Reviewerly/exHarmony huggingface dataset:
An Integrated Nature-Inspired Algorithm Hybridized Adaptive Broad Learning System for Disease Classification

Classification of different deadly diseases using machine learning algorithms helps in making the health care system more robust.This, not only reduces the human errors during diagnosis of disease due to inexperience but also helps the physician for taking an emergency action earlier to biopsy.As the genomic data undergoes through the malediction of excessive dimension problem, both the selection of the remarkable genes and classification of these data efficiently still remain as a demanding research problem.To obtain the notable features from high dimensional data, by using a nature-inspired algorithm, is a Non-deterministic Polynomial-time (NP)-Hard problem.Therefore, a researcher can apply new algorithm to solve this issue.In this suggested approach, an integrated natured-inspired algorithm i.e., Sine-Cosine (SC) based Monarch Butterfly Optimization (SC-MBO) is merged with an Adaptive Broad Learning System (ABLS) called SC-MBO-ABLS, to find out the most notable genetic features and classify the genomic data at the same time.In the preliminary stage, a feature extraction method i.e.Kernel based Fisher Score (K-FS) is applied to extract a notable key gene subset.Then, this extracted key gene subset goes through further execution with the SC-MBO-ABLS method.To examine the effectiveness of the presented method, ten genomic datasets are considered.Here, several performance evaluators (i.e., Precision, Matthews Correlation Coefficient, Sensitivity, Kappa, Specificity, and F-score) are used for a neutral estimation of the presented approach.This presented model is compared with SC-MBO wrapped Multilayer Perceptron (SC-MBO-MLP), SC-MBO wrapped Extreme Learning Machine (SC-MBO-ELM), and SC-MBO wrapped Kernel Extreme Learning Machine (SC-MBO-KELM).Further, sixteen existing standard models are compared to prove the supremacy of the presented method.Moreover, an analysis of Variance i.e., ANOVA test is carried out for statistical evaluation of the proposed work.Eventually, according to the above quantitative and qualitative measure, it is summarized that the suggested SC-MBO-ABLS method surpasses other considering standard models.
Machine Learning Based Software Effort Estimation of Suggestive Agile and Scrumban Methodologies

The dynamic IT business and industries are welcoming the customer software requirements to adopt with new changes as per needs. With the recent technological advancements in this era, the software industries have grown exponentially. The software industry has seen such a rapid advancement in technology that it is incomparable to any other industry. Agile is one of the methods that has been developed to improve software quality. Agile software development has gained popularity due to its simplicity and ease of use. Agile software development is a method for producing high-quality software with excellent team interaction and increased customer involvement. The agile method is best suited for situations where requirements change on a regular basis. One of the most significant benefits of using Agile is that software releases take less time, are easier to understand, and require less documentation. This study compares various agile methods, their benefits and drawbacks, and suggests SCRUMBAN, a new proposed framework to address those drawbacks.
Arabic Machine Translation Based on the Combination of Word Embedding Techniques

Automatic Machine Translation is a computer application that automatically translates one source-language sentence into the corresponding target-language sentence. With the increased volume of user-generated content on the web, textual information becomes freely available and with a gigantic quantity. Hence, it is becoming increasingly common to adopt automated analysis tools from Machine Learning (ML) to represent such kind of information. In this paper, we propose a new method called Enhanced Word Vectors (EWVs) generated using Word2vec and FastText models. These EWVs are then used for training and testing a new Deep Learning (DL) architecture based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Moreover, special preprocessing of the Arabic sentences is carried out. The performance of the proposed scheme is validated and compared with Word2vec and FastText using UN dataset. From the experimental results, we find that in most of the cases, our proposed approach achieves the best results, compared to Word2vec and FastText models alone.
High-Fidelity Light Field Reconstruction Method Using View-Selective Angular Feature Extraction

Deep learning (DL) provides an effective approach for light field (LF) reconstruction that aims to synthesize novel views from sparse-sampled views. However, it is challenging to address domain asymmetry when adopting spatial-angular interaction LF reconstruction methods. To solve this problem, a view-selective angular feature extraction block (VS-LFAFE) is proposed to obtain full-resolution angular features that enumerate whole view directions in a macropixel. By applying the VS-LFAFE, a novel LF reconstruction method is proposed with two subblocks: a spatial-angular feature extraction and fusion block and an angular upsampling block. Experimental results validate that the VS-LFAFE is effective, and the proposed method can achieve superior performance compared with the state-of-the-art methods.
Framework for Object Recognition and Detection for Blind Users Using Deep Learning

People who are visually impaired struggle with many aspects of daily life. For people who are blind, being able to detect and recognize familiar objects in the environment seems like a terrific idea. The main obstacle to object recognition in this difficult development requires computer vision that interacts with internal and external classes. This phenomenon calls for additional attention over time. In this study, we present a novel framework to support object detection and recognition for the blind and visually impaired, enabling them to move independently and be aware of their environment. Utilizing a distinctive discriminating mechanism, things are recognized utilizing previous implementation techniques. The acquisition of machine learning and vision technologies, creative technology, forward-thinking, and the appropriate response based on its accuracy and efficacy can be used to characterize a multi-label technique in this situation. In the proposed study, we use type/grouping strategies to address issues with current tools. These techniques can speed up the detection of many items while maintaining best-in-class time complexity. Additionally, the model that aids blind people can understand independently the items and money around, and they can hear about them through auditory feedback. The output of the framework can also be provided to the blind individual in audio format.
Spider Monkey Based K-Means Dynamic Collaborative Filtering for Movie Recommendation Systems

Building Automation Recommender Systems (BARSs) can keep constructing proprietors’ cash by means of lowering power intake whilst concurrently retaining occupant comfort. There are algorithms that optimize this exchange off, including detecting which home equipment have become the one without requiring costly popularity detectors to be connected to every appliance. However, efficient methods have to be identified which are best suited for specific constructing. Recommender structures were used extraordinarily, academically and commercially. The guidelines are generated by using methods which are capable of providing applicable thrilling gadgets to customers. Several techniques were proposed for offering customers with guidelines for the use of their score records. Most of those techniques are afflicted by new person problem (cold-begin) which is the preliminary loss of gadgets reviews. In this paper, demographic statistics are used to offer guidelines rather than the use of score records to keep away from cold-begin problem. Spider Monkey based K-Means Dynamic Collaborative Filtering is proposed for movie recommendations and the results are satisfactory.
Timing-Aware Qubit Mapping and Gate Scheduling Adapted to Neutral Atom Quantum Computing

As a less developed but potential quantum technology, neutral atoms (NAs) can provide advantages, including higher qubit connectivity, longer-range interactions, and much more native multicontrol gates than superconductivity. Long-range interactions, however, prevent parallelism of interacting qubit pairs with their surrounding restriction zones. Therefore, the quantum program cannot be run directly on an NA quantum computer (NAQC) unless compiled. The recent compiling study of NAQC applies simple layer-by-layer scheduling and does not consider the difference in gate duration. To address the above issues, we focus on the qubit mapping and quantum gate scheduling (M&S) problem of quantum circuits to meet hardware constraints of superconductivity and even NAs. Our goal is to shorten the execution time to mitigate decoherence noise. We propose a block-game-like abstraction mechanism TETRIS which is an abstract model of the M&S problem and aware of the duration difference of gates and several other NA characteristics. Based on the abstraction, we propose a heuristic greedy algorithm (HGA) to solve the M&S problem efficiently. To further speed up the execution of the circuit, we embed HGA into a Monte Carlo tree search (MCTS) framework to solve the M&S problem, which consumes more compiling time but achieves a better result. Comparing our MCTS algorithm with the only recent M&S algorithm for NAQC, the average speedup ratio is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.75\times $ </tex-math></inline-formula> for several quantum circuits collected from RevLib, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.18\times $ </tex-math></inline-formula> for circuits from Qiskit Lib.
Research on Whole Process Quality Control Technology of UHV Main Equipment Based on GIM and Internet of Things

In view of the problems of asymmetric multi-professional management of main equipment information in UHV project, untimely updating of equipment information, uncontrollable state of transportation process, and lack of unified platform for management of main equipment at all stages, Grid Information Model (GIM), Internet of Things technology and big data technology are introduced into the main equipment management of UHV project. The technical scheme of quality management and control of UHV main equipment in the whole process of installation is designed, and the control points and implementation methods of the key links in the whole process of planning, production, transportation, installation and commissioning, and testing are described in detail. The analysis results show that the scheme can meet the management and control needs of UHV project main equipment and reduce the cost of UHV main equipment management. It provides the ability of enterprise operation.
A Secure Lossless Redundancy Elimination Scheme With Semantic Awareness for Cloud-Assisted Health Systems

Due to the limited scalability and reliability of traditional storage systems, more and more medical data are stored in cloud. However, the cost of cloud-assisted health systems is skyrocketing. How to improve storage utilization and save resource cost for health systems has become urgent problems. Therefore, this study proposes a secure lossless redundancy elimination scheme with semantic awareness, called SLRE, to decrease storage overhead and improve the overall system performance. Compared with existing studies, when the locality of data is almost nonexistent or poor, SLRE can eliminate redundant data well by exploiting semantic properties of similarity and entropy rather than relying on the locality. Then, fine-grained post-deduplication delta compression based on proxy similarity discovers more similar blocks and improves storage efficiency. In addition, filtered Lempel–Ziv compression based on proxy entropy filters data with low redundancy to improve the overall performance. Importantly, in order to ensure the security of medical data after deredundancy, different auditing strategies for medical data with diverse characteristics are proposed to guarantee security and improve the auditing efficiency. The experimental results show that compared with the state-of-the-art methods, the overall compression ratio of SLRE is improved, and the system throughput and auditing efficiency are also improved.
HiCT: Hierarchical Comprehend of Transformer for Weakly Supervised Object Localization

The weakly supervised object localization (WSOL) has always been a very challenging research subject in the field of computer vision, which aims to predict the localization of objects in an image using only an image-level class labeling approach. The traditional convolutional neural network (CNN) based-approaches utilize the local class activation and discrimination for classification guidance, and the biggest drawback of CNN is that it cannot capture the remote feature dependencies between pixels. Recently, the transformer architecture has been deployed in the WSOL, but the transformer cannot well capture local features. To address the above problems, we propose HiCT (Hierarchical comprehend of transformer), a simple and effective visual converter variation method. Moreover, we also propose a discriminative-based attention layer (DAL), which aims to mine the local feature information by utilizing the global token attention graph mechanism. To further improve the coverage of object localization, we introduce the spatial aware digging module (SADM). In addition, a set of complementarity loss calculators to patch hierarchy (CPH) is proposed to improve the sample class aggregation capability of our model. Finally, we conducted experiments on two commonly used datasets of CUB-200-2011 and ILSVRC, so as to verify the effectiveness of our method.
A Blockchain Based Decentralized Certificate Management System Using Hyperledger Fabric

The development of blockchain has influenced various fields, including Banking, healthcare, fiśnancial and supply chain systems. At present, this technology is applied in the education sector and its unique features like decentralization, trustworthiness and security have added revolutionary characteristics to existing systems. An individual deals with a variety of certificates in daily routine life which includes voter ID, land documents, marriage documents, vehicle documents and health documents. These documents showcase an individual’s capabilities, achievements, strengths in terms of educational, extracurricular and professional certificates. Management of digital copy of these certificates became the main motive and the hardcopy perseverance became obsolete. The digital certificates help us to preserve and showcase an individual’s capabilities for career growth. These certificates can be forged and misused for self motives. Need to secure these certificates against frauds. A variety of systems exist to manage these certificates and to thwart forgery. Traditional method of certificate storage involves usage of a centralized database or a web server for certificate storage which is vulnerable to theft and forgery. Best alternative is a Blockchain based system. Different Blockchain based certificate management systems and their drawbacks are discussed and will propose a hybrid model of educational certificate management model using Hyperledger Fabric, IoT and 2D Barcode.
Deep Learning Applied to ERP in the Search for Spatial Intelligence Signatures

Neuroscience every day seeks to better describe human behavior by more assertive solutions to problems such as classification in EEG data and for that techniques, such as deep learning, have been widely used, making it feasible to implement models for the automatic processing of biosignals. In this sense, the present work proposes to apply deep learning to handle time series of visual evoked potentials, with the objective to observe the presence of patterns in ERP signatures to associate them with the degree of spatial intelligence of a subject, from the region frontal and parietal of the cerebral cortex, as an alternative to traditional task-based IQ tests, given the problem of lack of automatic intelligence signature pattern recognition systems, as a behavioral and electrophysiological biomarker associated with visual-spatial ability. Using the technique of supervised deep learning, through artificial neural networks (deep feedforward) MLP type for feature recognition in intellectually gifted subjects compared to control subjects collected while performing a classic three-dimensional mental rotation task. Contributing directly to the area of neuroscience, by confirming the hypothesis that in a series of visual ERPs there is a signature pattern of a subject's spatial intelligence and, therefore, its automatic classification is possible, and filling gaps in the literature regarding automatic extraction of intelligence biomarkers.
Calculation of Characteristic Parameters of Short-Circuit Current of Power Transformer Based on MPM

The short-circuit current waveform is a typical waveform widely used in short-circuit impedance test of power transformer. The three-peak method and the exponential curve method are recommended in IEC 62475 to analyze characteristic parameters of short-circuit current. However, these two methods may result in significant errors when there is more noise. To solve this problem, the matrix pencil method (MPM) is adopted which uses discrete signal to solve the defined state space signal model and calculate dynamically characteristic parameters of short-circuit current, including the initial phase angle, amplitude, and frequency. The results show that the errors of three-peak method, exponential curve method and MPM are 11.40 %, 11.38 % and 0.40 % respectively when the short-circuit current waveform is contained with 20dB Gaussian white noise. MPM effectively suppresses the interference and the calculation accuracy is significantly higher than methods recommended by IEC.
Containerized Power Data Center with Multi-station Integration

In view of the rising cost, low equipment utilization and operation efficiency faced by most traditional power systems at present, and the growing demand for power data centers, a multi-station integrated containerized power data center construction scheme is proposed, which integrates substations, energy storage stations, charging stations, data centers and distributed energy stations to achieve energy complementarity. First, the importance and advantages of multi-station integration are analyzed, and the architecture of data center energy supply based on multi-station integration is proposed. Then it introduces that the containerized data center has better performance than the traditional data center. Finally, the specific implementation and construction effect of this scheme are introduced in detail, which shows that it has good center characteristics, and proves that the multi-station integrated containerized power data center has excellent centralized advantages, and will lead the future development trend of power data center.
A Survey on Ensemble Multi-label Classifiers

Ensemble approach for multi-label classification has received great attention from the Machine Learning community over the last decade. It has been developed on top of the problem transformation or algorithm adaptation methods to overcome some scientific challenges introduced by multi-label learning. The success of ensemble models in this classification paradigm arises by offering an appealing solution to different problems, such as label dependencies, class imbalance, and high dimensionality. In this study, we present a structured synthesis of different ensemble multi-label classifiers including their different taxonomies cited in the literature.
A Machine Learning Approach to Image Inpainting

Although image inpainting, or the art of restoring old and degraded photographs/images, has been around for a long time, it has lately acquired popularity as a consequence of technical advancements in image processing technologies. Because of the developments in image processing tools and the versatility of picture editing, automatic image inpainting has become a significant and demanding subject in image processing analysis, with important applications in computer vision. The goal of this article is to develop a hybrid inpainting model that could outperform existing methods.
Content Based Fashion Image Retrieval Based on Recent Advances Deep Learning Approaches

Fashion Visual Search is to find other similar images in the product catalog for a consumer’s uploaded images based on visual appearance. In this report we propose using recent advanced deep learning methods and compare them with a ranking evaluation metric. The proposed feature extractors are training and evaluating in two different datasets: the first contains clothing images from both customer and shop and the other used pure clothing items with simple white background.
Current Secondary Circuit Anomaly Detection Based on ROD Algorithm

During the operation of the power grid system, abnormal current often occurs due to equipment failure or improper operation by technicians. Timely detection of abnormal current sections plays a significant role in maintaining the safe operation of the power grid. In view of the low accuracy of current abnormal section detection methods, a robust Rotation based Outlier Detection algorithm that is parameter-free, requires no distribution assumptions is used to detect abnormal current segments. The preprocessed current data are used to train the model. The experimental results show that the method can detect abnormal current segments well.
A Two-Way Integrated Communication System for the Deaf and Mute

A non-disabled person finds it onerous to learn sign language to communicate with the deaf-mutes. This paper discusses a two-way communication system that bridges this communication gap. The proposed system is a portable standalone embedded system that acts as a real-time sign language translator between the deaf-mutes and non-mutes. A fingerspelling data set of Indian sign language that includes some commonly used words were created for training and validating the model. The system uses computer vision techniques and customized speech translator APIs, enabling two-way communication between the deaf-mutes and non-mutes.
Ultra Short Term Distributed Photovoltaic Power Prediction Based on Satellite Cloud Images Considering Spatiotemporal Correlation

With the advancement of China's carbon peaking and carbon neutrality goals and the development of photovoltaic power generation technology, a large scale of distributed photovoltaics are connected to the rural distribution network in recent years. Photovoltaic power generation features high randomness and uncertainty, Accurate prediction of distributed PV power on ultra-short-term time scale (0-4h) is of great significance to the safe and stable operation of distribution network. This paper proposes a prediction algorithm based on satellite cloud images considering spatiotemporal correlation between solar stations nearby. Firstly, correlation between adjacent power plants are sorted, corresponding prediction models based on LSTM are built using historical power and NWP data, then satellite images are used to choose suitable prediction models for prediction when forecasting. With the actual dataset of photovoltaic power station in northeast China, The proposed algorithm is verified, the test results show that the proposed algorithm proposed in this paper is generally at a better accuracy level compared with other well-established benchmarks in terms of power curve and statistical error.