Reviewerly/exHarmony huggingface dataset:
Application of MobileNetV2 to waste classification

Today, the topic of waste separation has been raised for a long time, and some waste separation devices have been installed in large communities. However, the vast majority of domestic waste is still not properly sorted and put out, and the disposal of domestic waste still relies mostly on manual classification. The research in this paper applies deep learning to this persistent problem, which has important significance and impact. The domestic waste is classified into four categories: recyclable waste, kitchen waste, hazardous waste and other waste. The garbage classification model trained based on MobileNetV2 deep neural network can classify domestic garbage quickly and accurately, which can save a lot of labor, material and time costs. The absolute accuracy of the trained network model is 82.92%. In comparison with CNN network model, the classification accuracy of MobileNetV2 model is 15.42% higher than that of CNN model. In addition, the trained model is light enough to be better applied to mobile.
Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine

Extreme learning machine (ELM) is a type of randomized neural networks originally developed for linear classification and regression problems in the mid-2000s, and has recently been extended to computational partial differential equations (PDE). This method can yield highly accurate solutions to linear/nonlinear PDEs, but requires the last hidden layer of the neural network to be wide to achieve a high accuracy. If the last hidden layer is narrow, the accuracy of the existing ELM method will be poor, irrespective of the rest of the network configuration. In this paper we present a modified ELM method, termed HLConcELM (hidden-layer concatenated ELM), to overcome the above drawback of the conventional ELM method. The HLConcELM method can produce highly accurate solutions to linear/nonlinear PDEs when the last hidden layer of the network is narrow and when it is wide. The new method is based on a type of modified feedforward neural networks (FNN), termed HLConcFNN (hidden-layer concatenated FNN), which incorporates a logical concatenation of the hidden layers in the network and exposes all the hidden nodes to the output-layer nodes. HLConcFNNs have the interesting property that, given a network architecture, when additional hidden layers are appended to the network or when extra nodes are added to the existing hidden layers, the representation capacity of the HLConcFNN associated with the new architecture is guaranteed to be not smaller than that of the original network architecture. Here representation capacity refers to the set of all functions that can be exactly represented by the neural network of a given architecture. We present ample benchmark tests with linear/nonlinear PDEs to demonstrate the computational accuracy and performance of the HLConcELM method and the superiority of this method to the conventional ELM from previous works.
A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer Survival Prediction

Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types of cancer in women and is. Early breast cancer detection and classification are crucial. Early detection considerably increases the likelihood of survival, which motivates us to contribute to different detection techniques from a technical standpoint. Additionally, manual detection requires a lot of time and effort and carries the risk of pathologist error and inaccurate classification. To address these problems, in this study, a hybrid deep learning model that enables decision making based on data from multiple data sources is proposed and used with two different classifiers. By incorporating multi-omics data (clinical data, gene expression data, and copy number alteration data) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, the accuracy of patient survival predictions is expected to be improved relative to prediction utilizing only one modality of data. A convolutional neural network (CNN) architecture is used for feature extraction. LSTM and GRU are used as classifiers. The accuracy achieved by LSTM is 97.0%, and that achieved by GRU is 97.5, while using decision fusion (LSTM and GRU) achieves the best accuracy of 98.0%. The prediction performance assessed using various performance indicators demonstrates that our model outperforms currently used methodologies.
A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence

A cloud workflow scheduling problem is extremely challenging for its large scale and the elasticity and heterogeneity of cloud resources. In this paper, we model a cloud workflow scheduling as a multi-objective optimization problem in which makespan and energy consumption need to be simultaneously optimized. To address the problem, we propose a dynamic multiple population genetic algorithm (DMGA), in which the population is divided into three sub-populations (i.e., superior, ordinary, and inferior sub-populations). Accordingly, three sets of genetic operators are introduced. Concretely, the traditional genetic operators are selected by the ordinary sub-population. On the contrary, two types of the longest common subsequence (LCS) respectively based on superior individuals and inferior individuals are utilized to design two specific genetic operators, which are adopted by the superior sub-population and the inferior sub-population, respectively. For the superior sub-population, some superior gene blocks can be saved by the new proposed genetic operators, which is beneficial for speeding up the convergence. For the inferior sub-population, some inferior gene blocks can be destroyed based on the new genetic operators, which is favorable for helping the inferior individuals to jump out of local optima. Moreover, the dynamic multiple population framework enable an individual to perform diverse search behaviors in different generations aiming to satisfy distinct requirements of different fitness landscapes. We conducted experiments with 2 heuristic algorithms, 5 algorithms designed specifically for workflow scheduling, and 3 popular swarm intelligence algorithms. In the comparison of the five measures, DMGA outperformed the other algorithms on more than 50% of the tested dataset. The results indicate that DMGA outperforms other state-of-the-art peer algorithms on different scheduling problems, especially on large scale problems. Moreover, positive effectiveness of the new proposed strategies is also verified by a set of experiments.
Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional datasets

Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. The evaluation and suitability of these selected features are often analyzed using classifiers. These features are locked with data increasingly being generated from different sources such as social media, surveillance systems, network applications, and medical records. The high dimensionality of these datasets often impairs the quality of the optimal combination of these features selected. The use of the binary optimization method has been proposed in the literature to address this challenge. However, the underlying deficiency of the single binary optimizer is transferred to the quality of the features selected. Though hybrid methods have been proposed, most still suffer from the inherited design limitation of the single combined methods. To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. The approach used in this study designed a sub-population selective mechanism that dynamically assigns individuals to a 2-level optimization process. The level-1 method first mutates items in the population and then reassigns them to a level-2 optimizer. The selective mechanism determines what sub-population is assigned for the level-2 optimizer based on the exploration and exploitation phase of the level-1 optimizer. In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. The outcome of these are the HBEOSA-SA and HBEOSA-FFA, which are then investigated on the NT, and their corresponding variants HBEOSA-SA-NT and HBEOSA-FFA-NT with no NT applied. The hybrid methods were experimentally tested over high-dimensional datasets to address the challenge of feature selection. A comparative analysis was done on the methods to obtain performance variability with the low-dimensional datasets. Results obtained for classification accuracy for large, medium, and small-scale datasets are 0.995 using HBEOSA-FFA, 0.967 using HBEOSA-FFA-NT, and 0.953 using HBEOSA-FFA, respectively. Fitness and cost values relative to large, medium, and small-scale datasets are 0.066 and 0.934 using HBEOSA-FFA, 0.068 and 0.932 using HBEOSA-FFA, with 0.222 and 0.970 using HBEOSA-SA-NT, respectively. Findings from the study indicate that the HBEOSA-SA, HBEOSA-FFA, HBEOSA-SA-NT and HBEOSA-FFA-NT outperformed the BEOSA.
Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models

Accurate estimates of reference evapotranspiration (ET0) are of great significance to water resources planning and management, but the actual solar radiation (Rs), as the primary parameter for ET0 estimation, is difficult to obtain directly in most areas. Thus, studying the impacts of locally calibrated empirical solar radiation (Rs) models to improve the accuracy of Penman-Monteith (PM) is significant. Meanwhile, swarm intelligence algorithms have proved their potential in the domains of agriculture and hydrology, but few studies applied them in optimizing Rs models to improve ET0 estimation. This study used the particle swarm optimization (PSO), the gravitational search algorithm (GSA), and the mind evolutionary algorithm (MEA), respectively, to optimize the nine most common empirical Rs models, comprising three sunshine-based models (Angstrom, Ögelman, Bahel), three temperature-based models (Hargreaves, Bristow-Campbell, Hunt), and three combined-based models (Fan, Chen, El-Sebaii), and then integrated them into the PM for ET0 estimation at four climatic zones of China. The results showed that the Fan model obtained the most accurate Rs estimates in china, while the sunshine-based and temperature-based exerted significantly different applicability at different climatic zones. Regarding optimization algorithm, this study found that GSA performed better for the Ögelman model, Fan model, and Chen model when integrating into the PM equation for ET0 estimation, whereas MEA performed better for the Angstrom model, Hunt model, and El-Sebaii model. After optimization, PMGSA–Fan obtained the most accurate estimates of ET0 at four climatic zones, with a regional spatial gradient in estimates accuracy of Rs from north to south of China. In terms of sunshine-based models, the PMMEA–Angstrom performed better in TMZ, TCZ, and MPZ, whereas the PMGSA–Ögelman performed better in SMZ, respectively; in terms of temperature-based models, the PMMEA–Hunt performed better in TMZ and SMZ, whereas the PMLSM–Hargreaves performed better in TCZ and MPZ, respectively. Overall, this study identifies the optimal Rs estimation model and optimization algorithm for ET0 estimation at four climatic zones, which provide regionally and nationally accurate water consumption information without actual measured Rs in China.
Natural Language Processing using Graph Neural Network for Text Classification

The boom of the technological area has given rise to numerous new applications that actually rule the entire world. Some of them mainly are the social media networks like the Twitter, Gmail and Facebook. Lot of text and content are received to millions of people across the work. It has become very essential to be surer whether the content is fake, or isit malicious or is it a really important content. To classify these contents, text classification techniques are being widely used across the globe. This has invented a new technical world of Natural Language Processing where it becomes very important to process the texts and images before we actually use them. In this paper, we have introduced the use of Graph Neural Networks (GNN) to classify the text according to their content. The use of GNN is done as they work well for 2D vectors and texts are in two dimensions. Computation of Self-Organizing Maps (SOM) are done to compute the nearest neighbors in the graphs and it calculates the actual distances between the neighbors. This process is used in classifying the text and performs better when compared to the existing techniques.
Design of a Low-Power Super-Resolution Architecture for Virtual Reality Wearable Devices

Head-mounted displays (HMDs) have made a virtual reality (VR) accessible to a widespread consumer market, introducing a revolution in many applications. Among the limitations of current HMD technology, the need for generating high-resolution images and streaming them at adequate frame rates is one of the most critical. Super-resolution (SR) convolutional neural networks (CNNs) can be exploited to alleviate timing and bandwidth bottlenecks of video streaming by reconstructing high-resolution images locally (i.e., near the display). However, such techniques involve a significant amount of computations that makes their deployment within area-/power-constrained wearable devices often unfeasible. This research work originated from the consideration that the human eye can capture details with high acuity only within a certain region, called the fovea. Therefore, we designed a custom hardware architecture able to reconstruct high-resolution images by treating foveal region (FR) and peripheral region (PR) through accurate and inaccurate operations, respectively. Hardware experiments demonstrate the effectiveness of our proposal: a customized fast SR CNN (FSRCNN) accelerator realized as described here and implemented on a 28-nm process technology is able to process up to 214 ultrahigh definition frames/s, while consuming just 0.51 pJ/pixel without compromising the perceptual visual quality, thus achieving a 55% energy reduction and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 14$ </tex-math></inline-formula> times higher throughput rate, with respect to state-of-the-art competitors.
Reversible Data Hiding Method for Interpolated Images Based on Modulo Operation and Prediction-Error Expansion

Image reversible data hiding (RDH) using interpolation technology (IT) provides high single-layer embedding capacity and has become a research hotspot. To solve serious image distortion in existing methods caused by secondary embedding based on interpolated pixels, we propose an RDH method for interpolated images based on modulo operation and prediction-error expansion (PEE). Our method differs from existing RDH methods that only consider embedding data into interpolated pixels. First, an image interpolation method is used to generate an interpolated image, which is partitioned into overlapping 3 × 3 blocks. For each block, secret data are embedded into interpolated pixels using the modulo operation and into reference pixel using the PEE. Standard grayscale images of size 512 × 512 were selected from the popular BossBase and BOWS-2 image databases as input images for experiments. Extensive experimental results show that the proposed method avoids pixel values underflow/overflow caused by embedding data and reduces image distortion effectively. For each standard test image, the maximum bits per pixel (BPP) is higher than 1.5, and the corresponding peak signal to noise ratio (PSNR) is higher than 45.0 dB. At the given embedding capacity, the proposed method outperforms recent similar methods regarding the watermarked image quality. Moreover, the proposed method can resist histogram analysis, and the subjective evaluation proves that the proposed method has good imperceptibility.
Analyzing the progress of FINTECH-companies and their integration with new technologies for innovation and entrepreneurship

The purpose of this research is to present an overview of the progress of Financial Technology (FINTECH) companies and their integration with the latest cutting-edge technologies such as artificial intelligence, machine learning, and blockchain for innovation and entrepreneurship. We used the well-respected Scopus database to do our study. To learn about contemporary problems and trends about FINTECH-companies, publications published in English between 2014 and 2022 were searched using Scopus databases. Finally, we looked at 302 pieces of research as a whole. Qualitative data analysis techniques are used to identify the most prolific and relevant journals, countries, and authors. Study trends are also highlighted, allowing several new research avenues to be explored. Researchers have contributed significantly to FINTECH-companies by providing a thorough overview of the field's development and current condition.
A novel outlier detection approach based on formal concept analysis

Outlier detection is a major research field for data mining. In recent years, rough set and granular computing have been successfully applied to outlier detection, and a series of excellent results have been achieved. However, as another important area of granular computing, the study of formal concept analysis applied to outlier detection has not been reported. We introduce formal concept analysis to deal with the outlier detection problem and propose a granular concept-based outlier detection model in this paper. Firstly, we define a distance measure between objects using the object belonging or not belonging to the extent of a granular concept. Secondly, a distance measure between granular concepts and an outlier degree of each granular concept are defined. And then an outlier degree of each object is constructed from the outlier degree of the granular concept by weighted average. Finally, a granular concept-based outlier detection model is given and the related algorithm is designed. Experiments are performed on fifteen public datasets, and then our algorithm is compared with classical and rough set-based outlier detection algorithms. The results of the experiments show that the proposed algorithm can effectively and efficiently detect outliers.
Rechain: A Secured Blockchain-Based Digital Medical Health Record Management System

The typical method of keeping track of medical records uses paper and centralised systems, which introduces a great deal of uncertainty and can lead to inaccurate information, identity theft, a lack of accessibility, and other problems. With the use of blockchain technology, which offers us security, availability, and integrity, this study seeks to address these problems. Using this, the records can be safeguarded and accessible from anywhere, at any time. This blockchain-based solution can protect a patient's life in an emergency since all of the records can be viewed in one location without the inconvenience of running various tests. Re-chain a secure blockchain-based digital medical health record management system was developed to solve the problems with the centralized and traditional system. It is based on the Ethereum network and uses web3.storage which is an Interplanetary File System (IPFS) system to store files in a decentralized manner.
Preparing valence-bond-solid states on noisy intermediate-scale quantum computers

Quantum state preparation is a key step in all digital quantum simulation algorithms. Here we propose methods to initialize on a gate-based quantum computer a general class of quantum spin wave functions, the so-called Valence-Bond-Solid (VBS) states, that are important for two reasons. First, VBS states are the exact ground states of a class of interacting quantum spin models introduced by Affleck, Kennedy, Lieb and Tasaki (AKLT). Second, the two-dimensional VBS states are universal resource states for measurement-based quantum computing. We find that schemes to prepare VBS states based on their tensor-network representations yield quantum circuits that are too deep to be within reach of noisy intermediate-scale quantum (NISQ) computers. We then apply the general non-deterministic method herein proposed to the preparation of the spin-1 and spin-3/2 VBS states, the ground states of the AKLT models defined in one dimension and in the honeycomb lattice, respectively. Shallow quantum circuits of depth independent of the lattice size are explicitly derived for both cases, making use of optimization schemes that outperform standard basis gate decomposition methods. Given the probabilistic nature of the proposed routine, two strategies that achieve a quadratic reduction of the repetition overhead for any VBS state defined on a bipartite lattice are devised. Our approach should permit to use NISQ processors to explore the AKLT model and variants thereof, outperforming conventional numerical methods in the near future.