Reviewerly/exHarmony huggingface dataset:
Solving constrained portfolio optimization model using stochastic fractal search approach

Purpose Optimum utilization of investments has always been considered one of the most crucial aspects of capital markets. Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk. In this series, a population-based evolutionary approach, stochastic fractal search (SFS), is derived from the natural growth phenomenon. This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints. Design/methodology/approach This paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints. SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory. Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm, particle swarm optimization, simulated annealing and differential evolution. The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225, DAX 100, FTSE 100, Hang Seng31 and S&amp;P 100 have been taken in the study. Findings The study confirms the better performance of the SFS model among its peers. Also, statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis. Originality/value In the recent past, researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach. However, this is the first attempt to apply the SFS optimization approach to the problem.
Solar power time series forecasting utilising wavelet coefficients

Accurate and reliable prediction of Photovoltaic (PV) power output is critical to electricity grid stability and power dispatching capabilities. However, Photovoltaic (PV) power generation is highly volatile and unstable due to different reasons. The Wavelet Transform (WT) has been utilised in time series applications, such as Photovoltaic (PV) power prediction, to model the stochastic volatility and reduce prediction errors. Yet the existing Wavelet Transform (WT) approach has a limitation in terms of time complexity. It requires reconstructing the decomposed components and modelling them separately and thus needs more time for reconstruction, model configuration and training. The aim of this study is to improve the efficiency of applying Wavelet Transform (WT) by proposing a new method that uses a single simplified model. Given a time series and its Wavelet Transform (WT) coefficients, it trains one model with the coefficients as features and the original time series as labels. This eliminates the need for component reconstruction and training numerous models. This work contributes to the day-ahead aggregated solar Photovoltaic (PV) power time series prediction problem by proposing and comprehensively evaluating a new approach of employing WT. The proposed approach is evaluated using 17 months of aggregated solar Photovoltaic (PV) power data from two real-world datasets. The evaluation includes the use of a variety of prediction models, including Linear Regression, Random Forest, Support Vector Regression, and Convolutional Neural Networks. The results indicate that using a coefficients-based strategy can give predictions that are comparable to those obtained using the components-based approach while requiring fewer models and less computational time.
Human and Object Detection using Machine Learning Algorithm

Films with abandoned objects may be identified and traced using this paper’s technique. Especially in high-traffic locations like railway stations and airports, unattended baggage poses a severe security risk. By using the power of deep learning, people and their belongings may be accurately recognized. Each photograph is accompanied by a training video that comprises more than 18,000 people and their baggage (such as backpacks and purses). The YOLOv3 model is used, which has a real-time accuracy of 98 percent. Determine who owns something and if it has been abandoned. People and their luggage may be studied using an approach that takes into account their location and travel patterns. 65.66% of the time, abandoned properties and their owners are correctly identified 65.10% of the time, ownership is also correctly identified.
Dynamic Multi-Scale Network for Dual-Pixel Images Defocus Deblurring with Transformer

Recent works achieve excellent results in defocus deblurring task based on dual-pixel data using convolutional neural network (CNN), while the scarcity of data limits the exploration and attempt of vision transformer in this task. In addition, the existing works use fixed parameters and network architecture to deblur images with different distribution and content information, which also affects the generalization ability of the model. In this paper, we propose a dynamic multi-scale network, named DMTNet, for dual-pixel images defocus deblurring. DMTNet mainly contains two modules: feature extraction module and reconstruction module. The feature extraction module is composed of several vision transformer blocks, which uses its powerful feature extraction capability to obtain richer features and improve the robustness of the model. The reconstruction module is composed of several Dynamic Multi-scale Sub-reconstruction Module (DMSSRM). DMSSRM can restore images by adaptively assigning weights to features from different scales according to the blur distribution and content information of the input images. DMTNet combines the advantages of transformer and CNN, in which the vision transformer improves the performance ceiling of CNN, and the inductive bias of CNN enables transformer to extract more robust features without relying on a large amount of data. DMTNet might be the first attempt to use vision transformer to restore the blurring images to clarity. By combining with CNN, the vision transformer may achieve better performance on small datasets. Experimental results on the popular benchmarks demonstrate that our DMTNet significantly outperforms state-of-the-art methods.
An Oriented SAR Ship Detector With Mixed Convolution Channel Attention Module and Geometric Nonmaximum Suppression

Benefiting from deep learning, synthetic aperture radar (SAR) ship detection based on convolutional neural network (CNN) has developed rapidly and corresponding performance is getting better. Nevertheless, most of the existing methods still cannot achieve a good balance between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> in scenes with complex background interferences, or in a scene where two or more ships dock side by side. To address these problems, this article proposes a novel oriented SAR ship detector, which uses oriented bounding boxes (OBBs) to describe ships. For the purpose of reducing missed ships (aiming to improve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> ) while suppressing false alarms (aiming to maintain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision</i> ), the proposed detector embeds a mixed convolution channel attention (MCCA) module into the backbone network, which highlights the important feature channels to enhance ship representation features by reweighting all channels of the feature map. In addition, we consider the geometric position relationship of neighbor ships and propose geometric nonmaximum suppression (G-NMS) to remove the redundant ship candidates or possible false alarms. Extensive experiments conducted on the SSDD and HRSID <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{s}$</tex-math></inline-formula> datasets demonstrate the effectiveness of MCCA and G-NMS, the proposed detector also achieves better performance compared to state-of-the-art OBB-based detectors.
Gigapixel Whole-Slide Images Classification Using Locally Supervised Learning

Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tools for processing WSIs is challenging due to their enormous sizes. Currently, to deal with this issue, conventional methods rely on a multiple instance learning (MIL) strategy to process a WSI at patch level. Although effective, such methods are computationally expensive, because tiling a WSI into patches takes time and does not explore the spatial relations between these tiles. To tackle these limitations, we propose a locally supervised learning framework which processes the entire slide by exploring the entire local and global information that it contains. This framework divides a pre-trained network into several modules and optimizes each module locally using an auxiliary model. We also introduce a random feature reconstruction unit (RFR) to preserve distinguishing features during training and improve the performance of our method by 1% to 3%. Extensive experiments on three publicly available WSI datasets: TCGA-NSCLC, TCGA-RCC and LKS, highlight the superiority of our method on different classification tasks. Our method outperforms the state-of-the-art MIL methods by 2% to 5% in accuracy, while being 7 to 10 times faster. Additionally, when dividing it into eight modules, our method requires as little as 20% of the total gpu memory required by end-to-end training. Our code is available at https://github.com/cvlab-stonybrook/local_learning_wsi.
T-former: An Efficient Transformer for Image Inpainting

Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of CNNs (e.g. local prior, spatially shared parameters) limit the performance in the face of broken images with diverse and complex forms. Recently, a class of attention-based network architectures, called transformer, has shown significant performance on natural language processing fields and high-level vision tasks. Compared with CNNs, attention operators are better at long-range modeling and have dynamic weights, but their computational complexity is quadratic in spatial resolution, and thus less suitable for applications involving higher resolution images, such as image inpainting. In this paper, we design a novel attention linearly related to the resolution according to Taylor expansion. And based on this attention, a network called T-former is designed for image inpainting. Experiments on several benchmark datasets demonstrate that our proposed method achieves state-of-the-art accuracy while maintaining a relatively low number of parameters and computational complexity.
Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method

Abstract Social media platforms significantly increase general information about disease severity and inform preventive measures among community members. To identify public opinion through tweets on the subject of Covid-19 and investigate public sentiment in the country over the period. This article proposed a novel method for sentiment analysis of coronavirus-related tweets using bidirectional encoder representations from transformers (BERT) bi-directional long short-term memory (Bi-LSTM) ensemble learning model. The proposed approach consists of two stages. In the first stage, the BERT model gains the domain knowledge with Covid-19 data and fine-tunes with sentiment word dictionary. The second stage is the Bi-LSTM model, which is used to process the data in a bi-directional way with context sequence dependency preserving to process the data and classify the sentiment. Finally, the ensemble technique combines both models to classify the sentiment into positive and negative categories. The result obtained by the proposed method is better than the state-of-the-art methods. Moreover, the proposed model efficiently understands the public opinion on the Twitter platform, which can aid in formulating, monitoring and regulating public health policies during a pandemic.
Numerical gate synthesis for quantum heuristics on bosonic quantum processors

There is a recent surge of interest and insights regarding the interplay of quantum optimal control and variational quantum algorithms. We study the framework in the context of qudits which are, for instance, definable as controllable electromagnetic modes of a superconducting cavity system coupled to a transmon. By employing recent quantum optimal control approaches described in (Petersson and Garcia, 2021), we showcase control of single-qudit operations up to eight states, and two-qutrit operations, mapped respectively onto a single mode and two modes of the resonator. We discuss the results of numerical pulse engineering on the closed system for parametrized gates useful to implement Quantum Approximate Optimization Algorithm (QAOA) for qudits. The results show that high fidelity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>&gt;</mml:mo></mml:math> 0.99) is achievable with sufficient computational effort for most cases under study, and extensions to multiple modes and open, noisy systems are possible. The tailored pulses can be stored and used as calibrated primitives for a future compiler in circuit quantum electrodynamics (cQED) systems.
Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications. Clinical data structures, patient cohorts, and clinical protocols may be highly biased among hospitals such that sampling of representative learning datasets to learn ML models remains a challenge. As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this study, we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of the two datasets, resulting in an upper bound of the prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e., from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals' datasets and with a high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold-standard datasets that are large and diverse with data from varied application sites.
Towards effective and efficient online exam systems using deep learning-based cheating detection approach

With the high growth of digitization and globalization, online exam systems continue to gain popularity and stretch, especially in the case of spreading infections like a pandemic. Cheating detection in online exam systems is a significant and necessary task to maintain the integrity of the exam and give unbiased, fair results. Currently, online exam systems use vision-based traditional machine learning (ML) methods and provide examiners with tools to detect cheating throughout the exam. However, conventional ML methods depend on handcrafted features and cannot learn the hierarchical representations of objects from data itself, affecting the efficiency and effectiveness of such systems. The proposed research aims to develop an effective and efficient approach for online exam systems that uses deep learning models for real-time cheating detection from recorded video frames and speech. The developed approach includes three essential modules, which constantly estimate the critical behavior of the candidate student. These modules are the front camera-based cheating detection module, the back camera-based cheating detection module, and the speech-based detection module. It can classify and detect whether the candidate is cheating during the exam by automatically extracting useful features from visual images and speech through deep convolutional neural networks (CNNs) and the Gaussian-based discrete Fourier transform (DFT) statistical method. We evaluate our system using a public dataset containing recorded audio and video data samples collected from different subjects carrying out several types of cheating in online exams. These collected data samples are used to obtain the experimental results and demonstrate the proposed work's efficiency and effectiveness.
Democracy, epistemic agency, and AI: political epistemology in times of artificial intelligence

Abstract Democratic theories assume that citizens have some form of political knowledge in order to vote for representatives or to directly engage in democratic deliberation and participation. However, apart from widespread attention to the phenomenon of fake news and misinformation, less attention has been paid to how they are supposed to acquire that knowledge in contexts shaped by artificial intelligence and related digital technologies. While this topic can also be approached from an empirical angle, this paper contributes to supporting concerns about AI and democracy by looking at the issue through the lens of political epistemology, in particular using the concept of epistemic agency. It argues that artificial intelligence (AI) endangers democracy since it risks to diminish the epistemic agency of citizens and thereby undermine the relevant kind of political agency in democracy. It shows that next to fake news and manipulation by means of AI analysis of big data, epistemic bubbles and the defaulting of statistical knowledge endanger the epistemic agency of citizens when they form and wish to revise their political beliefs. AI risks to undermine trust in one’s own epistemic capacities and hinder the exercise of those capacities. If we want to protect the knowledge basis of our democracies, we must address these problems in education and technology policy.
Artificial Intelligence Combined with Deep Learning in Film and Television Quality Education for the Youth

Today, with the rapid development of science and technology, the value of education is more and more valued by people, but the research and development of quality education in film and television are still relatively traditional. The objective is to explore the application and effect of the combination of artificial intelligence and deep learning algorithm in the quality education of youth film and television. In view of the current problems in the quality education of film and television, this work innovatively introduces the augmented reality (AR) technology, and applies artificial intelligence and AR technology to the quality education of film and television for young people. At the same time, a deep learning algorithm is introduced to build a youth film and television quality education system based on artificial intelligence combined with deep learning, and further empirical analysis of the system is carried out in the form of a questionnaire survey. The questionnaire survey shows that, from the perspective of various dimensions of learning attitude, the two groups of learners had significant differences in emotional experience and self-cognition ([Formula: see text]), and more than 90% of the teenagers thought the system resource interface was beautiful; from the perspective of the perceived usefulness of the system, 68.24% of the teenagers believe that the system is easy to operate and useful, and as many as 82.82% of the teenagers believe that the system improves their interest in learning about quality education courses in film and television. Therefore, it is found that the constructed system can improve the learning interest of young people in the quality education course of film and television and make their learning attitude more positive, providing experimental reference for the latter research in the field of youth education.
Hoax News Detection Analysis using IndoBERT Deep Learning Methodology

Today's massive interaction on the social media platform results in a lot of information circulating, making the news abundant and easily accessible. We also witness much contradictory news fabricated according to the content creator agenda. This phenomenon makes it more likely that people get hoax news. The public is asked to be more vigilant and not easily trust the circulated information. The existence of hoax news can damage a company's reputation that has long been built. This research aims to detect hoax news using IndoBERT, Support Vector Machine, and Naïve Bayes by classifying news in Indonesian and finding the best model. The collected data includes various topics on news sites such as Detik, Liputan 6, Kompas, Cek Fakta, and Turnbackhoax. A total of 2000 news contained 1000 hoax news data and 1000 non-hoax news data. The best accuracy is the IndoBERT algorithm, with a value of 90%. We analyze this result and find that IndoBERT had the best performance detecting hoaxes by detecting contextual aspects related to more complex patterns.
A Live Extensible Ontology of Quality Factors for Textual Requirements

Quality factors like passive voice or sentence length are commonly used in research and practice to evaluate the quality of natural language requirements since they indicate defects in requirements artifacts that potentially propagate to later stages in the development life cycle. However, as a research community, we still lack a holistic perspective on quality factors. This inhibits not only a comprehensive understanding of the existing body of knowledge but also the effective use and evolution of these factors. To this end, we propose an ontology of quality factors for textual requirements, which includes (1) a structure framing quality factors and related elements and (2) a central repository and web interface making these factors publicly accessible and usable. We contribute the first version of both by applying a rigorous ontology development method to 105 eligible primary studies and construct a first version of the repository and interface. We illustrate the usability of the ontology and invite fellow researchers to a joint community effort to complete and maintain this knowledge repository. We envision our ontology to reflect the community’s harmonized perception of requirements quality factors, guide reporting of new quality factors, and provide central access to the current body of knowledge.