Reviewerly/exHarmony huggingface dataset:
Improved Fitness-Dependent Optimizer Algorithm

The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019.An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems.To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position.Moreover, in determining the weights, the FDO uses the weight factor (wf ), which is zero in most cases and one in only a few cases.Conversely, the IFDO performs wf randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved.In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated.Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results.Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results.The results show that IFDO is practical in some cases, and its results are improved in most cases.Finally, to prove the practicability of the IFDO, it is used in real-world applications.
Bayesian Local Sampling-Based Planning

Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness. However, most schemes are often inefficient as the samples drawn from the global proposal distribution, and do not exploit relevant local structures. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution. The proposal distribution is sequentially updated based on previous samples, consequently shaping it according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages. We provide the formulation of a sample-efficient distribution, along with theoretical foundation of sequentially updating this distribution. We demonstrate experimentally that by using a Bayesian proposal distribution, a solution is found faster, requiring fewer samples, and without any noticeable performance overhead.
A mobile data offloading framework based on a combination of blockchain and virtual voting

Summary The emergence of mobile cloud computing enables mobile users to offload computation tasks to other resource‐rich mobile devices to reduce energy consumption and enhance performance. A direct peer‐to‐peer connection among mobile devices to offload computation tasks can be a highly promising solution to provide a fast mechanism, especially for deadline‐sensitive offloading tasks. The generic blockchain‐based system might fail in such a scenario due to it being a heavyweight mechanism requiring high power consumption in the mining process. To address these issues, in this article, we propose a directed acyclic graph‐enabled mobile offloading (DAGMO) algorithm. DAGMO model is empowered by traditional blockchain features and provides additional advantages to overcome the fundamental limitations of generic blockchain. A game‐theoretic approach is used to model the interactions between mobile devices. The numerical analysis proves the proposed model to enhance the overall welfare of the participating nodes in terms of computation cost and time.
A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging

A convolutional neural network has the capacity to learn multiple representation levels and abstraction in order to provide a better understanding of image data. In addition, a good multi-level representation of data typically results in a better generalisation capability. This fact emphasises the importance of concentrating on the regularity information of training data in order to improve generalisation. However, the training data contain erroneous information owing to noise and outliers. In this paper, we propose a new regularisation approach for convolutional neural networks with better generalisation properties. Specifically, the weights of the convolution layers are perturbed by additive noise in each learning iteration. The approach provides a better model for prediction, as shown by the experimental results on a number of medical benchmark data sets. Furthermore, the effectiveness and accuracy of the proposed convolutional neural network are demonstrated by comparing with several recent perturbation techniques.
Compressive Privacy Generative Adversarial Network

Machine learning as a service (MLaaS) has brought much convenience to our daily lives recently. However, the fact that the service is provided through cloud raises privacy leakage issues. In this work we propose the compressive privacy generative adversarial network (CPGAN), a data-driven adversarial learning framework for generating compressing representations that retain utility comparable to state-of-the-art, with the additional feature of defending against reconstruction attack. This is achieved by applying adversarial learning scheme to the design of compression network (privatizer), whose utility/privacy performances are evaluated by the utility classifier and the adversary reconstructor, respectively. Experimental results demonstrate that CPGAN achieves better utility/privacy trade-off in comparison with the previous work, and is applicable to real-world large datasets.
Feature-extraction methods for historical manuscript dating based on writing style development

Paleographers and philologists perform significant research in finding the dates of ancient manuscripts to understand the historical contexts. To estimate these dates, the traditional process of using classical paleography is subjective, tedious, and often time-consuming. An automatic system based on pattern recognition techniques that infers these dates would be a valuable tool for scholars. In this study, the development of handwriting styles over time in the Dead Sea Scrolls, a collection of ancient manuscripts, is used to create a model that predicts the date of a query manuscript. In order to extract the handwriting styles, several dedicated feature-extraction techniques have been explored. Additionally, a self-organizing time map is used as a codebook. Support vector regression is used to estimate a date based on the feature vector of a manuscript. The date estimation from grapheme-based technique outperforms other feature-extraction techniques in identifying the chronological style development of handwriting in this study of the Dead Sea Scrolls.
A New Bonobo optimizer (BO) for Real-Parameter optimization

This paper presents a new metaheuristic optimization algorithm, namely Bonobo optimizer (BO). It is inspired from the social behaviour and reproductive strategies of Bonobos. Bonobos adopt fission-fusion social strategy, which is nothing but forming several groups of variable sizes and compositions within a community and after a short period of time, they again reunite with their own community. Furthermore, bonobos show mainly four types of reproductive strategies, such as promiscuous, restrictive, consortship and extra-group mating. These natural behaviours of bonobos are artificially modelled in the proposed algorithm to solve optimization problems. The novelty of the proposed BO lies with the updating mechanisms of searching agents and their associated parameters, and the selection method of the mating partners. The performance of the proposed BO has been examined on a set of twenty optimization problems with varying attributes, and the results are compared with the reported ones by using seven other metaheuristic algorithms, in the literature. The outcome of the experiment clearly shows the superior performance of the proposed BO in both the aspects of exploration and exploitation capabilities compared to that of the others. The source code of BO is available at: https://sites.google.com/site/softcomputinglaboratory/Home.
PRI Modulation Recognition Based on Squeeze-and-Excitation Networks

Pulse repetition interval (PRI) modulation recognition is a significant means to analyze radar working statuses and missions in Electronic Support system. Traditional methods may be insufficient to accurately recognize complex PRI at low SNR with high percentages of missing pulses. In this letter, an approach based on Squeeze-and-Excitation networks (SE-Net) and the autocorrelation functions for PRI modulation recognition automatically is proposed. Firstly, the features of six PRI modulation types in the autocorrelation domain are converted into images by calculating instantaneous autocorrelation functions. Then, the images will be the input of SE-Net which will automatically learn about deep features of different PRI modulation modes. Finally, SE-Net will output PRI modulation modes directly. Simulation results show that SE-Net is robust to the noise and missing pulses. The accuracies for all PRI modulation modes are more than 95% at -10dB with 30% missing pulses and more than 96% at -2dB with 50% missing pulses. Compared with traditional method and other networks, SE-Net can achieve better recognition performance at low SNR and high percentages of missing pulses.
Wavelength Division Multiplexing of 194 Continuous Variable Quantum Key Distribution Channels

We demonstrate the first continuous variable quantum key distribution (CV-QKD) system with fully digital phase and polarization tracking, enabling the first wavelength division multiplexing experiment for CV-QKD. We transmit 194 × 0.5 GHz channels with 25 GHz channel spacing, demonstrating an aggregate estimated asymptotic secret key rate of 172.6 Mbit/s over 25 km.
Sensitivity analysis of control parameters in particle swarm optimization

Particle Swarm Optimization (PSO) is a powerful nature-inspired metaheuristic optimization method that may determine optimal solutions of engineering problems in fewer evaluations compared to other optimization methods. However, the literature shows that PSO may suffer from converging prematurely to a local solution, and this occurs due to poor tuning of the control parameters in PSO. In this paper, an extensive parametric sensitivity analysis was conducted to understand the impact of the individual control parameters and their respective influence on the performance of PSO. A benchmark constrained optimization problem was considered for studying PSO by modifying each parameter one-at-a-time. Therefore, initially, a constraint handling technique was formulated to allow particles to update their best historical solutions according to the feasibility. Results of the sensitivity analysis revealed that PSO was most sensitive to the inertia weight, cognitive component, and social component. The optimal parameter set, determined from the sensitivity analysis, was verified by comparison with metaheuristic methods. The verification study shows that the proposed parameter setting outperformed the other methods in all but one case, where it performed competently.
Aiding Intra-Text Representations with Visual Context for Multimodal Named Entity Recognition

With the massive explosion of social media platforms such as Twitter and Instagram, people everyday share billions of multimedia posts, containing images and text. Typically, text in these posts is short, informal and noisy, leading to ambiguities which can be resolved using images. In this paper we will explore text-centric Named Entity Recognition task on these multimedia posts. We propose an end to end model which learns a joint representation of a text and an image. Our model extends multi-dimensional self-attention technique, where now image helps to enhance relationship between words. Experiments show that our model is capable of capturing both textual and visual contexts with greater accuracy, achieving state-of-the-art results on Twitter multimodal Named Entity Recognition dataset.
Reinforcement Learning with Attention that Works: A Self-Supervised Approach

Attention models have had a significant positive impact on deep learning across a range of tasks. However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. Unlike the selective attention models used in previous attempts, which constrain the attention via preconceived notions of importance, our implementation utilises the Markovian properties inherent in the state input. We propose the first combination of self attention and reinforcement learning that is capable of producing significant improvements, including new state of the art results in the Arcade Learning Environment.
K-Bucket Based Raft-Like Consensus Algorithm for Permissioned Blockchain

With the development of blockchain, more and more blockchain types emerge: public blockchain, consortium blockchain and private blockchain. Because of the node trust in some consortium blockchain and private blockchain, a no byzantine fault tolerance algorithm KRaft(Kademlia-Raft) algorithm with high throughput and high scalability is proposed. KRaft consensus algorithm is a Raft-like consensus algorithm that preserves the logic of part of Raft consensus algorithm. It optimized leader election and consensus process of the Raft consensus algorithm through the established K-Bucket node relationships in the Kademlia protocol, improved leader election speed and throughput. Firstly, the KRaft algorithm uses the K-bucket established by Kademlia protocol to achieve stable and efficient leader election process for the candidate node split vote problem and the low voting efficiency caused by the increase of the Follower node in the Raft algorithm. Secondly, aiming at the low efficiency and load imbalance of the leader single-node log replication in the Raft algorithm consensus process, a parallel log replication scheme with multiple candidate nodes for balancing the leader node load is proposed to improve the throughput and the scalability of the algorithm. Finally, as a Raft-like consensus algorithm, KRaft consensus algorithm satisfied the safety and liveness requirements of Raft consensus algorithm. KRaft consensus algorithm and Raft consensus algorithm were evaluated with local cluster simulation. The experimental results show that the KRaft consensus algorithm has a 41% improvement in transaction throughput and has a 67% improvement in the leader election speed.
Machine Intelligence in Cardiovascular Medicine

The computer science technology trend called artificial intelligence (AI) is not new. Both machine learning and deep learning AI applications have recently begun to impact cardiovascular medicine. Scientists working in the AI domain have long recognized the importance of data quality and provenance to AI algorithm efficiency and accuracy. A diverse array of cardiovascular raw data sources of variable quality—electronic medical records, radiological picture archiving and communication systems, laboratory results, omics, etc.—are available to train AI algorithms for predictive modeling of clinical outcomes (in-hospital mortality, acute coronary syndrome risk stratification, etc.), accelerated image interpretation (edge detection, tissue characterization, etc.) and enhanced phenotyping of heterogeneous conditions (heart failure with preserved ejection fraction, hypertension, etc.). A number of software as medical device narrow AI products for cardiac arrhythmia characterization and advanced image deconvolution are now Food and Drug Administration approved, and many others are in the pipeline. Present and future health professionals using AI-infused analytics and wearable devices have 3 critical roles to play in their informed development and ethical application in practice: (1) medical domain experts providing clinical context to computer and data scientists, (2) data stewards assuring the quality, relevance and provenance of data inputs, and (3) real-time and post-hoc interpreters of AI black box solutions and recommendations to patients. The next wave of so-called contextual adaption AI technologies will more closely approximate human decision-making, potentially augmenting cardiologists’ real-time performance in emergency rooms, catheterization laboratories, imaging suites, and clinics. However, before such higher order AI technologies are adopted in the clinical setting and by healthcare systems, regulatory agencies, and industry must jointly develop robust AI standards of practice and transparent technology insertion rule sets.
Activities and costs of re-engineering cloned variants into an integrated platform

Many software systems need to exist in multiple variants. Organizations typically develop variants using clone&own---copying and adapting systems towards new requirements. However, while clone & own is a simple and readily available strategy, it does not scale with the number of variants, and then requires a costly reengineering of the cloned variants into a configurable software platform (a.k.a., software product line). Ideally, organizations could rely on decision models or at least on substantial empirical data to assess the costs and benefits of such a re-engineering. Unfortunately, despite decades of research on product lines and platforms, such data is scarce, not least because obtaining it from industrial reengineering efforts is challenging. We address this gap with a study on re-engineering two cases of cloned variants of open-source Android and Java games. Student developers re-engineered the clones into software product lines, logging their activities and costs. They performed the types of activities typically associated with re-engineering, but the activities were intertwined and done iteratively. The costs were relatively similar among both cases, but the used variability mechanism had a substantial impact. Interestingly, beyond a common diffing tool, no dedicated re-engineering tool was particularly useful. We hope that our results support researchers working on re-engineering techniques and decision models, as well as practitioners trying to assess the costs and activities involved in re-engineering a software platform.
A superposition based multiple-image encryption using Fresnel-Domain high dimension chaotic phase encoding

A new superposition based multiple-image encryption using Fresnel-domain high dimension chaotic phase encoding is proposed. In this method, each color image of an authenticated user is decomposed into R, G, and B channels. The amplitude of each channel is independently normalized, phase encoded and then modulated by corresponding high-dimension chaotic random phase mask (HDCPM). The modulated R, G, and B channels of multiple color images are individually added and Fresnel transformed to produce corresponding encrypted channels. All the parameters (initial values and control parameters) of HDCPM are exploited as remarkably sensitive decryption keys of the proposed system. Moreover, the update, management, and transmission of the decryption keys are convenient. The individual decryption keys of R, G, and B channels significantly increase the resistance to various types of attacks. The proposed cryptosystem can be executed by using a hybrid optoelectronic system. Numerical simulation results demonstrate the viability and effectiveness of the proposed scheme.
MC-Unet: Multi-scale Convolution Unet for Bladder Cancer Cell Segmentation in Phase-Contrast Microscopy Images

Owing to the high density, low contrast, deformable cell shapes, low inter-cellular shape and appearance variation, and occlusion of the cells by division or fusion especially in phase-contrast microscopy images, it is still a challenging task to segment cells from the complex background. In this work, we proposed a multi-scale convolution Unet (MC-Unet) for bladder cancer cell segmentation in Phase-Contrast microscopy images. More specifically, the second 3x3 convolution of each layer in the standard Unet is replaced with a multi-scale convolution (MC) block with different kernel sizes, such as 1x1, 3x3, and 5x5. To verify the effectiveness of the proposed method, a series of experiments are conducted on the bladder cancer T24 dataset and the MoNuSeg dataset, and the results shows the proposed MC-Unet can obtain better comprehensive performance than the standard Unet.
Robust learning with imperfect privileged information

In the learning using privileged information (LUPI) paradigm, example data cannot always be clean, while the gathered privileged information can be imperfect in practice. Here, imperfect privileged information can refer to auxiliary information that is not always accurate or perturbed by noise, or alternatively to incomplete privileged information, where privileged information is only available for part of the training data. Because of the lack of clear strategies for handling noise in example data and imperfect privileged information, existing learning using privileged information (LUPI) methods may encounter serious issues. Accordingly, in this paper, we propose a Robust SVM+ method to tackle imperfect data in LUPI. In order to make the SVM+ model robust to noise in example data and privileged information, Robust SVM+ maximizes the lower bound of the perturbations that may influence the judgement based on a rigorous theoretical analysis. Moreover, in order to deal with the incomplete privileged information, we use the available privileged information to help us in approximating the missing privileged information of training data. The optimization problem of the proposed method can be efficiently solved by employing a two-step alternating optimization strategy, based on iteratively deploying off-the-shelf quadratic programming solvers and the alternating direction method of multipliers (ADMM) technique. Comprehensive experiments on real-world datasets demonstrate the effectiveness of the proposed Robust SVM+ method in handling imperfect privileged information.
Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations

Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to eliminate redundant features optimally is challenging, especially if the parameters of these algorithms are not tuned. In this study, a particle swarm optimization is proposed both for feature selection and parameters tuning of the gradient boosting machine technique on 1,349,239 data points of an incident dataset. The predictive ability of the proposed method compared to conventional tree-based methods revealed near-perfect predictions of the proposed model on test data (classification accuracy − 0.878 and coefficient of determination − 0.93) for the two outcome variables ACCIDENT and INJURYFREQ. The high predictive power obtained reveals that injuries do not occur in a chaotic fashion, but that underlying patterns and trends exist that can be uncovered and captured via machine learning when applied to sufficiently large datasets. Also, key relationships identified will assist safety managers to understand possible risk combinations that cause accidents; helping to trigger proactive risk mitigation plans.