Reviewerly/exHarmony huggingface dataset:
Efficient fireworks algorithms for dynamic optimisation problems in continuous space

Real world problems in various domains demonstrate different characteristics of changes over time. This is why several researchers have been interested in dynamic optimisation for the last two decades. Since changes occur over time in a dynamic optimisation problem, the goal of a related algorithm becomes tracking the changing optima over time. Evolutionary algorithms and various swarm intelligence techniques have been adapted in the literature to solve dynamic optimisation problems. The Fireworks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for global optimisation of complex static functions that simulates the explosion process of fireworks. Although a set of improvements over the conventional FWA are presented in the literature for the static optimisation problems, the most evident extension is the Enhanced Fireworks Algorithm (EFWA). In this paper, cost effective extensions of the EFWA are proposed for solving dynamic optimisation problems in continuous space. The performance evaluation of our EFWA-based algorithms is validated with the Moving Peaks Benchmark. Empirical studies on different benchmark instances clearly show the applicability of our extensions. Our EFWA-based extensions outperform the related work in terms of both quality of solution and computational cost for a large set of test instances of the benchmark.
Better Fixed-Point Filtering with Averaging Trees

Production imaging pipelines commonly operate using fixed-point arithmetic, and within these pipelines a core primitive is convolution by small filters - taking; convex combinations of fixed-point values in order to resample, interpolate, or denoise. We describe a new way to compute unbiased convex combinations of fixed-point values using sequences of averaging instructions, which exist on all popular CPU and DSP architectures but are seldom used. For a variety of popular kernels, our averaging; trees have higher performance and higher quality than existing standard practice.
Automated recognition of glomerular lesions in the kidneys of mice by using deep learning

In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as much as in clinical settings. We have been performing automated recognition of various pathological animal tissues and quantitative analysis of pathological findings, including liver and lung. In this study, we attempted to construct an artificial intelligence (AI)-based trained model that can automatedly recognize glomerular lesions in mouse kidneys that are characterized by complex structures.By using hematoxylin and eosin (HE)-stained whole slide images (WSI) from Col4a3 KO mice as variation data, normal glomeruli and glomerular lesions were annotated, and deep learning (DL) was performed with the use of the neural network classifier DenseNet system in HALO AI. The trained model was refined by correcting the annotation of misrecognized tissue area and reperforming DL. The accuracy of the trained model was confirmed by comparing the AI-obtained results with the pathological grades evaluated by pathologists. The generality of the trained model was also confirmed by analyzing the WSI of adriamycin (ADR)-induced nephropathy mice, which is a different disease model.Glomerular lesions (including mesangial proliferation, crescent formation, and sclerosis) observed in Col4a3 KO mice and ADR mice were detected by our trained model. The number of glomerular lesions detected by our trained model were also highly correlated with that of counted by pathologists.In this study, we constructed a trained model allowing us to automatedly recognize glomerular lesions in the mouse kidney with the use of the HALO AI system. The findings and insights of this study will facilitate the development of digital pathology in non-clinical research and improve the probability of success in drug discovery research.
Efficient certificateless public integrity auditing of cloud data with designated verifier for batch audit

In the cloud storage environment, scholars have proposed data integrity auditing schemes to ensure the integrity of data in the cloud. In some scenarios, users may need to designate an auditor to perform auditing tasks, however, most existing cloud auditing schemes are not applicable. A small number of cloud auditing schemes with designated verifiers use PKI cryptography and identity-based cryptography, which has complex certificate management and key escrow problems. Therefore, based on the certificateless signature algorithm of the specified verifier, we construct an efficient certificateless provable data possession mechanism in the cloud with a designated verifier called CL-DV-PDP. Based on of meeting the above user's requirement, our scheme can also perform a batch audit of multi-user data at the same time. We use a dynamic hash table to dynamically update the cloud data and the scheme also supports privacy protection. We define the security model of the scheme in detail, and the security of the scheme is proved based on the CDH assumption under the random oracle model. In the efficiency analysis section, we compare the functions and computational costs of our scheme with the relevant references, the result shows that the functions of the scheme are comprehensive and our scheme is efficient.
Decentralized crowdsourcing for human intelligence tasks with efficient on-chain cost

Crowdsourcing for Human Intelligence Tasks (HIT) has been widely used to crowdsource human knowledge, such as image annotation for machine learning. We use a public blockchain to play the role of traditional centralized HIT systems, such that the blockchain deals with cryptocurrency payments and acts as a trustworthy judge to resolve disputes between a worker and a requester in a decentralized setting, preventing false-reporting and free-riding. Our approach neither uses expensive cryptographic tools, such as zero-knowledge proofs, nor sends the worker's answers to the blockchain. Compared with prior works, our approach significantly reduces on-chain cost: it only requires O(1) on-chain storage and O(log N ) smart contract computation, where N is the question number of a HIT. Additionally, our approach uses known answers or gold standards to determine the worker's answer quality. To motivate the requester to use honest known answers, the requester cannot learn the worker's answers if the answer quality does not meet the requirement. We further provide formal security definitions for our decentralized HIT and prove security of our construction.
A Coarse-to-Fine Multi-class Object Detection in Drone Images Using Convolutional Neural Networks

Abstract Multi-class object detection has a rapid evolution in the last few years with the rise of deep Convolutional Neural Networks (CNNs) learning based, in particular. However, the success approaches are based on high resolution ground level images and extremely large volume of data as in COCO and VOC datasets. On the other hand, the availability of the drones has been increased in the last few years and hence several new applications have been established. One of such is understanding drone footage by analysing, detecting, recognizing different objects in the covered area. In this study conducted, a collection of large images captured by a drone flying at a fixed altitude in a desert area located within the United Arab Emirates (UAE) is given and it is utilised for training and evaluating the CNN networks to be investigated. Three state-of-the-art CNN architectures, namely SSD-500 with VGGNet-16 meta-architecture, SSD-500 with ResNet meta-architecture and YOLO-V3 with Darknet-53 are optimally configured, re-trained, tested and evaluated for the detection of three different classes of objects in the captured footage, namely, palm trees, group-of-animals/cattle and animal sheds in farms. Our preliminary experiments revealed that YOLO-V3 outperformed SSD-500 with VGGNet-16 by a large margin and has a considerable improvement as compared to using SSD-500 with ResNet. Therefore, it has been selected for further investigation, aiming to propose an efficient coarse-to-fine object detection model for multi-class object detection in drone images. To this end, the impact of changing the activation function of the hidden units and the pooling type in the pooling layer has been investigated in detail. In addition, the impact of tuning the learning rate and the selection of the most effective optimization method for general hyper-parameters tuning is also investigated. The result demonstrated that the multi-class object detector developed has precision of 0.99, a recall of 0.94 and an F-score of 0.96, proving the efficiency of the multi-class object detection network developed.
Secure and High-Quality Watermarking Algorithms for Relational Database Based on Semantic

Relational databases are widely applied in various industries, such as government departments, medical institutions and enterprises, for data storage and relationship management. It is convenience to data maintenance, however, the data in it maybe vulnerable to be forged or tampered with. Therefore, protecting the copyright of relational databases is a critical issue. Fortunately, watermarking technology can be used to prove copyright ownership. Since the digital watermarking technology can solve this problem effectively, based on semantic, we propose two watermarking approaches with high security and strong robustness for numeric and non-numeric data of relational databases in this paper. On the one hand, we propose a reversible numeric watermarking approach. It performs the replacement operation at the semantic level and retain the statistical characteristics of the data. On the other hand, based on word segmentation and word embedding, the non-numerical attributes of the relational database with natural language are chosen to embed watermark. It is noteworthy that the proposed mechanism can be applied to both Chinese and English with minimum distortion. Additionally, we also propose the virtual splitting of attribute column and the principle of modification minimum to guarantee the capacity of watermark and reduce the data modification rate. Additionally, the BCH (31,16,7) error control code is added to the binary watermark string to improve the detection rate of watermark. Based on the above innovations, the security of watermarking algorithm is improved successfully by double encryption (chaos encryption and hash encryption) with two keys (the user&#x0027;s private key and the attribute column key). The simulation results demonstrate that the proposed two algorithms in this paper have stronger robustness on defensing malicious attacks than previous works.
The Impact of Big Data Analytics and Forensic Audit in Fraud Detection

The high number of fraud cases that occur, causes a large amount of losses to be borne by the company as well as investors and creditors.Based on this, we are looking for an effective method to detect fraud.This study aims to analyze the effect of big data analytics on forensic audits and fraud detection.In addition, this study was conducted with the aim of analyzing the effect of forensic auditing as a mediating variable on fraud detection.This research was conducted using a quantitative approach with a survey method by distributing questionnaires.The respondents of this study are auditors who work in public accounting firms.Statistical testing in this study is in the form of Structural Equation Modeling (SEM) using the smart PLS 3 application.The results of this study indicate that big data analytics has a positive effect on forensic audits.This research also proves that big data and forensic audit each have a positive effect on fraud detection.In addition, this study also proves that forensic audit moderates/strengthens the effect of big data analytics on fraud detection.
Data Analysis Using NLP to Sense Human Emotions Through Chatbot

Years of Technological progress have made machines capable of understanding basic human emotions from their tonality. Written text is also an important method of communication where a wide range of emotions can be expressed. Thus, it is imperative for machines to be also able to understand the emotions being portrayed in the text messages. For this study, an analysis was done on a dataset known as GoEmotions for the potential development of a chatbot. This dataset contains 58 thousand selected Reddit comments collected from various subreddits that use the English language. The results shows that, these were then classified and categorized based on the principal preserved component analysis (PPCA) method into four main emotions; positive, negative, ambiguous, and neutral successfully. And for this task, Natural Language Processing (NLP) pre-training was applied, which was a transformer based machine learning technique.
Recognition of Handwritten Gujarati Conjuncts Using the Convolutional Neural Network Architectures: AlexNet, GoogLeNet, Inception V3, and ResNet50

A methodology for recognizing offline handwritten Gujarati conjuncts has been introduced in the proposed article. This article includes 767 types of frequently utilized conjuncts. The convolutional neural network (CNN) architectures AlexNet, GoogLeNet, Inception V3, as well as ResNet50, are used to recognize handwritten Gujarati conjuncts. The performance of these CNN architectures is systematically evaluated. Gujarati conjuncts are required to segment from the appropriate place before recognition. Conjuncts are segmented into preceding components and succeeding components before recognition. These preceding components and succeeding components can be then individually recognized by the system. 19694 sample images of preceding components of the conjuncts and 28050 samples images of the succeeding components of the conjuncts are utilized for the proposed research work. Maximum accuracy of 93.41% for the preceding components and maximum accuracy of 89.01% for the succeeding components have been achieved by using GoogLeNet.
Learning Fashion Compatibility With Context Conditioning Embedding

Fashion compatibility predictions have obtained a lot of attention recently. Mining the compatibility between fashion items in an outfit is different from learning the visual similarity, since this relationship is more delicate. Decomposing the outfit compatibility into pairwise item matching is a popular way to treat the problem. However, in most existing methods, the items are matched without considering the context, i.e, the remaining items in the outfit. Recent efforts have been made to learn the underlying high order relationships among items by treating the outfit as a whole. These models could be sensitive to the properties of different datasets, and the item representations in these models are not as compact as those in the pairwise models. In this paper, we propose a context conditioning embedding approach to learn compact representations that preserve the shared information among items under the existence of contextual items. We use two different spaces, the general and the contextual spaces, to embed items, where the representation in the contextual space contains information from the context. We employ mutual information maximization for model learning, which is shown to be more appropriate for the problem. With extensive experiments, we show that our model achieves superior performance than other state-of-the-art methods.
Augmented Reality In Science Learning For Elementary School Students

This study is motivated by the students' poor understanding when studying science.This research aims to improve the understanding and motivation of students studying science while learning.The method used in this study is an experimental method for 40 elementary school students in Skalame 1 in the Leles district of Garut Regency, West Java.Research results show that augmented reality in scientific teaching materials can improve the understanding and learning motivation of elementary school students.Augmented reality has a positive effect on the student's enthusiasm for science learning, as the learning skills are so much fun that the student does not get bored while studying the material.
Comparative Analysis on Joint Modeling of Emotion and Abuse Detection in Bangla Language

Emotions are not linguistic entities, although they are easily articulated through language. Emotions influence our actions, ideas, and, of course, how we communicate. On the other hand, abusive text, such as undiscriminating slang, offensive language, and vulgarity, is more than just a message; it is a tool for very serious and brutal cyber violence. Hence, detection of such language has become very important in any language now-a-days. Therefore, many works and researches have been done on detecting emotional language, abusive language or both in many dialects including Bangla. This paper proposes to present a comparative analysis of different researches made on detecting emotional and abusive Bangla language. It further aims to present the best approach that tailors certain attributes of emotional and abusive language detection with respect to their prognosis performance and their implementation toughness in Bangla lingo. Potential enhancements for future study are presented in the paper, while the limitations of current researches are addressed and discussed. This work seeks to bring a fresh viewpoint to the joint modeling of emotional and abusive language detection in Bangla by examining and criticizing flaws and in order to offer future changes, poor design choices must be examined.
Factors driving the exposure to information sources: Pakistani farmers' information behavior

The purpose of the present study is to examine the factors behind the selection and exposure of information sources by Pakistani farmers. This study is based on a quantitative research approach that involved survey design for the collection of data. The population of the present investigation was the farmers who have involved in any type of farming in District Okara province Punjab, Pakistan. A convenient sampling technique was used to select a sample from the different three tehsils of District Okara. A survey questionnaire was employed to collect data. Results proved that characteristics of information i.e. accessibility, usefulness, and credibility have a significant impact on the frequency of information sources/channels exposure, however, convenience proved an insignificant factor to farmers’ information sources exposure. Results further confirm that farming experience has a negative association with information exposure among Pakistani farmers. Since the usefulness and credibility of information sources trigger their use among farmers, therefore, farmers’ societies, agents, and government officials should work on developing credible content and highlighting the usefulness of different available information sources and channels.
Universal MATLAB‐based square‐root solutions in the family of continuous‐discrete Gaussian filters for state estimation in nonlinear stochastic dynamic systems

Abstract This article concerns with the issue of square‐rooting in continuous‐discrete Gaussian filters intended for state estimation in nonlinear stochastic dynamic systems of continuous‐discrete sort. These cover all methods devised within the quadrature, cubature, and unscented Kalman filtering approaches as well as those that can be constructed in the future. Based on the universal moment differential equations developed by Särkkä and Sarmavuori in 2013, we advance further that study and design two square‐root solutions grounded on MATLAB ODE solvers in the mentioned Gaussian filtering framework. The main problem addressed is a potential negativity of some weights utilized in calculations of the predicted and filtering means and covariances, which precludes from orthogonal square‐rooting schemes to be applied. In practice, such square‐root implementations are often requested because of their exceptional numerical robustness to round‐off and other disturbances. These also preserve the symmetry and positivity of the covariances computed, automatically. Here, we employ the recently‐devised ‐orthogonal square‐rooting technique for designing our universal MATLAB‐based square‐root solutions in the realm of quadrature, cubature, and unscented Kalman filters, which are easily adjusted to any particular method by using its weights and deterministically selected samples exploited in calculations of the sampled means and covariances. Such a ‐orthogonal square‐rooting approach is grounded on hyperbolic factorizations. It leads to two novel algorithms covering any continuous‐discrete Gaussian filter of the quadrature, cubature, or unscented Kalman‐like kind. Practical performances of our square‐root solutions are validated, assessed, and compared within two simulated ill‐conditioned scenarios in aeronautical and chemical engineering.
A Positive-Unlabeled Generative Adversarial Network for Super-Resolution Image Reconstruction Using a Charbonnier Loss

Recently, the generative adversarial network (GAN) has been widely used to obtain the real high-frequency details of images. This spurs the application of GAN in super-resolution reconstruction. However, GAN is unstable in the training process, for the following two reasons: Firstly, the discriminator in GAN keeps the positive (true) and negative (false) criteria of the generated samples unchanged throughout the learning process, without considering the gradual quality improvement of the generated samples (Sometimes, the generated samples are even more realistic than the real samples). To solve the above problems, this paper proposes a super-resolution model based on positive-unlabeled (PU)-GAN-Charbon (SRPUGAN-Charbon). The proposed model includes one generator network that synthetizes super-resolution images and one discriminator network trained to distinguish super-resolution images from real high-resolution images. In addition, the Charbonnier loss function was called to handle the outliers in super-resolution images, and retain the low-frequency features of super-resolution images. Extensive experiments were conducted on three benchmark databases, including BSDS500, Set5, and Set14. The results show that the proposed SRPUGAN-Charbon method is superior to the most advanced methods in terms of visual effect, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).
An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution, and an inefficient task scheduling algorithm results in under-or over-utilization of the resources, which in turn leads to degradation of the services.Therefore, in the proposed work, load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper, we propose an adaptive genetic algorithm-based load balancing (GALB)aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan, performance improvement ratio, and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.