Reviewerly/exHarmony huggingface dataset:
Deployment of a deep-learning based multi-view stereo approach for measurement of ship shell plates

In this work, a measurement system integrated with a deep-learning based multi-view stereo (MVS) approach is developed to measure ship shell plates. Specifically, a deep learning architecture of CasMVSNet is deployed for depth map inference from multi-view images, which can remarkably decrease the dense reconstruction time and GPU memory consumption. This is the first report that a learned MVS architecture is deployed to three-dimensional (3D) reconstruction of ship shell plates. Measurement experiments are then performed for three typical hull plates to evaluate the accuracy, efficiency and completeness of the proposed measurement method. The results suggest that the complete point cloud data of the curved hull plates can be reconstructed in about 3 min with the average of errors less than 1 mm, which fulfills the requirements of precision and efficiency in shipbuilding production. Compared with traditional wooden templates, the proposed measurement method is more accurate, efficient and inexpensive. The developed measurement system with quantitative data can also be readily integrated with the 3D computer numerical control (CNC) plate bending machine. Moreover, the robustness and flexibility of the proposed measurement method have been verified by comparison with the measurement method based on active binocular stereovision.
RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

The Russian language is still not as well-resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this article, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with moderate inter-rater agreement into five classes: Positive, Neutral, Negative, Speech Act, and Skip. As a source of data, we used Twitter Stream Grab, a historical collection of tweets obtained from the general Twitter API stream, which provides a 1% sample of the public tweets. Additionally, we released a RuBERT-based sentiment classification model that achieved F1 = 0.6594 on the test subset.
On Proof-of-Accuracy Consensus Protocols

Consensus protocols are a fundamental part of any blockchain; although several protocols have been in operation for several years, they still have drawbacks. For instance, some may be susceptible to a 51% attack, also known as a majority attack, which may suppose a high risk to the trustworthiness of the blockchains. Although this attack is theoretically possible, executing it in practice is often regarded as arduous because of the premise that, with sufficiently active members, it is not ’straightforward’ to have much computing power. Since it represents a possible vulnerability, the community has made efforts to solve this and other blockchain problems, which has resulted in the birth of alternative consensus protocols, e.g., the proof of accuracy protocol. This paper presents a detailed proposal of a proof-of-accuracy protocol. It aims to democratize the miners’ participation within a blockchain, control the miners’ computing power, and mitigate the majority attacks.
Multi‐scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images

The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image-guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi-scale segmentation network with a cascading pyramid convolution module (CPCM) and a double-input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI.First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom-up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM.For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15 mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64 mm.The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state-of-the-art methods.
Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents while operating at low altitude. Even though many studies have been conducted to detect transmission lines, there still remains many challenges due to their thin shapes in diverse backgrounds. Moreover, most previous methods require a significant level of human involvement to generate pixel-level ground truth data. In this paper, we propose a transmission line detection algorithm based on weakly supervised learning and unpaired image-to-image translation. The proposed algorithm only requires image-level labels, and a novel attention module, which is called parallel dilated attention (PDA), improves the detection accuracy by recalibrating channel importance based on the information from various receptive fields. Finally, we construct a refinement network based on unpaired image-to-image translation in order that the prediction map is guided to detect line-shaped objects. The proposed algorithm outperforms the state-of-the-art method by 2.74% in terms of F1-score, and experimental results demonstrate that the proposed method is effective for detecting transmission lines in both quantitative and qualitative aspects.
Bingo

In this paper, we introduce Bingo, a flexible and customizable yet performant Python framework for symbolic regression with genetic programming. Bingo maintains a modular code structure for simple abstraction and easily swappable components. Fitness functions, selection methods, and constant optimization methods allow for easy problem-specific customization. Bingo also maintains several features for increased efficiency such as parallelism, equation simplification, and a C++ backend. We compare Bingo's performance to other genetic programming for symbolic regression (GPSR) methods to show that it is both competitive and flexible.
Improved data clustering using multi-trial vector-based differential evolution with gaussian crossover

In this paper, an Improved version of the Multi-Trial Vector-based Differential Evolution (IMTDE) algorithm is proposed and adapted for clustering data. The purpose here is to enhance the balance between the exploration and exploitation mechanisms in MTDE by employing Gaussian crossover and modifying the sub-population distribution between the strategies. Results show that IMTDE is superior to the compared algorithms in most 19 datasets used. The code is available here: https://github.com/parhamhadikhani/IMTDEClustering.
Evolutionary quantum architecture search for parametrized quantum circuits

Recent advancements in quantum computing have shown promising computational advantages in many problem areas. As one of those areas with increasing attention, hybrid quantum-classical machine learning systems have demonstrated the capability to solve various data-driven learning tasks. Recent works show that parameterized quantum circuits (PQCs) can be used to solve challenging reinforcement learning (RL) tasks with provable learning advantages. While existing works yield potentials of PQC-based methods, the design choices of PQC architectures and their influences on the learning tasks are generally underexplored. In this work, we introduce EQAS-PQC, an evolutionary quantum architecture search framework for PQC-based models, which uses a population-based genetic algorithm to evolve PQC architectures by exploring the search space of quantum operations. Experimental results show that our method can significantly improve the performance of hybrid quantum-classical models in solving benchmark reinforcement problems. We also model the probability distributions of quantum operations in top-performing architectures to identify essential design choices that are critical to the performance.
Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.
Untangling phylogenetic diversity's role in evolutionary computation using a suite of diagnostic fitness landscapes

Diversity is associated with success in evolutionary algorithms. To date, diversity in evolutionary computation research has mostly been measured by counting the number of distinct candidate solutions in the population at a given time point. Here, we aim to investigate the value of phylogenetic diversity, which takes into account the evolutionary history of a population. To understand how informative phylogenetic diversity is, we run experiments on a set of diagnostic fitness landscapes under a range of different selection schemes. We find that phylogenetic diversity can be more predictive of future success than traditional diversity metrics under some conditions, particularly for fitness landscapes with a single, challenging-to-find global optimum. Our results suggest that phylogenetic diversity metrics should be more widely incorporated into research on diversity in evolutionary computation.
A novel quantum-inspired evolutionary computation-based quantum circuit synthesis for various universal gate libraries

The quantum circuit is the prime factor to realize quantum computation. This paper proposes a novel quantum-inspired evolutionary computation method to synthesize quantum circuits effectively and efficiently. Recently, the Clifford+T gate library has become popular, as the T gate has a better fault-tolerant and error correction efficacy, which is essential to near-term quantum computation. The proposed quantum-inspired evolutionary computation is more flexible in constructing diverse solutions than the classical synthesis method. The entangled local search mechanism further enhances the ability to discover the optimal solutions on an efficient frontier. The encoding can be slightly changed to achieve various gate libraries circuit synthesis in the quantum or reversible Boolean circuits. The experiments demonstrate that our proposed method can be more effective in composing a compact quantum circuit than the state-of-the-art method. Meanwhile, the T-depth can attain an optimal value without ancilla bits. Furthermore, we provide an illustration to construct the function equivalent quantum circuit with the NOT, CNOT, Controlled-Square-Root-of-NOT quantum gate library (NCV). We also conduct exhaustion to prove that our synthesized circuit is an optimal solution, requiring far fewer resources in time and evaluation than exhaustion.
Exact and sequential penalty weights in quadratic unconstrained binary optimisation with a digital annealer

Quadratic unconstrained binary optimisation (QUBO) problems is a general class of combinatorial optimisation problems, which regained popularity after recent advances in quantum computing. Quantum-inspired technologies like Fujitsu's Digital Annealer (DA), based on simulated annealing, can solve QUBO problems much faster than traditional computers. Penalty methods can convert constrained optimisation problems into QUBO problems. However, existing exact methods of determining penalty weights are limited to specific QUBO problems and require manual analysis by experts in QUBO. Empirical methods are general but become computationally prohibitive for large problem sizes and do not guarantee that penalty weights preserve the feasibility of global optima. We present a simple, efficient, general method applicable to any QUBO to determine exact penalty weights. Such weights are simple upper-bounds of the smallest penalty weight guaranteeing that unconstrained global optima are the same as the feasible global optima. These bounds can be iteratively improved by sequential penalty methods which we also present. Experimental results with the DA on minimum cut, travelling salesman and multi-dimensional knapsack problems show the viability of the novel methodology hybridising exact and sequential methods. This work contributes towards general, automatic and scalable penalty methods in QUBO.
Quantum parametric circuit optimization with estimation of distribution algorithms

Variational quantum algorithms (VQAs) offer some promising characteristics for carrying out optimization tasks in noisy intermediate-scale quantum devices. These algorithms aim to minimize a cost function by optimizing the parameters of a quantum parametric circuit. Thus, the overall performance of these algorithms, heavily depends on the classical optimizer which sets the parameters. In the last years, some gradient-based and gradient-free approaches have been applied to optimize the parameters of the quantum circuit. In this work, we follow the second approach and propose the use of estimation of distribution algorithms for the parameter optimization in a specific case of VQAs, the quantum approximate optimization algorithm. Our results show an statistically significant improvement of the cost function minimization compared to traditional optimizers.
AutoQubo

This paper presents a general method to derive automatically a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of, in principle, any combinatorial optimisation problem from its high-level description given in any programming language in a free format. Currently ad-hoc approaches to formulating a problem at a time as a QUBO are the norm. The proposed method goes beyond the state-of-the-art as it is general in its applicability. The method is data-driven and based on sampling, but the obtained QUBO is exact rather than only an approximation of the original problem for optimisation problems occurring in practice. The method is of particular interest in the context of Quantum Ising Machines - quantum computers specialised to optimisation tasks. It can be understood as a compiler that makes these machines usable without specific expertise by automating the translation of high-level description of combinatorial optimisation problems to a low-level format suitable for the quantum hardware.
Rethinking class orders and transferability in class incremental learning

Class Incremental Learning (CIL), an indispensable ability for open-world applications such as service robots, has received increasing attention in recent years. Although many CIL methods sprouted out, researchers usually adopt default class orders, leaving the characteristics of different class orders less visited. In this paper, we rethink class orders in CIL from the following aspects: first, we show from preliminary studies that class orders do have an impact on the performance, and mainstream episodic memory-based CIL methods generally favor an interleaved way of arranging class orders; then, we interpret the phenomena above with transferability and propose transferability measures of class orders, which are in line with the method performance under different class orders; based on that, we propose a Class Order Search Algorithm (COSA) to obtain an optimal class order by finding which one has almost the highest transferability. Experiments on Group ImageNet and iNaturalist verify the importance of class orders in CIL methods, and demonstrate the effectiveness of our proposed transferability measures and COSA. These findings may help raise more attention to the hardly visited class orders in CIL.
Peer privacy protection motivation and action on social networking sites: Privacy self-efficacy and information security as moderators

The protection motivation theory was adopted in this study to explore the effects of severity and susceptibility of the perceived threat of peer disclosure on privacy protection actions on social networking sites. Severity and susceptibility of a perceived threat were second-order formative constructs, measured by three reflective components: privacy disclosure, informal sanction, and online flaming. A set of relational factors were introduced as the antecedents, including interdependence, network convergence, depth, predictability, and commitment, which may facilitate social networking site user perceptions of the potential risk of information disclosure, in turn evoking protection motivation in their peers. In addition, in this study, two coping appraisal factors, privacy self-efficacy and information security awareness, are also considered as moderators in the research model. 740 valid questionnaires were collected from social networking site users and tested using partial least squares structural equation modeling techniques. The results showed that interdependence and network convergence have a positive effect on severity and susceptibility, respectively, and predictability and commitment have positive effects on both the severity of and susceptibility to perceived threats. The severity of a perceived threat and susceptibility to a perceived threat positively affect privacy protection behavior. Therefore, SNS companies should design several interactive mechanisms to help SNS users build online relationships with each other in order to enhance their consciousness of information security. Also, these companies should also educate beginners who lack privacy self-efficacy about how serious security threats will be and should also help experienced users determine how likely such threats will be to drive them to take privacy protection measures.
A New Architecture of 8K VR FOV Video End-to-End Technology

With the rapid development of virtual reality (VR) technology and the increasing demand for higher video quality, streaming videos imposes stringent requirements on the network. To solve these problems, the Field of View (FOV) streaming is one the popular solutions. In streaming aspect, it's common to provide video service over Internet Protocol Television (IPTV) platform. The application of 8K VR FOV in IPTV platform can reuse the existing Content Delivery Network (CDN) architecture and set-top boxes (STBs), thus significantly reducing the complexity of the system. Furthermore, it can also accelerate the popularization of VR technology, and improve the service development of IPTV platform. Currently, most of the VR FOV solutions in industry are based on Dynamic Adaptive Streaming over HTTP (DASH), which are difficult to apply in most IPTV platforms. Our Real Time Streaming Protocol (RTSP) 8K VR FOV video end-to-end solution for IPTV platforms significantly reduces bandwidth by about 60% compared to the traditional solution, and provides the same subjective quality. We evaluate and compare the performance of our solution with traditional 8K solution. The results show the superiority of the proposed architecture in terms of bandwidth, latency and CPU utilization.
Secure Federated Learning for IoT using DRL-based Trust Mechanism

Federated learning (FL) has evolved to leverage a distributed dataset from numerous IoT devices to improve the performance of a Machine Learning (ML) model while preserving the privacy of device data. Client devices train a global model jointly and share local model updates with a central entity or a server. However, FL is vulnerable to a variety of adversarial attacks that target its security and privacy and lead to compromising the main FL task. In particular, devices can contribute unreliable local model updates due to poisoning attack, or unintentionally due to their limited resources. Therefore, identifying trustworthy and reliable devices to participate in FL task is a key security challenge. In this paper, we propose a reputation management mechanism based on Deep Reinforcement Learning (DRL) in order to optimize the selection and evaluation of reliable devices and improve the accuracy of the FL model. The experimental results show that the proposed DRL-based reputation management scheme can enhance the FL accuracy by 20% while requiring fewer training iterations when compared to conventional reputation-based methods.
Towards CBDC-supported Smart Contracts for Industrial Stakeholders

The stakeholders of complex industrial-financial ecosystems and supply chains are interconnected in many ways, at many organisational and technical levels. Even in order to deliver a product or exchange various materials, various Information and Operational Technology solutions are required, etching the way towards the Industrial Internet of Things. Moreover, integrating the necessary legal and financial co-operations in the big picture is similarly important, where blockchains and Smart Contracts aim to provide the means. Nevertheless, new solutions should thrive not to disrupt current economic landscapes, but slowly transform and enhance instead. This article discusses solutions for supply chain trade use cases with over-the-counter derivatives, and argues the same in a world where Central Bank Digital Currency (CBDC) governs financial markets. It also provisions Arrowhead framework and Smart Contract based solutions for the issues there, such as matching bids, handling counterparty risks or physical settlements.