local local oscillator over 100-km fiber

We experimentally demonstrated a sub-Mbps key rate Gaussian-modulated coherent-state (GMCS) continuous-variable quantum key distribution (CV-QKD) over a 100-km transmission distance. To efficiently control the excess noise, the quantum signal and the pilot tone are co-transmitted in the fiber channel based on wideband frequency and polarization multiplexing methods. Furthermore, a high-accuracy data-assisted time domain equalization algorithm is carefully designed to compensate the phase noise and polarization variation in low signal-to-noise ratio. The asymptotic secure key rate (SKR) of the demonstrated CV-QKD is experimentally calculated to be 7.55 Mbps, 1.87 Mbps, and 0.51 Mbps over a transmission distance of 50 km, 75 km, and 100 km, respectively. The experimentally demonstrated that the CV-QKD system significantly improves the transmission distance and SKR compared to the state-of-art GMCS CV-QKD experimental results, and shows the potential for long-distance and high-speed secure quantum key distribution.
Data Quality Detection Mechanism Against Label Flipping Attacks in Federated Learning

Federated learning (FL) is an emerging framework that enables massive clients (e.g., mobile devices or enterprises) to collaboratively construct a global model without sharing their local data. However, due to the lack of direct access to clients’ data, the global model is vulnerable to be attacked by malicious clients with their poisoned data. Many strategies have been proposed to mitigate the threat of label flipping attacks, but they either require considerable computational overhead, or lack robustness, and some even cause privacy concerns. In this paper, we propose Malicious Clients Detection Federated Learning (MCDFL) to defense against the label flipping attack. It can identify malicious clients by recovering a distribution over a latent feature space to detect the data quality of each client. We demonstrate the effectiveness of our proposed strategy on two benchmark datasets, i.e., CIFAR-10 and Fashion-MNIST, by considering different neural network models and different attack scenarios. The results show that, our solution is robust to detect malicious clients without excessive costs under various conditions, where the proportion of malicious clients is in the range of 5% and 40%.
Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images

As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects.
Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution

Blockchain, smart contracts, and the Internet of Things (IoT) are essential technologies for the business process re-engineering of supply chains in the era of Industry 4.0. The agricultural food supply chain is one of the research areas where these disruptive technologies can play a crucial role in automating business processes, providing real-time goods monitoring, and securing transactions. With the help of blockchain, smart contracts, and the IoT, a product’s health and environment can be monitored throughout the supply chain. In this study, we have critically examined the relevance of these technologies through various activities of the agriculture supply chain using the approach of Business Process Modeling (BPM). The blockchain and smart contracts-based findings of the BPM were then incorporated along various layers of the Reference Architecture for Modeling Industry 4.0 (RAMI 4.0). This enabled us to introduce the IoT, blockchain, and smart contract based smart agriculture framework, Agri-4-All. Agri-4-All can be used to automate the intra-organizational and inter-organizational processes of the agricultural supply chain. We developed, deployed, and tested the intra-organizational and inter-organizational smart contracts written in the Solidity language for a typical scenario related to the agriculture supply chain. Our hybrid smart algorithms implemented using Ganache and Truffle suite reduce the gas cost in our proposed intra-organizational smart contracts by 13.89 times as compared to the traditional smart contract-based model.
Gaze-aware hand gesture recognition for intelligent construction

The advances in construction robotics in recent decades has been a powerful driver of construction automation. User-friendly interfaces to support human–robot work collaboration are critical for increasing adoption of construction robots. Among different interfaces, hand gesture is an effective and reliable interaction cue in the noisy construction environment. This paper proposes a novel human gaze-aware hand gesture recognition framework as a human–robot interface for intelligent construction. The proposed framework relies on an eye tracker to visually detect and track robotic machines in the first-person view. Then, the machine-of-interest is determined based on the bounding boxes of machines and gaze points. Finally, a hand gesture recognition architecture is incorporated with the machine information for conveying messages to the machine-of-interest. This approach was tested through a framework validation test and achieved precision and recall of 93.8% and 95.0%, respectively. A pilot study was conducted to demonstrate interaction with a robotic excavator and a dump truck, and the results illustrated that the proposed framework could serve as an effective interface for one-to-many human–robot collaborations in construction.
Towards practical and massively parallel quantum computing emulation for quantum chemistry

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, the quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein-ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOP/s is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry.
Two-photon driven Kerr quantum oscillator with multiple spectral degeneracies

Kerr nonlinear oscillators driven by a two-photon process are promising systems to encode quantum information and to ensure a hardware-efficient scaling towards fault-tolerant quantum computation. In this paper, we show that an extra control parameter, the detuning of the two-photon drive with respect to the oscillator resonance, plays a crucial role in the properties of the defined qubit. At specific values of this detuning, we benefit from strong symmetries in the system, leading to multiple degeneracies in the spectrum of the effective confinement Hamiltonian. Overall, these degeneracies lead to a stronger suppression of bit-flip errors. We also study the combination of such Hamiltonian confinement with colored dissipation to suppress leakage outside of the bosonic code space. We show that the additional degeneracies allow us to perform fast and high-fidelity gates while preserving a strong suppression of bit-flip errors.
Risk analysis in decentralized finance (DeFi): a fuzzy-AHP approach

Decentralized finance is disrupting the financial ecosystem through innovative, transparent, and interoperable financial solutions. Based on distributed ledger technology, decentralized finance is a nascent and rapidly evolving area. Decentralized finance protocols are witnessing a perfect storm (in terms of growth). However, this emerging area needs sober consideration as these financial technologies possess unique risks for users, makers, regulators, and other stakeholders. The current research aims to identify and prioritize risks in decentralized finance. The present study conducted an extensive survey of the literature to identify various risks involved in decentralized finance. For empirical analysis, the study collected data from 90 experts. A fuzzy analytical hierarchical process (F-AHP) was applied to prioritize various risks in decentralized finance. Pairwise comparison and weights of all the criteria and sub-criteria revealed that technical risks are the most significant ones, followed by legal, regulatory, and financial risks. Among the sub-risks, financial risks are at the highest level, followed by smart contract risks and transaction risks. The outcomes of this research have several implications for regulators, policymakers, entrepreneurs, technologists, and practitioners. These stakeholders can focus on these vulnerabilities and offer more sustained solutions in the future.
Sentiment analysis on images using convolutional neural networks based Inception-V3 transfer learning approach

Sentiment analysis has become a precious tool for businesses because it can be used in so many ways: to find out what customers think about products and services, to build customer relationships and loyalty, to improve customer service, and to use emotional marketing. Over the last several years, developing an end-to-end image sentiment analysis approach has significantly emphasized transfer learning methods. Deep learning algorithms have been proven to achieve remarkable outcomes across a broad spectrum of applications. Examining feelings conveyed by images is complex, but there is much space for development. A technique known as Inception-v3 that can readily focus on large portions of the body, such as a person's face, offers a significant advantage compared to the work that was done in the past. This study makes use of Inception-v3, which is a well-known deep convolutional neural network, in addition to extra deep characteristics, to increase the performance of image categorization. A CNN-based Inception-v3 architecture is employed for emotion detection and classification. The datasets CK+, FER2013, and JAFFE are used in this process. The findings are also compared with various well-known machine learning approaches, and the results obtained by the suggested model are superior. The research indicates that the proposed method can reach an accuracy level of 99.5%. The proposed approach can be used in many business applications such as Information Management, Sales, Marketing, User Interaction, Healthcare, Education, Finance, Public Monitoring, Digital PR, etc.
Hourly forecasting of the photovoltaic electricity at any latitude using a network of artificial neural networks

Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to tackle the problem of climate change and the energy crisis. Artificial intelligence is currently used in different science fields for its great potential and accuracy in forecasting problems. In this work, a network of artificial neural networks (ANNs) was trained and validated to forecast the hourly worldwide electrical power produced by various PV modules, with different electrical characteristics. Each ANN describes the worldwide performance of each PV module on the optimal inclination angle. The training data consists of the hourly air temperature, horizontal total solar radiation as input data and electrical power produced as output. The power is obtained from the hourly simulation of PV modules with an electrical circuit model in 24 localities at very different latitudes. The validation and generalization of the network were obtained by considering the six PV modules in further 24 localities and by considering two further PV modules in all 48 localities considered. The excellent results in terms of accuracy metrics confirmed that the network of ANNs is a reliable, simple and accurate tool that can be used to predict the hourly performance of any PV module in any location worldwide.
Supporting the implementation of AI in business communication: the role of knowledge management

Purpose Although the use of artificial intelligence (AI) has been estimated to be up to 56% in the last decade, the adoption rate of AI for communication activities is still low. The lack of in-depth literature on the subject, and the limited sharing of the experience gained by companies, limits the creation, dissemination and consolidation of understanding in this area. The purpose of this study is to solve the problem of the absence of knowledge, identification, skills development and introduction of an innovation (such as the AI in the business communication) in the company, with the principles of knowledge management (KM). Design/methodology/approach This study adopts the perspective of KM to provide guidelines for the definition of standards and facilitate the introduction of AI in business communication. Findings To the best of the authors’ knowledge, this study is the first to relate the perspectives of business communication, AI and KM, activating a virtuous circle between KM and AI. At the centre of the proposed model are people, processes and technologies, based on which KM represents the ideal perspective to define the implementation of AI. This is primarily from the perspective of augmented intelligence, owing to the inability of AI to completely replace humans in the business communication processes, as it lacks, among others, emotional intelligence. Research limitations/implications This study finds in KM a possible strategy to solve the problems faced so far in applying AI in business communication processes, providing a model capable of transforming and adapting itself to the context, thanks to the open approach. Originality/value This study contributes to the literature by linking the introduction of emerging technology (AI) in a specific process, such as business communication, from the KM perspective.
Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions

Most transfer learning-based methods require sufficient data for training, but the target data may not be available. Also, the health prognosis of target data under unknown conditions is a challenging online few-shot issue, which is still not effectively addressed. In addition, the limited knowledge learned from a single source domain may further limit the extraction of degradation features. To address these challenges, a multi-source adversarial online regression (MAOR) method considering the pseudo domain extension is proposed to predict the remaining useful life of bearings under online unknown conditions. It can obtain a target data stream for each round and perform an online learning task. Specifically, when generating pseudo-domains, the domain-level adaptation is designed by considering the heterogeneous distribution between pseudo-domains and the similarity of manifold between pseudo and source domains. Also, the feature-level adaptation is embedded in a multi-source adversarial adaptation architecture to learn robust domain-invariant features and build the offline model. An offline-online prediction framework is developed to predict online target data streams and update the online model with adaptive weighting. To validate the superiority of the proposed MAOR, two bearing cases are extensively evaluated. The experiment results show that MAOR can achieve significant outcomes in different online tasks with competitive performance.
Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO).As the name suggests, ALO deploys two strategies to find the optimal solution.The central concept of ALO is to intensify guided searches as a required method.Then, a random search is performed only if the guided search fails to improve the current solution.ALO consists of four guided searches and one random search, performed in three phases: two mandatory and one optional.In the first phase, the guided search is conducted with the best global solution as the reference.In the second phase, the guided search is conducted with a randomly selected solution as the reference.The random search is performed in the third phase.Evaluating ALO, it was tested on 23 classic functions and benchmarked against five existing metaheuristics with known shortcomings: Mixed leader-based optimizer (MLBO), slime mould algorithm (SMA), golden search optimizer (GSO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA).The results indicate that ALO is highly competitive, outperforming MLBO, SMA, GSO, COA, and ZOA in solving 16, 16, 14, 10, and 9 functions respectively, and demonstrating ALO as a promising new metaheuristic.