Reviewerly/exHarmony huggingface dataset:
An application of Barnacle Mating Optimizer in Infectious Disease Prediction: A Dengue Outbreak Cases

Meta-heuristic algorithms have been significantly applied in addressing various real-world prediction problem, including in disease prediction. Having a reliable disease prediction model benefits many parties in providing proper preparation for prevention purposes. Hence, the number of cases can be reduced. In this study, a relatively new meta-heuristic algorithm namely Barnacle Mating Optimizer (BMO) is proposed for short term dengue outbreak prediction. The BMO prediction model is realized over real dengue cases data recorded in weekly frequency from Malaysia. In addition, meteorological data sets were also been employed as input. For evaluation purposes, error analysis relative to Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Deviation (MAD) were employed to validate the performance of the identified algorithms which includes the comparison between BMO against Moth Flame Optimizer (MFO) and Grey Wolf Optimizer (GWO) algorithms. Upon simulation, the superiority is in favour to BMO by producing lower error rates.
Effective writing style transfer via combinatorial paraphrasing

Abstract Stylometry can be used to profile or deanonymize authors against their will based on writing style. Style transfer provides a defence. Current techniques typically use either encoder-decoder architectures or rule-based algorithms. Crucially, style transfer must reliably retain original semantic content to be actually deployable. We conduct a multifaceted evaluation of three state-of-the-art encoder-decoder style transfer techniques, and show that all fail at semantic retainment. In particular, they do not produce appropriate paraphrases, but only retain original content in the trivial case of exactly reproducing the text. To mitigate this problem we propose ParChoice: a technique based on the combinatorial application of multiple paraphrasing algorithms . ParChoice strongly outperforms the encoder-decoder baselines in semantic retainment. Additionally, compared to baselines that achieve nonnegligible semantic retainment, ParChoice has superior style transfer performance. We also apply ParChoice to multi-author style imitation (not considered by prior work), where we achieve up to 75% imitation success among five authors. Furthermore, when compared to two state-of-the-art rule-based style transfer techniques, ParChoice has markedly better semantic retainment. Combining ParChoice with the best performing rulebased baseline (Mutant-X [34]) also reaches the highest style transfer success on the Brennan-Greenstadt and Extended-Brennan-Greenstadt corpora, with much less impact on original meaning than when using the rulebased baseline techniques alone. Finally, we highlight a critical problem that afflicts all current style transfer techniques: the adversary can use the same technique for thwarting style transfer via adversarial training . We show that adding randomness to style transfer helps to mitigate the effectiveness of adversarial training.
On the Source Code Structure of Quantum Code: Insights from Q# and QDK

A considerable number of high-level quantum programming languages have been proposed and implemented in the last years. This fact opens the possibility to study the structure of the source code of quantum software, using initially the same metrics typically used in classical software. Here we report a preliminary study in module structure and use of quantum gates in the libraries of Microsoft’s quantum development platform QDK (Quantum Developer Kit) that uses a specific language, Q#. The structure of dependencies and the use of primitives is analyzed across all the source code available in the Github repositories related to the platform to date.
Object Detection Using Multi-Scale Balanced Sampling

Detecting small objects and objects with large scale variants are always challenging for deep learning based object detection approaches. Many efforts have been made to solve these problems such as adopting more effective network structures, image features, loss functions, etc. However, for both small objects detection and detecting objects with various scale in single image, the first thing should be solve is the matching mechanism between anchor boxes and ground-truths. In this paper, an approach based on multi-scale balanced sampling(MB-RPN) is proposed for the difficult matching of small objects and detecting multi-scale objects. According to the scale of the anchor boxes, different positive and negative sample IOU discriminate thresholds are adopted to improve the probability of matching the small object area with the anchor boxes so that more small object samples are included in the training process. Moreover, the balanced sampling method is proposed for the collected samples, the samples are further divided and uniform sampling to ensure the diversity of samples in training process. Several datasets are adopted to evaluate the MB-RPN, the experimental results show that compare with the similar approach, MB-RPN improves detection performances effectively.
Automobile Insurance Fraud Detection using the Evidential Reasoning Approach and Data-Driven Inferential Modelling

Automobile insurance fraud detection has become critically important for reducing the costs of insurance companies. The majority of insurance companies use expert knowledge to detect fraud. Experience-based knowledge are interpretable and re-usable but the simplistic way that this knowledge is used in practice, often leads to some degree of misjudgment. This paper aims to establish a unique Evidential Reasoning (ER) rule that combines independent evidence from both experience based indicators and probabilities of fraud obtained from historical data. Each piece of evidence is weighted and then combined conjunctively with the weights optimised using a maximum likelihood evidential reasoning (MAKER) framework for data-driven inferential modelling. Based on a real-world insurance claim dataset, our experimental results reveal that the proposed approach preserves the interpretability and usability of expert detection system, and anticipates the changes in fraud practices by tracking the trend of the weights of experience-based indicators. Furthermore, the experimental results show that the proposed approach outperforms a number of widely used machine learning models, such as logistic regression and random forests.
Exploring Brazilian Photovoltaic Solar Energy development scenarios using the Fuzzy Cognitive Map Wizard Tool

Photovoltaic Solar Energy (PSE) sector has gained great attention during the last decades due to its significant role in the transition to sustainable energy systems. As a viable energy option, PSE has the potential to meet many of the challenges facing the world, along with the diminution of world’s dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. In the case of Brazil, the adoption of photovoltaic solar energy is mainly driven by the shortages and several other barriers that are met in the Brazilian energy sector. The development of the Brazilian PSE is the main concern of this study, and authors focus on the investigation of certain factors and their influence on this main outcome with the use of Fuzzy Cognitive Maps (FCMs). FCM is a well-established methodology for scenario analysis and management in diverse domains, and is based on fuzzy logic and neural networks aspects. In this paper we report particularly on the application of a new web-based software tool, called FCMWizard, which can model complex and dynamic systems, implement several hypotheses and run various scenarios, helping decision-makers and stakeholders with the policy-making and energy management process. In this context, a semi-quantitative model was designed, which comprises 10 key concepts and three plausible scenarios were further conducted. The findings of this study highlight the economic and political influence on the development of the PSE sector in Brazil.
Statistically-driven Coral Reef metaheuristic for automatic hyperparameter setting and architecture design of Convolutional Neural Networks

The adjustment of the hyperparameters and network structure of Convolutional Neural Networks (CNNs) composes an important step towards building effective, but still efficient learning models. The selection of the best configuration is a problem-dependent task that involves to explore an enormous and complex search space. Due to this reason, the use of heuristic-based search fits perfectly within this task, seeking to obtain a near to optimal solution in a complex and large exploratory space. This paper presents SCRODeep, a self-adapting algorithm based on a statistically-driven Coral Reef Optimisation algorithm (SCRO), for the selection of the most adequate CNNs architecture in a particular domain. This metaheuristic has been designed to navigate through a search space where the architecture (defining the particular set of layers, including convolutional or pooling layers), and the hyperparameters of the network (i.e. activation functions, number of units or the kernel initializer, among others) are represented, but where the connections weights and bias are inferred using typical CNNs optimisation algorithms. In contrast to other approaches, where the use of a metaheuristic implies in turn to fix a series of hyperparameters (i.e. the mutation probability in a genetic algorithm), our approach follows a self-parametrisation perspective, thus removing the necessity of fixing these values. The method has been tested in the design of CNNs for image classification, showing that SCRODeep is able to find competitive solutions, while the complexity of the architectures found is constrained.
Stego key recovery method for F5 steganography with matrix encoding

Abstract When embedding secret message into image by steganography with matrix encoding, there are still no effective methods to recover the stego key because it is difficult to statistically distinguish the stego coefficient sequences selected by true and false keys. Therefore, this paper proposes a method for recovering the stego key of a typical JPEG (Joint Photographic Experts Group) image steganography—F5 which composes of the check matrix and shuffling key. Firstly, the check matrix is recovered based on the embedding ratio estimated by quantitative steganalysis. The shuffling key is then recovered based on the distribution difference between the bit sequences extracted by the true and false shuffling keys. Additionally, the cardinality of the shuffling key space is significantly reduced by examining the extracted encoding parameter and message length. Experimental results show that the proposed method can recover the stego key accurately and efficiently, even when the existing Xu’s method fails for the high or very low embedding ratio.
Biomarkers and their applications in ecosystem research

Biomarkers are biogenic organic compounds that carry the chemical structures specific to their biological sources and survive long-term preservation in environmental and geological systems.The abundance of biomarkers may indicate the relative contribution of specific biological sources to the natural organic matter while their chemical and isotopic compositions may also inform on the transformation stage of organic matter and the environmental settings.Compared with conventional bulk analysis, biomarkers offer highly specific and sensitive tools to track the sources, transformation and dynamic changes of natural organic matter components and have therefore been widely used in ecological and biogeochemical studies in the past decades.In particular, combined with ecosystem observations and control experiments, biomarkers have shown great potentials in revealing changes in microbial activity and carbon sources, soil organic matter dynamics, stabilization mechanisms and response to global changes.The recently-developed biomarker-specific isotope analysis also exhibits a great promise in revealing ecosystem carbon and nitrogen turnover and food web structures.This review summarizes several major categories of commonly used biomarkers, their analytical methods, applications in ecosystem studies and existing pitfalls, and discusses future directions of research to provide guidance for biomarker users in ecology and environmental sciences.
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain alternative feedback, possibly at a low cost. A broad goal is to understand the usefulness of, and to design algorithms to exploit, this alternative feedback. In this paper, we consider a semi-supervised regression setting, where we obtain additional ordinal (or comparison) information for the unlabeled samples. We consider ordinal feedback of varying qualities where we have either a perfect ordering of the samples, a noisy ordering of the samples or noisy pairwise comparisons between the samples. We provide a precise quantification of the usefulness of these types of ordinal feedback in both nonparametric and linear regression, showing that in many cases it is possible to accurately estimate an underlying function with a very small labeled set, effectively \emph{escaping the curse of dimensionality}. We also present lower bounds, that establish fundamental limits for the task and show that our algorithms are optimal in a variety of settings. Finally, we present extensive experiments on new datasets that demonstrate the efficacy and practicality of our algorithms and investigate their robustness to various sources of noise and model misspecification.
A Modified Higher-Order Singular Value Decomposition Framework With Adaptive Multilinear Tensor Rank Approximation for Three-Dimensional Magnetic Resonance Rician Noise Removal

The magnetic resonance (MR) images are acknowledged to be inevitably corrupted by Rician distributed noise, which adversely affected the image quality for diagnosis purpose. However, the traditional denoising methods may recover the images from corruptions with severe loss of detailed structure and edge information, which would affect the lesion detections and diagnostic decision making. In this study, we challenged improving the Rician noise removal from three-dimensional (3D) MR volumetric data through a modified higher-order singular value decomposition (MHOSVD) method. The proposed framework of MHOSVD involved a parameterized logarithmic nonconvex penalty function for low-rank tensor approximation (LRTA) algorithm optimization to suppress the image noise in MR dataset. Reference cubes were extracted from the noisy image volume, and block matching was performed according to nonlocal similarity for a fourth-order tensor construction. Then the LRTA problem was implemented by tensor factorization approaches, and the ranks of unfolding matrices along different modes of the tensor were estimated utilizing an adaptive nonconvex low-rank method. The denoised MR images were finally restored through aggregating all recovered cubes. We investigated the proposed algorithm MHOSVD on both the synthetic and real clinic 3D MR images for Rician noise removal, and relative results demonstrated that the MHOSVD can recover images with fine structures and detailed edge preservation with heavy noise even as high as 15% of the maximum intensity. The experimental results were also compared along with several classical denoising methods; the MHOSVD exhibited a sufficient improvement in noise-removal performance at various noise conditions in terms of different measurement indices such as peak signal-to-noise ratio and structural similarity index metrics. Based upon the comparison, the proposed MHOSVD has proved a relative state-of-the-art performance with excellent detailed structure reservation.
Research of Neural Network Structural Optimization Based on Information Entropy

In the application of deep learning, the depth and width of the neural network structure have a great influence on the learning performance of the neural network. This paper focuses on structural optimization of depth and width, leveraging the information entropy model and decision tree strategy as feature selection and structural adjustment to optimize neural network candidates. Therefore, a decision tree-based heuristic optimization algorithm for neural network structural adjustment is proposed. Furthermore, the proposed approach is applied to fully-connected neural networks trained on the Iris dataset, and the proposed approach is verified effective via experimental simulation.
Social assessment of technology and humanitarization of engineering in the information society

The article considers definitions of the contemporary technology and its social and moral assessment. In the information society, humanitarization of engineering and technical education in general becomes extremely important together with the social-humanitarian knowledge in the interdisciplinary assessment of the scientific-technological development. Technology Assessment (TA) is a new scientific discipline, a theory of assessing and forecasting the development of technology, and a practice of consulting. Based on the TA, algorithms are developed to identify negative effects of technology and to make scientifically sound decisions. An interdisciplinary dialogue on the social assessment of technology should focus not only on technocratic tasks but also on the social-humanitarian methodological and epistemological foundations of the TA. In recent years, this component of the social assessment of technology has influenced the Western-European academic discourse on Responsible Research and Innovation, which reflects the scientific understanding of the importance of ethical reflection of technical activity. Thus, there is an obvious need for the combination of the social-humanitarian expertise of innovative technological projects with technical, mathematical and applied methods in the information age. Contemporary radical changes determined by the scientific-technological revolution require new approaches, methods and forms of interaction between people and communities, while their global nature determines universal ethical principles of these relationships. The post-modern information development of Russia will be accompanied not only by implementation of information technologies in all spheres of life, but also by the social-moral assessment of technology, humanization and humanitarization of engineering, strengthening personal professionalism and creative abilities.
Global/local motion planning based on Dynamic Trajectory Reconfiguration and Dynamical Systems for Autonomous Surgical Robots

This paper addresses the generation of collision-free trajectories for the autonomous execution of assistive tasks in Robotic Minimally Invasive Surgery (R-MIS). The proposed approach takes into account geometric constraints related to the desired task, like for example the direction to approach the final target and the presence of moving obstacles. The developed motion planner is structured as a two-layer architecture: a global level computes smooth spline-based trajectories that are continuously updated using virtual potential fields; a local level, exploiting Dynamical Systems based obstacle avoidance, ensures collision free connections among the spline control points. The proposed architecture is validated in a realistic surgical scenario.
Uniform Pooling for Graph Networks

The graph convolution network has received a lot of attention because it extends the convolution to non-Euclidean domains. However, the graph pooling method is still less concerned, which can learn coarse graph embedding to facilitate graph classification. Previous pooling methods were based on assigning a score to each node and then pooling only the highest-scoring nodes, which might throw away whole neighbourhoods of nodes and therefore information. Here, we proposed a novel pooling method UGPool with a new point-of-view on selecting nodes. UGPool learns node scores based on node features and uniformly pools neighboring nodes instead of top nodes in the score-space, resulting in a uniformly coarsened graph. In multiple graph classification tasks, including the protein graphs, the biological graphs and the brain connectivity graphs, we demonstrated that UGPool outperforms other graph pooling methods while maintaining high efficiency. Moreover, we also show that UGPool can be integrated with multiple graph convolution networks to effectively improve performance compared to no pooling.
Traditional village protection based on big data under the impact of COVID-19

Under the influence of novel corona virus pneumonia epidemic, the protection of traditional villages is put forward higher request. The spread of the epidemic among villages will make the situation of epidemic prevention and control more difficult. As an important part of culture, traditional villages have high historical value. In this paper, the traditional village protection method, a new geographical data algorithm IData storage method. Compared with the traditional ArcGIS method, it improves the efficiency and accuracy of topographic map entry. IData’s data factory can use the symbolic technology of skeleton lines to represent all the figures in the national standard mode, and any complex figure can only be represented by one element. Idate can quickly load data and render symbols in a drawing. With the powerful data processing engine of IData data factory, we can check out the errors that other software can’t find and process the data automatically. Records of the loss of traditional villages can be recorded quickly. The establishment and protection of traditional villages have had a beneficial impact.
Crowdsourcing Technology for Classroom Learning

Crowdsourcing technology is a revolution that connects collective intelligence of users and has transformed learning process among teachers and students to a new form of engagement and collaboration in the classroom.It enables learners to make personal learning networks to gain ideas and use the crowd to find solutions to problems.This study employed experimental, sharable content object reference model (SCORM), and descriptive research methods and conducted at the University of La Salette, Inc. (ULSI) with students, teachers, subject matter experts, personnel and IT experts as participants.Data were gathered through sets of questionnaires and interview and were analyzed through frequency and percentage, mean, and t-test for significant difference.Results revealed that the university has the readiness and sustainability for implementation of Crowdsourcing Technology for Classroom Learning (CTCL) platform indicated by the presence of ICT infrastructure, administrative and resource support.Students and teachers have the readiness to adopt the platform supported by their technological tools, confidence, attitudes and training towards a successful online learner or teacher.CTCL was designed through SCORM of Moodle platform.Considering the significance of utilizing the platform, learning becomes more effective, engaging and enhancing in the students' performance in their course.Moreover, satisfaction and usability acceptance of users on the platform met the requirements of software quality standards of ISO 25010.Therefore, adoption of CTCL in the university and its sister schools in the province of Isabela is recommended as a crowdsourced learning management system.In the future, this study can be enhanced further and work on integration of other emerging technologies such as widespread use of mobile computing which would change the instructor and learner learning systems.Researchers working on similar topic would consider integration of virtual and augmented reality technologies and offline mode of learning using mobile learning platforms for enhancement of this study